FactSet's Taking Risk blog

Empirical durations: A tool to gauge mortgage price sensitivity

Mido Shammaa — 2011-08-31T18:02:59Z

Today's blog is contributed by Mido Shammaa, Product Manager for FactSet's fixed income products. He works on prepayment modeling for FactSet's models and leads the integration of third-party vendor prepayment models.

We’re often asked to explain the results generated by our prepayment models. The question we are asked most often is about effective duration. Yet there isn’t a simple way to answer these concerns because effective durations are driven by two complex pieces of the fixed income calculation engine: the prepayment model and the interest rate process. Both are viewed as black boxes by clients and due to the large number of paths and cashflows, they are very hard to verify.

Empirical duration is a method used to approximate a security’s sensitivity to interest rate changes. Empirical duration uses market prices, so it can be an impartial benchmark that can be useful to compare with effective durations generated using a prepayment model such as FSP.

To calculate empirical duration we ran a regression of daily MBS price changes against yield changes (usually the 10-Year CMT). There are many ways to do that, but we chose the most basic one because its results were the most intuitive.*

We did consider both the method proposed by DeRosa, Goodman, and Zazzarino in 1993 and the one proposed by Steve Manson in 2002, but neither provides realistic results in the current market environment.**

We used daily prices provided by Bank of America/Merrill for generic mortgages from recent vintages (2008 through 2011) with a variety of collateral types. In the following charts we plot empirical durations against effective and coupon curve durations calculated using the FactSet Prepayment Model with the UST as a discount curve as of 7/15/2011.

As we can see the calculated durations track the empirical ones very closely.

The trend invariably brings up the question as to why not use empirical durations for hedging, reporting, or portfolio management. There are many reasons as to why using empirical duration is not practical.

Unlike effective duration, empirical duration is not standardized. Practitioners disagree about what time frame should be observed when conducting the analysis, the appropriate frequency to use, or even what rate to do the regression against. In this case, I used all available prices for the instruments that I covered, but if an instrument has a longer trading history then the time frame becomes an issue.
With effective or coupon curve duration the price sensitivity that is measured is clearly defined as to what rates are being shocked (for effective, it’s the spot rate, while for coupon curve, it is the curve) and by what amount. In the empirical duration calculation, however, it is not clear what is driving the mortgage price changes relative to the 10-Year CMT as mortgage prices depend on a number of factors in addition to the rate level (the spread between the mortgage market and the treasury market, correlations between rates and markets, and market volatility). This inability to isolate the effects detracts from the usefulness of empirical durations in risk reporting and hedging.
Most importantly, while calculating empirical durations is easy to do for actively traded mortgages such as generics, TBAs, and recent vintage agencies; you can get incorrect results if you perform a similar analysis for illiquid or non-conforming securities that do not trade. For newly issued securities, or those without a price history, it would be impossible to perform the regression.

Empirical durations are therefore best used as a sanity check on the results of the prepayment model being used.

* The formula is:

Where and the empirical duration of the security. This method is used by Lakhbir S. Hayre in his 2001 report “Mortgage Durations and Price Moves”

** DeRosa, Goodman, and Zazzarino “Duration Estimates on Mortgage Backed Securities” and Manson in “An Empirical Duration Measure for Mortgage Backed Securities”

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A retrospective on U.S. debts, and the logic of ceilings (Part 2)

Steve Greiner — 2011-08-22T18:01:10Z

In my last post, I discussed how we should understand the way that our ratio of debt to GDP impacts our economy.

In this segment, let’s look at the increase of our national debt through time

I'd like to earmark which president was in office for the particular increase. The following chart illustrates this perfectly but unfortunately doesn’t take us into 2011. The full impact of debt borrowing during 2011 by President Obama is not accounted for in this chart. (Click to enlarge.)

First, we note the strong rise in debt under President George W. Bush. Clearly, the wars of Iraq and Afghanistan meant we had to raise more funds to fight these wars, among other things. It appears that both Bush presidencies have a steep slope in rising debt. However, I want to call attention to a subtlety missed by most of us. When “W.” took office the national debt was $5.768 trillion and when he left office eight years later it was $10.626 trillion amounting to $607 billion per year of debt increase. However, the debt when Obama took office at $10.626 now stands at $14.071 trillion after just two years. This is a whopping $1.723 trillion per year for Obama.

We can argue all day long about whether he had to do it, was left with a lousy economy by W. or not, but Obama's administration did oversee the largest debt increase in the history of the U.S., amounting to $3.445 trillion in just two years. Another way of looking at this involves observing the debt to GDP through time as opposed to just debt growth. The next plot illustrates this nicely. You can click the image to get a larger view.

Here we show the debt levels as the red bars (axis on the left) and the debt to GDP as the blue line (axis on the right). Now, this chart isn’t up-to-date, it was produced in 2006. I’ve cut the chart off around 2007, so you can see only the accurate data in regards to GDP and debt (which as predicted with 2006 data was much lower than what we actually saw from 2006-present). In 2007, the debt to GDP was only 65% to 66%. Thus one can clearly see that raising the debt ceiling at that time or before that time wasn’t such a significant request from congress—the debt was manageable since it was a smaller percentage of GDP. We as a nation raised enough through taxes to service the debt and still run the government.

Now however, given the data I quoted in the first post, from 2007 to 2011 our debt to GDP has risen to 100% and we’re in danger of not being able to afford the debt payments which makes the request of congress to raise the debt ceiling more controversial. In addition, the economy is stalling, unemployment remains persistently high, and our future is “mortgaged” due to all this debt. Hence the reason many in congress see this persistent trend, started by the second President Bush but exacerbated by Obama as making the U.S. face up to this day of reckoning. The debt to GDP has reached a level which is unsustainable.

The last chart pinpoints where the U.S. is on a world map divided into four quadrants: those with rising debt but a declining deficit, those whose debt and deficit are both in decline, those with with a rising deficit and declining debt, and finally those with both a rising debt and deficit. Countries with rising debt and rising single-year deficit spending are shown in the upper right. This includes the U.S. and Japan. It also represents the worst situation to be in. Countries like Greece, Spain, Portugal and France fare better because while they have huge debt, they are shrinking their spending. Healthy countries like Sweden, Korea and Switzerland are shown below the horizontal like and have balanced budgets and little debt.

In conclusion, the issue about the debt ceiling has everything to do with basic fundamental economics. We in the U.S. cannot afford “everything”. Even we need to curtail spending and stop growing our debt in perpetuity. The solution will involve devaluing our currency, making our debt smaller relative to other currencies, and curtailing spending by cutting welfare and entitlement programs like Medicare, government pension and social security while raising some taxes. There is no other way. Unfortunately this means unemployment will remain stubborn highly for some time and economic growth will be muted also for just as long. Meanwhile, if more regulation and more growth in the size of government occurs then we will have no choice but to take the “Grecian” formula for ourselves.

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A retrospective on U.S. debts, and the logic of ceilings (Part 1)

Steve Greiner — 2011-08-19T18:02:18Z

The debt ceiling has been raised, and a new wave of financial tumult has surfaced regardless of the fact that the U.S. raised its debt ceiling. Still, it is useful to spend some time talking about our national debt, which is still so much a topic on everyone’s lips as a result of the U.S. getting a downgraded S&P rating.

There’s a lot of misrepresentation about the national debt in the media in terms of its size and significance. In addition, many people confuse the budget deficit with the national debt. The budget deficit in lay-terms represents a single year’s difference in income (from taxes) versus government spending, while the national debt represents the amount of accumulated debt from budget deficits over many years. Also, I want to challenge the conception that raising the debt ceiling, simply because it’s been done before in other administrations, was a “good thing” or “no brainer."

I believe what’s missing in these arguments is a historical perspective about the U.S. national debt. It’s not so much a fault of the President of the U.S. per-se (i.e., Bush vs. Obama) as it is about a general failure of our congress, senate, and the sitting president to understand the economic underpinnings of what has been occurring over many years, over many sitting presidents. Moreover, in the past raising the debt ceiling was easy because the debt to GDP ratio was small.

Let's illustrate the concept with an example: Consider if you have income of $50,000 per year and credit card debt of $5000 on a card with $10000. In this situation, you’d have a debt to income ratio of 10% ($5000/$50,000, if your credit card represents your total debts). If you then spend $5000 more on vacation, now your total debt is $10,000. You’ve reached your credit limit and have 20% debt to income ratio. If your job is secure and you’ve had it for many years you can call your bank and ask them to raise your credit limit to $15,000 and they probably would. This can go on until your debt to income level approaches some limit.

The argument about your debt ceiling, your credit card limit, in this example, will get more and more heated until finally the bank says, “no more credit” and stops raising your limit. Now, what is the acceptable credit limit you might ask in percentage of income terms? It is certainly not 100%. If you have $50,000 of credit card debt with 10% interest rate and only a $50,000 income, the credit card debt service will begin to eat away your take-home pay each month.

It’s logical to have a credit limit to protect you from yourself, to protect you from paying too much money in interest on debt, so that you will not have to declare bankruptcy, so you will not have to default on your debt. However, if your income is growing at 10% a year, then next year your income will be $55,000. Your debt to income ratio will fall simply because your income went up, not because your debt decreased.

It’s logical to have a credit limit to protect you from yourself, to protect you from paying too much money in interest on debt, so that you will not have to declare bankruptcy, so you will not have to default on your debt.

For a nation, there are two ways to mimic this trend— the obvious way is to increase your GDP (i.e., grow your economy) and the non-obvious way is to devalue your currency. This analogy makes it all easy to understand. Now, consider: What is the appropriate amount of debt to income, or debt to GDP for a country to not go bust or default?

To put this in a global perspective, the following chart offers a list of countries that most of us recognize their debt as a percentage of GDP as collected by the International Monetary Fund (IMF) and the date of the data. Now, you hear in the media these days about Europe’s woes from the PIIGS countries (Portugal, Ireland, Italy, Greece and Spain, which I rename the GIIPS, as I find PIIGS insulting). I highlight in bright yellow these countries. I also highlight Japan and the U.S. in beige so you can compare the debt to GDP of Japan and the U.S. with the GIIPS countries that are travailing Europe these days.

When you hear people say, “The U.S. is just like Greece”, you can see why they say that. Their debt to GDP is 130% while the U.S. is approaching 100% (it’ll surpass 100% this year). Italy, Ireland, Iceland are all just a little bit ahead of the U.S. and Portugal is just behind us.

Spain is way below the U.S. and France is way up there, as is Belgium along with some smaller countries most of us don’t care too much about. However so is Singapore. Singapore is an example though where their GDP is growing so fast that they’ll stay ahead of their debt, analogous to the 10% income growth of the individual I spoke about. But the other countries all have low single digit GDP growth and some negative growth.

That is where the debt to GDP ratio becomes important. When the debt to GDP ratio becomes large like the U.S., which grew from 40% in 1980 to ~100%, the credit rating agencies begin to lose confidence that the country can make debt payments regularly. In turn, buyers of our debt begin to demand higher interest rates to purchase new debt just like the credit card agencies will raise the interest rate on your credit card. Thus, if the 3% interest rates the U.S. pays on its debt to creditors rises to 5% or 6%, the amount of money paid each year to creditors doubles which leaves less for the government to operate, less for Medicare, less for road construction, and so on. This is why a ratings downgrade, as we saw S&P do, is so important. It keeps the cost of paying debt managable. So we see that Japan has really high debt to GDP and also we know that their economy has stalled.

So the impact of high debt is also that it slows the economy and wreaks havoc with growth of employment, growth of business and lowers the general earnings of everybody. Japan can sustain higher levels of debt simply because most of their owners are the Japanese themselves, while the U.S. has a much higher incidence of foreign buyers of our debt. When Japan pays interest on its debt to its debt holders, it mostly goes to the Japanese people, while when the U.S. pays interest on its debt, it mostly goes overseas, to the Chinese, Indians and resource-rich countries found in the middle east. These are the major buyers of our debt, hence the media talks about the Chinese loaning us money. Indeed they are.

Next week, I’ll continue with a look at our national debt over time.

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ETFs: The next bubble? (Part 2)

Andrew Kovacs — 2011-08-03T13:07:43Z

Last week, on the blog, I talked about the differences I've seen happen in the ETF market in just a little over 10 years. To read my opening post, click here.

Like many, I believe ETFs are a great tool for investors, particularly as an easy way to manage exposures. Nowadays you can use ETFs to pretty much gain or limit any exposure under the sun. Say you want to participate in the growth of Chinese Infrastructure, no problem,Emerging Global Shares China Infrastructure ETF is one of several options available to you.

What’s that? You want to hedge your U.S. large cap risk exposure to momentum? The Russell 2000 High Momentum ETF may be just what you are after. What if you believe gold is where to invest? Then perhaps you should take a look at UBS E-Tracs CMCI Gold Total Return ETN. It really seems like there is something for every situation.

So what’s the problem? My concerns regarding some ETFs and ETNs stems from the old adage “invest in what you know,” to which I strictly adhere.

For many (probably even most) of these funds, the structure is pretty straightforward and they actually own what it is they are trying to track, but for a growing number of funds this in not necessarily the case. Rather than owning some, or all, of the assets directly, ETFs may employ derivatives like total return swaps to replicate or “enhance” a fund. This method of managing ETFs will usually result in even lower fees and in theory should allow the ETF to more closely track the target index or benchmark. These types of funds are often referred to as Synthetic ETFs because they are constructed using derivatives as opposed to the actual physical assets they are supposed to emulate.

"For many (probably even most) of these funds the structure is pretty straightforward and they actually own what it is they are trying to track, but for a growing number of funds this in not necessarily the case. "

Let’s consider one such ETF, the db x-trackers S&P 500 ETF. If you have a little time to spare, then may I suggest perusing this fund’s hefty 912 page prospectus? When I did that I came up with three things that made me a little wary.

I think many investors (I know I was) would be hard pressed to fully understand how this product works.
The fund attempts to replicate index returns via the use of OTC swaps, which of course means we need to consider counterparty risk. Thankfully ETFs tend to have very strict collateral requirements to protect investors from counterparty risk (in fact this ETF is actually over-collateralized), so in theory this type of risk is somewhat mitigated.
There is a section entitled, “Potential Conflicts of Interest”, which basically goes on to say that other “DB Affiliates” (i.e., divisions of the same company that manages the ETF) may act as the counterparty for swap transactions and contracts amongst other things. In fact, Deutsche Bank AG is the counterparty for this particular fund’s swaps.

So why do the above items make me a bit wary? Well, for starters, investing in a product like this is a potential violation of the “invest in what you know” philosophy to which I subscribe. Second, while the db x-trackers may be amazingly collateralized, how can we be certain about the quality of the collateral (e.g., are illiquid or hard to value assets allowed to be used as collateral)? And what happens if something bad befalls Deutsche Bank? Remember,as the counterparty to the swap contracts they are also the ones supplying the collateral. It was just a couple of years ago that some other large, reputable financial institutions became embroiled in circumstances which ultimately led to their default or even dissolution and taught a lot of people that counterparty risk is definitely a real risk. Personally I am not quite ready to assume that a situation like that can never happen again.

Not surprisingly I am not the only one who is concerned about the risks of products like synthetic ETFs. As I was wrapping up this post I noticed a news story with the title “EU watchdog may ban some ETF retail sales," which goes on to say that ESMA (The European Securities and Markets Authority) is considering establishing some controls around the sale of synthetic ETFs to retail investors.

I am not trying to pick on the db x-trackers (or even synthetic ETFs as a group) and I am definitely not suggesting people avoid these types of products. Rather my main goal in writing this is merely to suggest that, while ETFs are a fantastic investment tool, investors need to cast the same critical eye on ETFs that they do on other complicated investments and not be lulled into a false sense of security because of their popularity and easy access. With the seemingly explosive proliferation of Exchange Traded Products coming to market every day I can only imagine that these products will only become more and more complicated and less and less transparent.

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ETFs: The next bubble? (Part 1)

Andrew Kovacs — 2011-07-29T21:12:39Z

Recently I have had discussions with folks inside and outside of FactSet about the seemingly exponential growth of Exchange Traded Funds (ETFs) and their cousins Exchange Traded Notes (ETNs) over the last few years, and whether or not this growth should be cause for concern amongst investors.

When I started at FactSet back in 2000, ETFs were just starting to regularly appear in client portfolios (I know they had been around for quite a while prior to 2000). There were only a few names that came up back then—SPDRs, iShares, and HOLDRS—which I was able to confirm with a quick backtest that showed we had data for about 95 ETFs as of December 2000. I thought as a starting point for my comments I would begin with a quick “then” and “now” comparison of the ETF market and how it has changed since 2000 and today. Please bear in mind that this is purely from my perspective and by no means intended to be a detailed development history of the ETF market.

Then: If a client held an ETF all he wanted to do was make sure we could supply a price and make sure it contributed to his portfolios’ total market value and return.

Now: If client holds an ETF she still wants to be able to represent it as a single physical asset in the portfolio, but she also wants to be able to “look through” the ETF in an attempt to understand the direct and indirect exposures. She may also want to see how the constituents of the underlying basket of securities contribute to the portfolio's overall ex-ante tracking error (which of course requires additional data and functionality beyond a simple price).

"If a client holds an ETF [today] he or she...wants to be able to 'look through the ETF in an attempt to understand the direct and indirect exposures..."

Then: ETFs were primarily set up much like traditional equity index funds in that they actually held the same basket of securities in the same proportions as the equity index or sector they were attempting to replicate.

Now: There are a variety of ETFs now ranging from the traditional index funds, to actively managed ETFs, Commodity ETFs, Bond ETFs, Currency ETFs, and Synthetic ETFs (I’ll come back to some of these in a minute).

Then: In 2000 there were only 3 major ETF sponsors: Barclays (iShares), State Street (SPDRs), and Merrill Lynch (HOLDRS) and about 95 ETFs in the marketplace.*

Now: As of July 2011, there are currently 35 such sponsors in the U.S. and Canada alone (plus a bunch more in handful of other countries around the world) and approximately 1,500 ETFs/ETNs in the marketplace.*

As you can see from the information I've shared above, the appetite for and the number of ETFs available in the market place is rapidly increasing.

In the next post, I will address why I feel investors may want to tread with caution when it comes to the ETF market, particularly synthetic ETFs. I'll analyze some examples and explain some points you may want to consider when investing.

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* The numbers I have provided are based on counts I generated from security screens and data available on FactSet. As a secondary reference I also reviewed some stats collected by the Investment Company Institute (ICI) 2011 Fact Book. This reference provides a great overview of US Registered Investment Companies and provides a chapter on the current state of the ETF market in the U.S. For instance, the ICI Fact Book details how, from 2000 to 200, the number of ETFs in the U.S. grew by 279 and the net asset value increased by about $357 billion USD. From 2006 to 2010 the number of funds increased by an additional 591 and the net asset value climbed another $569 billion.

From News Corp to national crises, don't judge market volatility by the headlines

Bryan Hoefs — 2011-07-18T15:53:25Z

Over the past few days and weeks, the biggest story in the UK has been about News of the World—the tabloid that allegedly committed several crimes in order to publish more and more sensational stories. Journalists appear to be under incredible pressure to come up with headline-grabbing articles.

It's not just the tabloids that are creating sensational headlines. Words like rocked, soars, plummet, panic, and contagion occur very frequently on business headlines these days. Whether it’s the U.S. threatening to go into default if the Democrats and Republicans can’t come to some sort of an agreement by August, Eurozone debt problems, or the potential breakup of the Euro, it all must surely impact the volatility in the markets. After reading recent headlines, it’s understandable if you believed that volatility was currently at an all-time high.

But is volatility high, in fact?

Overall, it’s hard to say it is, at least when looking at the recent past. If you look at the VIX, commonly used to gauge anticipated volatility in the markets, there isn’t evidence for high levels of volatility at present. Yes, it has increased somewhat over the last few weeks, but the VIX is still at its lowest levels since 2007. At the start of the financial crisis at the end of 2008, it was three times higher than today.

The Eurozone debt crisis has caused a lot of concern in the markets, and volatility has increased for the most affected regions. For example, on July 11, the spreads of Italian bonds relative to the German benchmark bond increased by the largest amount since the existence of the Euro, and have hit a record high.

While the European and Italian equity markets have been more volatile than other equity markets, volatility still isn’t at the same level as few years ago. Using the R-Squared Equity Risk model, which is a short term equity risk model available on FactSet, I’m comparing predicted absolute risk of the MSCI Italy index and MSCI Europe. You can see that the levels predicted aren’t nearly as high as a few years ago, even for Italy.

Just to return briefly to News Corp, it’s safe to say that single stock volatility has increased. The stock is down 15% over the last 10 days since story broke. Despite these levels, even this controversial stock is still not at its most volatile point. Moves relative to the S&P 500 were much greater in 2008 and 2009.

So while individual markets and companies have seen increased volatility over the past few weeks, it’s nothing like the systematic increase in risk seen in 2008. What will happen if the Euro breaks up, or the U.S. doesn’t make its interest payments, remains to be seen. This is just another reminder that you can’t judge a book by its cover, and you can’t make assumptions about underlying volatility simply by reading the headlines.

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New Quants: More Reflections on Adapting

Steve Greiner — 2011-07-06T18:01:01Z

There was a lot of smoke blowing immediately after the credit crisis as we looked for sources of blame. One book that got a lot of press was The Quants by Scott Patterson. Mr. Patterson was a Wall Street Journal reporter and clearly has a flare for the imaginative. While The Quants makes what is normally a quite dry subject (like accounting or actuarial science) an easy read and adds adventure to the quant story, there’s much in it that’s inaccurate and hyperbolic. For instance, Patterson presents conversations (one between a waiter and Cliff Asness and one between Peter Muller and Ken Griffin) in language I find hard to believe occurred verbatim.

One of the main themes of the book however is really about how Wall Street whiz kids brought the house down with their impetuous, brilliant, and extremely aggressive nature. It implies that they made their fortunes by robbing less intelligent clients and their investors. It also proposes that, simultaneously, other quants used badly mis-specified models based on the normal curve (specifying default correlation among bonds for instance) to underprice risk and bring about huge losses for the banks. If you took both exaggerations and divided them by 10, you’d probably reach something nearer the truth. The overall pain of losses was spread pretty much across all quants (except John Paulson) and non-quants during the credit crisis.

It's ideas and exaggerations about who specifically was at fault during the crisis that led me to write my book, Ben Graham Was a Quant.

However, one good takeaway from Paterson's book is that it makes you think about whether quants have learned anything about major market turns and whether they’ve adapted their models and modeling techniques to consider the impact of “Obsidian Uncertainties” (i.e., Black Swans) and ELE (extinction level events). The answer to that question is a stark yes.

Have quants modified their models to consider the impact of Black Swans and ELE? The answer is a stark yes.

The first example of it comes with UCITS mandating VaR requirements for EU mutual funds to less than 4 breeches per year at 99% CI. In 250 trading days, 2.5 breeches of the 99% VaR is right on target. UCITS mandate therefore is a signal to quants to “tighten up.”

Another good example comes from the increasing usage of stress testing one’s portfolio against variables that could move your portfolio toward large losses. Thus, it’s no longer sufficient to create an Alpha model based on backtesting through turbulent periods alone. Now quants are examining their quantitatively derived portfolio behaviors by using covariance matrices from the past to forecast the risk from credit crises, LTCM debacles, Asian contagion security dependencies and so forth, all of which involve situations where idiosyncratic risks take a backseat to market risks in a major way.

Crisis events are characterized by factor efficacy falling off considerably while securities increase their correlation as well. When that has happened in the past, quants used to hold firm and wait for the correlative nature of the markets to return to pre-crisis levels. Stock “de-correlation” meant factor efficacy was returning. Now however, quants have learned that these periods may persist for long periods of time and that one way of prepping your portfolio for these events is to examine forecasted risks from short horizon risk models (~1 year of daily values).

Crises are characterized by factor efficacy falling off while correlation among securities increases. When that used to happen, quants held firm and waited for correlations to normalize. Now, quants have learned to expect longer crisis periods and take steps to predict market inflections faster.

The tricky part is, adaptation in one’s overall investment strategy due to market conditions is exactly what Ben Graham taught us not to do. During the technology bubble, for instance, many value investors moved more toward growth only to go out of business when the bubble burst, besides introducing enough style drift that consultants fired them for that reason alone. Ben Graham’s philosophy is about maintaining investment process discipline and not reacting to the whims of Mr. Market. However, for the truly post-modern quant, adaptation is the discipline. From the quant’s perspective, application of the Ben Graham principles to the investment process is about adhering to risk mitigation “come hell or high water." It’s about providing the portfolio with a margin of safety through the methods available in the quantitative art.

The credit crisis has truly allowed quants to leverage their methods and think about risk in new ways. This indeed has raised the IQ of the intelligent quantitative manager.

In this way, inflection points of the market are more quickly spotted than using a long horizon risk model (~60 months) and adjustments to the portfolio can occur by rotating more quickly to factor bets that are more efficacious in the new environment.

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Talking risk with model providers: Recap from the FactSet European Symposium

Sean T. Carr — 2011-06-03T18:02:52Z

I was fortunate last week to be able to spend 40 minutes at the extremely popular, standing-room-only Risk Panel session at our European Symposium. In the room were a host of individuals representing all of the risk models currently offered by FactSet:

Sebastian Ceria, President & CEO, Axioma
Dimitris Melas, Executive Director & Head of Research, MSCI BARRA
Anish Shah, Senior Researcher, Northfield
Jason MacQueen, President & Founder, R-Squared
Laurence Wormald, Head of Research, Sungard APT

It was an opportunity for panel chair and fellow blogger Steve Greiner to pose some industry specific questions that he felt relevant to the audience, and also for an exchange of views regarding some of the fundamental differences in the approaches of the different companies.

A transcription could never do the discussions full justice but I did want to highlight a couple of the points raised as worthy of a secondary airing. I have therefore summarized a couple of the questions and selected relevant answers below. I hope that you will also find them interesting:

Chair: What’s the most appropriate way of incorporating commodities into a multi-asset class framework?

Laurence Wormald, Sungard APT: The answer is to build a proper multi-asset class framework–not to select some factors as being suitable for some assets, other factors for different assets. Principal Components is a decent methodology for capturing, on average, the moves across asset classes. It is important to note that modelling of commodities is not just about a spot price, but that it is also important to include the forward curve in the calculation.

Anish Shah, Northfield: Northfield has had Commodities in their model for years, as the factors within their models capture the varying nature of the correlations through regional, industry, and factor exposures. A simple correlation analysis of Gold with S&P 500 shows a swing between positive and negative, so it is important not to get too fixated on an accuracy that cannot be achieved.

Chair: How important is it to use a risk model that has factors that are common to your investment process?

Sebastian Ceria, Axioma : Optimizers find holes in a risk model and ‘loads’ on those holes. One way of getting around this is building your own risk model — the problem here is that if you are wrong then you may not find out until it is too late. Another challenge is understanding how your constraints (imposed or welcomed) affect your alpha capture. Axioma developed a technique called the alpha alignment factor to attempt to solve these ‘risk under-estimation’ problems.

Jason MacQueen, R-Squared: One explanation for the inability of managers with skill to outperform their indices is the unsuitability of their portfolio construction process to reflect where they feel their ‘alpha’ bets should be. The selected risk model used in this process is a major factor in this breakdown. It is therefore crucial to have a risk model that best reflects your own definitions of sector, industry, valuation, etc., to reflect the investment process and maximize ‘alpha’ capture.

Chair: For your particular set of risk models, what is the strongest element of the forecasting methodology, e.g. the risk factors used to build up the systematic portion of risk, or the mathematical techniques used in model development?

Dimitris Melas, MSCI BARRA: By far the most important element of our methodology is to use fundamental factors in our risk models, going back several decades to the work of Barr Rosenborg. Fundamental factors have a number of inherent advantages, aligning very well with the investment philosophy of both institutional fundamental as well as quantitative managers, and also you are able to predict the risk of assets that do not have a long trading history.

Sebastian Ceria, Axioma: Whether you are using fundamental or statistical factors (we use both), it is important to analyze how you build those factors. We use daily data to give better granularity, updating the models daily also, but that in itself brings its own challenges due to the asynchronous nature of global markets. Monthly data updates introduce several problems including “crowding,” as was seen with a lot of quant managers in 2007 and permits people to “front-run” and take advantage of up-coming trades.

Jason MacQueen, R-Squared: I’d also like to add that the mathematical limitations that force a discrete beta allocation to industry, country, etc. is done for convenience and leads to inaccuracy. You cannot assume that all French Banks have a Beta of 1 to France and a Beta of 1 to Finance, allowing these betas to float gives a much better forecast.

Dimitris Melas, MSCI BARRA: The alpha and risk factor misalignment problem does exist, but there are simple and straightforward ways that do not require the creation of a whole new theory and technology to tackle it, one shouldn’t necessarily use a sledgehammer to crack a nut.

---

Finally, I’d like to reiterate my thanks to all of the individuals involved in delivering a most entertaining session, feedback was great from all attendees who relished the opportunity to see the providers in a unique environment (and wonderful setting!)

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What is the true size of China's monetary might?

Steve Greiner — 2011-05-27T18:03:21Z

I've been thinking about big numbers a lot lately. Specifically, large amounts of money.

No number needs more transparency than the U.S. debt level.

It is always easier to gauge the magnitude of a number when viewed collectively. As with people’s heights, when somebody 5’6” is standing next to somebody 6’5”, it’s easier to grasp their values in when compared.

First for comparison, the U.S. GDP these days runs around ~$15 trillion dollars. That’s $15,000,000,000,000 per year that our economy produces. The U.S. debt is also of similar magnitude, but with a sign change (-$14.7 Trillion) making the debt to GDP ratio about ~100%. It’s 140% for Greece and over that for Japan. The difference is Japan’s debt is 90% owned by its own people, whereas the U.S. debt is half owned by Americans, the other half is owned outside the U.S.

Now, the current budget deficit of the President of the United States is around $1 trillion if there are no cuts (there will be). What does this mean? If this budget is passed by congress, it would raise the U.S. debt by a trillion in a single year, to $15.7 trillion. This amounts to ~$52,333 per U.S. citizen, very roughly.

The U.S. runs a trade deficit every year. In services (consulting, paperwork, general business), we give more than we take in, but by far and away we take in more foreign goods than we give out. This deficit runs to -$668 billion per year or about -4.5% of GDP. China on the other hand has a GDP of about 1/3 of the U.S. of about $5 trillion per year, with a trade surplus of $169 billion, which is about 3.4% of their GDP. However, China has a surplus of foreign exchange reserves of $3 trillion whereas the U.S. has...well, debt. This $3 trillion in reserves is 60% of China’s GDP.

Now, this Chinese surplus is invested in over a trillion dollars of U.S. Treasuries. They on the other hand, have no interest in our dollar falling (devaluing) as it has been, as their investment is losing money when that happens. Nor do the Chinese want the U.S. to default; if it does, they won’t get paid dollar for dollar either. So given the fear of that happening, what might the Chinese invest these proceeds in to diversify away from U.S. Treasuries?

Well, for one the entire amount of commercial mortgages owed in the U.S. collectively is $2.4 Trillion. Meaning the Chinese could pay the entire amount of listed mortgages on commercial real estate in the U.S., take a huge ownership in buildings and land here, and still have $600 billion leftover. The 2008 to 2010 loss in total real estate in this country was $8 trillion, just to put things in perspective.

China could pay off the entire debt of Spain, Ireland, Portugal, and Greece and still have $1.5 trillion leftover, a full half of their surplus. In addition, using this half of their surplus, they could buy all outstanding shares of Apple, Microsoft, IBM, Google, and Exxon.

Or they could spend the whole $3 trillion and buy Exxon, Apple, GE, Microsoft, IBM, Chevron, Berkshire Hathaway, Walmart, AT&T, Proctor & Gamble, Johnson & Johnson, Oracle, JPMorgan, and Google. They’d spend all their money, but considering that on January 1st their surplus was $2.85 trillion and by the end of March it was $3 trillion, after buying all these companies, by the end of next month, they’d have another $15 billion in cash to do something with. If China bought all the companies in the Russell 2000 index of small cap stocks, all 2000 of them, they’d still have $1.4 trillion dollars left over.

All of Manhattan’s taxable real estate amounts to just shy of $300 Billion. China could buy the whole island and have over $2.5 trillion dollars left! We could throw in all the property of Washington D.C. for another $232 Billion and make them overpay and they’d still own Manhattan and D.C. and have $2 trillion leftover!

Now imagine if you will; an alternate universe in which the U.S. had a trade surplus of 60% of our GDP like the Chinese? A whopping $9 trillion dollars instead of -$14.7 trillion in debt! Who of us would be worried about social security, health insurance, and Medicare under that circumstance? Food for thought!

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Is the world running out of commodities?

Steve Greiner — 2011-05-24T18:00:51Z

Recently, I read a very thorough analysis of long term commodity prices from one of my favorite strategists which prompted me to think about natural resources. I wondered: Have we entered a new paradigm when it comes to world natural resource use and distribution? This basic premise has to do with population growth, namely, the rise of China and India and their consumption of resources at a scale the world has never known. All this consumption sits on top of the developed world’s continued use of materials and resources.

For instance, as of 2010 China’s share of global consumption was:

Cement 53.2%

Iron Ore 47.7%

Coal 46.9%

Pork 46.4%

Steel 45.4%

Lead 44.6%

Zinc 41.3%

Aluminum 40.6%

The list of resources that China consumes at great rates will only increase as the world’s second largest economy continues to grow unabated.

Allow me to use oil as a data point: From 1878 until 1971 oil hovered about $16/barrel, a small amount in today’s dollars. However, from 1971 to the present, it rose precipitously and today it is not only at record levels but has moved 6 standard deviation above $16 in today’s dollar terms. Brent Crude closed as high as $117.52 recently. With so many other commodities performing the same way, is this demonstrative of a paradigm shift in global natural resource supply and demand? Another example helps to put a nail in the coffin on this idea: Since 1994, one has to dig up an extra 50% of ore to get the same tonne of copper and this 150% effort has to be done using energy at 2 to 4 times the former price.

I read Jim Roger’s book, Investment Biker in 1997. He had finished a motorcycle ride around the world and much of that book is a mini-summary of global economics. He was the first person I heard talk about the coming commodity boom and his visits to many developing countries during this trip convinced him the world would soon be needing tremendous amounts of raw materials. More than 13 years later, we are seeing this come to pass.

For the U.S., the purchasing power of the dollar continues to fall, especially relative to other currencies. The following chart shows the return of Silver, Gold, and Brent Crude from May of 2009 until now, in various currencies. Hong Kong currency represents the Yuan in this plot since the HKD is pegged to the dollar. Notice however that measured in Swiss Francs, Australian, and Canadian dollars, the appreciation of these three commodities hasn’t been nearly as severe as compared to what U.S. dollar consumers are paying (or earning on these commodity investments). Is the fall of the dollar inviting demand for commodities as a hedge?

This run-up in prices hasn’t been missed, and just before the credit crises of 2008 began, speculators took the media’s blame for the huge price increases even though the CFTC’s Interagency Task Force’s July 2008 Report on Crude Oil said:

The Task Force’s preliminary assessment is that current oil prices and the increase in oil prices between January 2003 and June 2008 are largely due to fundamental supply and demand factors. During this same period, activity on the crude oil futures market—as measured by the number of contracts outstanding, trading activity, and the number of traders—has increased significantly. While these increases broadly coincided with the run-up in crude oil prices, the Task Force’s preliminary analysis to date does not support the proposition that speculative activity has systematically driven changes in oil prices.

There have been other reports offering the same vindication that the fundamentals of supply and demand are changing such that price rises of commodities are due to shortages. Currently corn stockpiles are at decade lows. How can this have any other impact other than that corn futures must rise, especially when growing middle class consumers in China, Indian, Vietnam and Indonesia are hungering for more (historically) western styled foodstuffs?

The next chart documents commodity index price rises in energy, petroleum, industrial and precious metals, agriculture, livestock, and softgoods. Never before has the correlation across varying commodities been so high (i.e., all commodities moving lock-step in one direction, up) other than during WWI and WWII when shortages abounded on a global scale. Fortunately we’re not in a global war, but the cause is likely the same, global shortages of commodities.

Another point of reckoning has to do with the much larger availability of commodity ETF’s and mutual funds which are allowing the retail investor to participate in this asset class. A decade ago one had to buy physicals, with ensuing storage problems, or futures, both difficult for the retail market to handle. In addition, the emergence of pension funds and institutional asset managers which make commodities part of their holdings too gives credibility to the bull market in commodities as well as helps to keep prices higher by creating demand for the asset class.

Nevertheless, regardless of the cause, the demand means there’s more reason for commodities prices to continue to rise until this demand can be met. The question I’m having a hard time answering is, will it?

Getting a picture of your portfolio holdings, from any angle

Chris Ellis — 2011-05-24T17:58:57Z

On May 10, Chris Ellis will present on Security Exposures Analysis at the CFA Annual Conference in Edinburgh, Scotland.

Security Exposures Analysis, which we have addressed on the blog before, helps users understand how trends in the market may impact their holdings. For example, it can provide a manager with a view of which portfolios have a heavy weighting in Japanese banks. Chris describes that you can take the analysis further by focusing on thresholds (e.g., every security representing more than 3% of portfolio weight).

As a preview to Chris's presentation at the CFA Annual Conference, the following 10-minute video helps to introduce the topic. For more in-depth information, listen in on parts 1 and 2 of our podcast on the implementation of Security Exposures Analysis in FactSet, linked from the bottom of this post.

Preview Chris's CFA Annual Presentation "What's Your Risk? Security Exposures Analysis"

See it live, May 10, 2:00 p.m. in Edinburgh, Scotland at the CFA Annual Conference. Details here.

For more information on Security Exposures Analysis, please listen to our two part podcast series.

Part 1: Understanding the basics: Why exposure analysis matters

Part 2: Examining the trends: Expanding exposure analysis to boost manager performance

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Defining "Quant" After the Credit Crisis

Steve Greiner — 2011-05-17T18:10:09Z

Warren Buffett has an undying reputation as perhaps the most quintessential investment manager who has ever lived. However, what is not as well known is that he was a student of Benjamin Graham’s. I was overheard some time ago as describing Ben Graham as being a quant. I brought this up because of the shellacking quants were taking: Being accused as the cause of the credit crisis of 2007-2009. The point I was making was that, in spite of the hysteria of the credit crisis (when quants were loudly called the harbingers of the financial meltdown) Ben Graham’s own words put himself in their camp and nobody would argue about his investing acumen nor discredit his methodologies the way they were disparaging quantitative investment methods.

Way back in 1949 when Graham published The Intelligent Investor, he listed seven criteria that in his opinion defined “the quantitatively tested portfolio.” There cannot be any other interpretation than that of the author himself, who concluded that the application of these criteria builds a quantitatively derived portfolio. Thus begins quantitative asset management, its birth given to us by Benjamin Graham, who, even in the latter years of his retirement while living in La Jolla California, continued to research imaginative ways to “auto-magically” invest one’s assets in purely mechanical ways.

To be a quant puts one in league with Ben Graham, practicing active management.

Ben Graham stated, “I deny emphatically that because the market has all the information it needs to establish a correct price, that the prices it actually registers are in fact correct.”

His sentiments are echoed in the words of several esteemed managers and quants who came after him. These investors include Mohamed El-Erian, the very successful Harvard Endowment CIO who now works at PIMCO; Jeremy Grantham of GMO; and lastly, George Soros, who said, “First I contend that financial markets never reflect the underlying reality accurately, they distort it in some way or another and those distortions find expression in market prices.”

It is an interesting observation to me that many long-time practicing investors with healthy investment records stand on the side of inefficient markets, whereas inexperienced (that is to say, from investment management experience) academics support the opposite view and the Efficient Market Hypothesis.

Thus we begin both a diagnosis of Ben Graham the man and his investing technique and the application of modern quantitative processes to Graham’s methodology. Thinking of all this, I began to write Ben Graham Was a Quant, in part to remedy the miscommunication that led to a disconnect between how the quantitative method ought to be practiced, and how it was interpreted to be practiced by the public during the Credit Crisis. I tried to define in the book what exactly quants are, how they ply their trade, and what the benefits are to quantitative asset and risk management. For instance, the following charts from chapter 6 in the book, typifies the ease with which Ben Graham’s “recipe” can easily be coded within FactSet’s Universal Screening module. Here we show very simple FactSet mnemonics for a simple screen, using Graham’s methodology.

This kind of script can also easily be ported directly to FactSet Alpha Testing, where returns can be calculated simply by sorting of these factors over a long time period as shown in the next table.

Then, the output of the Alpha Test can be imported directly over to Portfolio Attribution module within FactSet for reporting and independent analysis. The ease of building these models; running them through a series of tests; and reporting their returns, characteristics, and statistics has surely made the workday easier and more productive for the practicing quant.

Of course, my book covers much more than what quants do and how they work. Throughout, I cover defining Alpha and risk, then work on using FactSet software in Alpha Testing to run factor studies and statistics. Finally, I attempt to tie the two parts together by discussing where Graham gets his “Alpha” from and how large it really is. There is, of course, much more than I can address here, but I’d encourage anyone interested in really understanding the depths of the quantitative method, as defined by the great Ben Graham, to consider a look inside the cover.

Three minutes on Ben Graham Was a Quant

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The Value of Updating Risk Models Daily

Dr. Sebastian Ceria — 2011-05-17T18:10:42Z

Contributed by guest blogger Dr. Sebastian Ceria,
CEO at Axioma, Inc.

Long-term Investment Approaches Cannot Ignore
Short-term Volatility

Most institutional investors seek long-term returns. So the idea of using risk models that are updated daily has often been dismissed as overkill by portfolio managers focused on long-term investments.

The argument goes like this: Risk models that are updated daily, even if calibrated for longer investment horizons, will only drive one toward short-term trading and excessive turnover. Such results are, by definition, at odds with a focused long-term strategy.

In theory, that sounds fine. In practice, unfortunately, the argument crumbles as portfolio managers confront the need to respond both to asset-owner angst, compliance constraints within their mandates, and the effects of very real events with global impact. Indeed, when one considers the nature of investment management today, and the contract between investment managers and their clients, it’s simply not realistic to expect asset owners and portfolio managers to blithely ignore unnerving events of the sort we’ve seen lately: Japan, the unrest in the Arab world, Portugal, the Gulf oil spill, and so on.

Long-term investment strategies cannot ignore the short term. And let’s face it: Many portfolio managers rebalance more frequently than they are likely to acknowledge. Why? Because nowadays new information that affects their decision process is constantly available. Or perhaps they are adding funds to their portfolio. Or withdrawing funds. Or adapting in some other ways to changes in the market.

Which brings us to the disaster in Japan, a timely case in point.

Risk models can get hung out to dry for their failure to effectively predict events. The earthquake in Japan was, obviously, a sudden, wholly unpredictable natural disaster. To say that risk models—factor risk models, anyway—failed to predict the tsunami is, of course, absurd. The only course of action in a case like this is to adapt to the situation. The only way to do that is to update your models with new information.

The question is, do risk models that are updated daily react to such events fast enough to provide useful insights? Research papers published by Axioma on March 18, 2011 (Market Aftershocks? The Global Impact of the Japan Earthquake as Seen Through the Lens of Axioma’s Daily Risk Models) and a subsequent paper published on March 28, 2011 (Market Aftershocks? A Seven-Day Update) addressed this very question. Axioma’s risk models are updated daily, so we were able to observe market developments as they occurred. Furthermore, risk models updated on a daily basis also provide access to up-to-date risk factors (B/P, ST/MT momentum, size, etc.). These factors are particularly relevant for value/growth managers who take exposures to the respective factors very seriously. While the covariance matrices evolve slowly, the changes in the risk exposures can be quite dramatic in response to major unexpected events, thereby adding additional value to daily updates.

The Japan earthquake and tsunami occurred on March 11; roughly speaking, the middle of the month. For portfolio managers with a direct Japan exposure, the disaster clearly mandated a rebalancing. If they used a monthly model, there were but two choices: use either the old covariance matrix from Feb 28^th, which they knew was wrong, or wait 20 days for an updated model. The opportunity risk is too big to even contemplate.

Event-driven market surges in volatility and opportunity are likely to be the the rule rather than the exception going forward. The current environment of high correlations, combined with the highly liquid cash flows into and out of ETFs, which have a leverage effect on the market, makes the market especially susceptible to rapid responses to market events.

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Is Diversifying in Asia Dead?

Willett Bird — 2011-03-18T18:00:49Z

I recently had an interesting meeting with a senior Portfolio Manager here in Hong Kong at a large multi-national firm which has been working in Asia for over twenty years. After the initial pleasantries, I asked her how her funds were performing this year: “Great, we’ve been bullish on China and Oz and those have both paid off for us well this year.” Yet I was more intrigued by her reply when I enquired about fund inflows. I’m paraphrasing here, but basically her answer was:

Inflows have been fantastic as there continues to be a global perception that investing in Asian equities is a materially diverse investment. This simply is no longer the case. Americans and Europeans think that by shoving money across to Asia they will immediately pick up diversification benefits, and while I am the beneficiary, they are simply wrong.

Upon returning to my desk, I decided to do a little research. On the web, it was easy to find arguments in for both sides, and all of them seemed (or claimed to be) “conclusive. So I decided to do a little research on my own. I went into FactSet’s Chart Center application, selected the FactSet Market Aggregates for the major countries in the Asia Pacific region, and ran a simple rolling 2-year correlation versus the FactSet Global Market Aggregate.

Click image to enlarge

Clearly, there has been a material change in the diversification benefits of investing in Asia over the past 10 years, as you can see the spreads between countries grow tighter and tighter throughout time. My client was correct that over the last 10 years, the correlations versus the world have gradually converged, especially leading into the Global Financial Crisis. During this period, the correlations remained high and steady but it is worth noting that over the last 6 months, the correlations between these countries and the World have started to taper off.

Balanced Risk Explained

Steve Greiner — 2011-03-17T21:45:03Z

In our interview with Dr. Steve Greiner, FactSet's Director of Portfolio Risk Research, we discuss the concept and details of FactSet's Balanced Risk.

Listen to our 15-minute discussion with Steve Greiner using the player below, or read highlights from the interview below to understand more about the unique ways that Balanced Risk can be used to understand a cross asset-class portfolio.

More on Steve Greiner's presentation at GARP's 12th Annual Risk Management Convention, March 9 in New York City.

What does Balanced Risk accomplish?

From a 10,000-foot view, balanced risk sets out to accomplish three things: 1) Allowing the user go back through market time periods to understand risk based on real past circumstances, 2) Determining cross-asset class correlation, 3) Providing added Value at Risk and Stress Testing measures that fit in well with vendor models also available on FactSet.

How does building a risk model for the equity side differ from building one for the fixed income side of the market?

On the equity side, risk models are really built as what we would refer to as a multi-factor risk model. Due to liquidity issues, you cannot create a multi-style risk model for fixed income. You must, instead, understand what common fixed income risk factors exist and how they interact with interest rates.

Why is VaR considered to be only partially accurate and how can its accuracy be improved?

VaR, by definition, cannot be exactly accurate.Whether we use a risk model with one year of daily data or 60 months of monthly data, VaR is still attempting to predict how your portfolio will look at some future point. To use an analogy of the weather, if you look outside and attempt to predict the weather over the next two hours, or if you try to predict the weather over the next two weeks, we know that your first forecast will be more accurate. Therefore, regardless of whether your VaR numbers are built on a longer or shorter look-back period, it is really a matter of whether you're asking your VaR to predict portfolio performance one week, one month, or one year from today that determines accuracy. To summarize, the shorter the forecast horizon, the greater the accuracy of the model.

What is one unique aspect of FactSet's risk product?

We allow the user to go back in time and chose from specific stressed periods of the market and choose the covariance matrix from that time period. The user can then refer to that covariance matrix to calculate the VaR of his or her portfolio today if that situation was to occur. So essentially, you can compare what your VaR would be using the dependence structure of assets in the past versus the structure today.

However, whether you create a VaR for fixed income based on the covariance structure of the past or the one that exists today, the only difference for fixed income will be the period of time of data. For fixed income, the issue comes back to being able to use Monte Carlo scenarios using interest rate shocks, and so forth.

Our Most Anticipated Sessions at GARP's Annual Risk Management Convention

Michael Pappas, Quantitative Specialist & Matthew Cioppa, Senior Consultant — 2011-03-17T21:46:25Z

This March 8-9, FactSet will be attending a two-day conference hosted by GARP (the Global Association of Risk Professionals). We’re very interested in a number of sessions and hope to see some of our loyal blog readers there. For now, our attendees wanted to tell you about a couple of sessions that they're very interested in attending at this year’s GARP Annual Risk Management Convention. Perhaps you’ll see our Quantitative and Risk FactSetters at our booth.

Note: If you’re attending, don’t miss FactSet’s own session on Balanced Risk. Learn more.

We’re most excited to check out the session on “reverse” stress testing: Growing Role of (Reverse) Stress Testing (Tuesday at 1:30 p.m.). As we understand it, “reverse” stress testing includes thinking of the possible negative outcomes or vulnerabilities for the firm, enterprise, or portfolio, and then identifying scenarios that might cause this to occur.

A good stress testing set-up needs to take multiple factors into account to be robust and dynamic, and managers or creators of those factors/stresses should seek to choose a set that includes both events likely (or feared) to occur, as well as events which have specific meaning to the holdings of the portfolio or enterprise in question. For example, the Portfolio Manager who also engages in currency speculation will want to test his portfolio against changes in exchange rates in addition to index and economic shocks.

From the portfolio aspect, there seems to be little or no difference between reverse and regular stress tests, so it will be good to gain some color on how this applies on an enterprise risk level and what tools can be used from our financial risk background to help achieve a stress test on an enterprise level.

Unfortunately the session on reverse stress testing occurs at the same time as another interesting one: Navigating the Regulatory Landscape – Practitioner’s Perspective (Tuesday 1:30 p.m.). It would be interesting to hear some of the public opinions from clients on how these regulations will actually affect them in their day-to-day. We’re also curious as to what extent banks are losing top talent to hedge funds, or what, if any, repercussions have been felt internally for the groups impacted by these regulations. Given the potential overlap of Dodd-Frank and Basel III, there’s bound to be a lot of contention about how the two rule sets interact, and what rule set will be enforced in the case of an interaction or contradiction. Is it just me, or does anyone get the visual of a 1-pane New Yorker cartoon where a kid pits his parents against each other, whining “But Mom said it was OK?!”

Another interesting session is in the track “The Next Crisis.” Akshay Kapoor has a lecture entitled Managing Credit Risk in a Post Crisis World (Tuesday 4:30 p.m.), and we’re eager to hear his take on how the recent credit crisis is affecting trends in the use of credit risk management by institutional investors. The crisis taught us that there is a great deal of instability in the credit market and suggests that credit models that are currently being used might need to be revamped. The emphasis on flexible models and on understanding how risk impacts the credit markets has been a continued interest among FactSet’s risk clients and indeed our own session at the event discusses the importance of modeling risk for the fixed income side of the portfolio.

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Capturing Euro-Sovereign Default Correlation through Contingent Claims Analysis

David Mieczkowski — 2011-02-10T19:01:53Z

You’re the managing director of enterprise wide risk management at a major bank. A European balanced mandate portfolio manager in the asset management group is boosting his yield by being overweight the PIIGS and underweight the Euro Stoxx 50. The FX desk is managing a healthy P&L by putting on a USD/EUR carry trade using a weighted average position in Euro-Sovereign treasury bonds. A European CDS trader has sold a first to default swap on a basket of Spanish and Portuguese firms, with a large notional amount, to a European hedge fund and is partially hedging it by taking short positions in Spanish and Portuguese CDS. How do you assess the individual and combined risks to the firm posed by these three activities?

Obviously, a good Euro-Sovereign credit model is necessary, but as the examples point out, a single country model isn’t sufficient. We need a model that is capable of capturing not only the interdependence of the Euro-Sovereign credit risk itself, but one which is also capable of capturing the interdependence between Euro-Sovereign credits, Euro-Corporate credits, and European equity positions.

In a pair of earlier posts (Why default correlation matters Part I & II), I discussed how default correlation is central to analyzing the VaR of a portfolio of corporate credits. I also outlined how firm value models (equivalently, Contingent Claims Analysis) provide a convenient framework to capture correlation between corporate credits, as well as between corporate credits and equity. In this post, I’ll discuss how that framework can naturally be extended to include Euro-Sovereign credits, and provide some evidence of its efficacy.

The Euro-Sovereign Spread Model

The model will use the basic contingent claims framework that underlies the Merton model. To use this balance sheet centric model, we will need to determine suitable inputs for:

The market value of the junior liability claim (equity)
The volatility of the junior liability returns
The notional amount of the senior liability claim (bond) or Debt-to-Equity ratio
The duration on the senior claim
The risk free rate
Recovery rate upon default

With these inputs, we can determine the asset value, the asset volatility, and then the market value of the senior liability claim (bond price). The last input is technically not needed in the Merton framework, but it is convenient to specify a recovery assumption and utilize the approximate relation amongst spread, probability of default, and recovery to determine the spread:

Obtaining these inputs in the corporate model is straightforward. For the Euro-Sovereign model, I use the following as proxies:

FactSet Country Aggregate index value (in Euros)
Implied volatility of the FactSet Country Aggregate index
returns, determined by taking the 60 day trailing historical volatility multiplied by an implied volatility factor. The implied volatility factor is derived by computing the ratio of the VStoxx implied volatility level over the 60 day trailing volatility of the Euro Stoxx 50.
Global Insight’s three-year ahead Forecast Debt-to-GDP, reset on a yearly frequency
Average Life of the scheduled P&I on all outstanding Sovereign debt
Rate on the Euro benchmark with equivalent duration as above
Country dependent recovery that ranges from 35% for Greece to 85% for France

The Results

First we present the time series plots of the actual and model predicted spreads for the major¹ Euro-zone members.

Non-PIIGS:

PIIGS:

Overall, the model predicted spreads track actual spreads fairly well, although there is some divergence for some of the PIIGS in the last eight or so months, where the model predicted spread is too low. One explanation for this is that the implied volatility for the PIIGS is, in fact, higher than the proxy derived using the VStoxx. Below I show the time series of implied volatilities that matches the existing spreads.

Non-PIIGS:

PIIGS:

The calibrated volatilities match the implied volatilities for the Non-PIIGS better than the PIIGS. This is not surprising, since we use the VStoxx to help derive the country implied volalatility, which is clearly going to be a more accurate measure for the countries that have representation in that index. Note, in fact, that of the PIIGS, the two countries that have representation on the Euro Stoxx 50 (Italy and Spain) have calibrated volatilities that are closer to the VStoxx derived volatility than Greece, Ireland or Portugal. What is also of note is that the implied volatilities calibrated from the spread model are within the realm of plausibility, suggesting that the Contingent Claims Analysis approach has merit.

Now that we’ve looked at performance of the model on a country by country basis, let’s turn to measuring the joint predictive power. Let’s start by taking a look at the historical correlations.

Pair-wise spread correlations are fairly high among the 10 major Eurozone countries in general, but the PIIGS and non-PIIGS subgroups have higher correlations in-group than out-of-group.

Each country’s daily spread changes are negatively correlated with their FactSet country aggregate equity index returns, while being positively correlated with daily equity index volatility. In particular, spread/return and spread/vol correlations differ from zero more significantly amongst the PIIGS than the non-PIIGS.

Now the correlations amongst the actual country index returns, volatility changes, and model predicted spreads:

On the whole, the correlation structure is reasonably well preserved. The better the predicted spreads for a country match the actual, the closer the correlations match. What’s worth noting is that significant amount of the correlation structure is preserved even for the countries that are the least accurate. For corporate credits, I showed in prior posts that the VaR of a portfolio of investment grade credits was dominated by the default correlation and that significantly more yield could be achieved at the same risk through careful portfolio construction that gave full consideration to the correlations. Contingent Claims Analysis shows that the same holds for the Euro-Sovereigns.

¹ Germany and the Lilliputians (Cyprus, Estonia, Luxembourg, Malta, Slovakia, Slovenia) are not displayed. Germany effectively IS the Euro benchmark and the others do not have reliably liquid spread or equity index data.

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Vlog: What's the equivalent of idiosyncratic risk for fixed income?

Steve Greiner — 2011-01-12T19:01:04Z

Classic equity risk models include idiosyncratic risk. Steve Greiner explains what FactSet uses as an equivalent measure for fixed income.

Risk and Reward: Risk Budgeting and Cross-Asset Correlations

Dr. Laurence Wormald — 2011-01-06T19:00:51Z

Contributed by guest blogger Dr. Laurence Wormald, Head of Research at SunGard APT

It’s a truth universally acknowledged that a fund manager seeking to take effective control of investment risk across asset classes must be using or looking to use risk budgeting. However, it may be a lesser known fact that to implement risk budgeting successfully, the most important element is a proper estimation of cross-asset correlations.

There are those who allocate risk to country/region/asset class desks without managing their interaction. This group may be faced with regular surprises, as these interactions can either lead to risk increasing or risk reducing. There can be no expectation of reward without a proper estimate of the risk incurred in these interactions, and thus the portfolio is probably less efficient than it could be.

On the other side, there are those who budget properly to create a risk reduction via cross-asset-class correlations. This group can take more risk in the alpha generation process – a genuine benefit to the investment manager from diversification.

Being able to monitor actual risk expenditure against budget, via what is sometimes called “covariance accounting,” to break down tracking error or total volatility in terms of the cross-asset-class correlations is a vital investment capability that requires a robust underlying risk model.

The robustness of the correlation estimates in the multi-asset class models is ensured by using the same principal components methodology which SunGard uses for single-asset-class models. Principal components analysis is a powerful technique for separating signals from noise in any dynamic system, and that is what is required to robustly estimate the systematic correlations between assets within different classes (such as sovereign and corporate bonds, equities, and commodities). Historical data is always noisy, but by using principal components techniques, that noise can be effectively filtered out before estimating the systematic correlations.

However, as the last few years have shown, risk management is an art as well as a science. The art comes in choosing what we call the “Estimation Core,” that is the set of assets and macro factors used in estimating the APT components and in choosing the most appropriate number of principal component factors for each multi-asset class model.

Our first criterion for inclusion in the estimation core is that the historical data be fully validated, so that problems associated with stale pricing or missing returns are minimized. For a model based on weekly data, we require 180 weeks of scrubbed returns data for any asset which will be included in the estimation core.

SunGard APT’s research team uses extensive back testing in selecting the best-validated datasets for each asset class, including the most important macro series (selected from a set of approximately 30 macro series such as FX rates, key interest rates and credit spreads, equity, commodity, and volatility Indices) across all assets before extracting the principal components. In selecting, we look for significant explanatory power both in periods of normal market behavior and during market crises, considering crisis scenarios back to the 1990s.

Next, we separately choose estimation cores for each of the major asset classes (consistent with those for our single-asset-class models) before combining them in the multi-asset class estimation. In this way, corporate bonds, for example, inherit exposure to their issuers as well as to rates and credit spreads, while the cross-correlation of equities to commodities is simultaneously estimated with that to FX.

Finally, we test on random matrix theory to check that the principal components are in fact capturing the behavior of the systematic driving terms which drive cross-asset-class correlations. This approach makes the multi-asset class models robust enough for FactSet users to estimate a complete set of cross-asset correlations for use in risk reporting, risk budgeting, and optimized portfolio construction. For more information on optimized portfolio construction, view our recorded webinar on robust optimization.

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Taking Risk's Top Five Posts of 2010

2010-12-21T19:01:36Z

Our worldwide experts posted dozens of entries this year sharing their latest research and insights. These posts were the most popular of 2010:

Is it a small world after all?
Do regional models still matter in the global economy? A comparison of global models to specialized regional and local models.
The difference a daily risk model makes
What can daily calculation of factor returns, covariances, exposures and residual risks do for a risk practitioner?
The illusion of stability
Why a less volitile year is bad news for risk management.
How skillful a manager is Paul the Octopus?
Was the late Paul the Octopus really skilled in his World Cup predictions? If so, where does that rank him against some of the greatest fund managers?
Introducing Betamax: A new measure of covariance stationarity
Risk managers are recognizing the importance of correlation, but there have previously existed precious few quantitative tools around to address correlation risk.

Thanks for reading in 2010! Don't miss commentary from FactSet's top risk specialists in 2011. Subscribe by e-mail or RSS to receive the latest posts as they are published, and join in the conversation by posting your own comments and questions.

What area of risk management presents firms with their most acute challenges?

Chris Ellis — 2010-12-16T18:20:41Z

This week, I participated in a Risk and Compliance web seminar hosted by Waters Technology. Our panel attempted to tackle challenges faced by risk managers and compliance officers as they try to get as close to a real-time view of risk exposure as possible.

If you’d like to listen to the full presentation, you can access the archived recording here. During the discussion and later from clients, I was asked some noteworthy questions, and I want to share my views in this forum.

What areas of risk management currently present firms with their most acute challenges?

This question was presented to the Waters Technology audience, which answered “Getting a single, coherent view of exposure across business lines throughout the entire organization.”

With the release of Single Name Security Exposures analysis last week, FactSet is able to help investment managers directly address another key facet of this challenge.

Single Name Security Exposures lets you quantify and analyze your exposure to a given security or group of securities across all or a subset of portfolios, equity or fixed income, at a single point in time or across time. There are two parts to this. First, you define the universe. That universe could be a watch list, securities passing a formula, securities passing a screen (e.g., all Continental European microcap biotech stocks with a bond rating above or below a certain measure), or top N largest and smallest exposures. You can define the universe at a security, issuer, or ultimate issuer basis.

For part two, you define the group of portfolios. This could be the entire enterprise – all the portfolios in the organization. You could group by geographic location, such as everything in your London or Frankfurt or Tokyo office. Or you could choose portfolios of a certain mandate or portfolios managed by a particular manager or team. Really it’s any set of portfolios you want.

Once you’ve defined the universe and group of portfolios, you can think of security exposures as analyzing the intersection of a Venn diagram. Dissect the data in any form you like to examine exposures there.

What are the challenges facing financial institutions in developing a reliable cross-asset, firm-wide view of their risk exposure?

When you look at a cross-asset firm-wide view, the key piece of data that’s particularly challenging is getting the entity data to map up subsidiaries to parent companies and then to ultimate parent companies and then multiple asset classes. That gets complicated quickly when subsidiaries are issuing debt that’s ultimately the responsibility of the parent company and therefore linked to the risk of the parent. We’ve spent an enormous amount of time on this entity data data as part of our ever-growing content collection effort. That data is used in quite a few FactSet applications (particularly Portfolio Analysis), but Single Name Security Exposures really brings it to the surface as a nuanced application option.

What does the parent/child entity data cover?

Our entity mappings include equity, preferred stock, debt, equity options, and credit default swaps. You control on a per-report basis how broadly “parent” is defined. Decide: are you analyzing issues, issuers, or ultimate issuers?

Does the parent/child relationships data also account for ADRs and Ordinary Shares?

Yes. The parent/child relationship does match ADRs and parents. You can choose to match those only, include all share classes, all securities from immediate issuer, or all securities from ultimate parent. As an example, you can link PNC and National City debt or AES and debt issued by Indiana Power and Light Company (IPALCO).

Talking with our clients, we believe this type of cross-asset firm-wide exposures analysis will only become more important as compliance standards tighten. Contact me if you’d like to learn more or to request access to an in-depth web demo of the application. Or leave a comment below if you have a question I didn't cover.

You can also learn more about Single Name Exposures Analysis and try the product for yourself at our 2011 Investment Process Symposium. Register by December 15 to receive the early bird rate of $499.

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What’s your risk? Single name security exposure analysis

Chris Ellis — 2010-12-08T19:39:16Z

This Thursday, I'll be delivering a live webcast on FactSet’s new Single Name Security Exposures tool. Single Name Security Exposures lets you look across all portfolios or a subset of portfolios to quantify your exposure to a security, an issuer, an industry, a country, or a specific set of securities.

Simply put, Single Name Security Exposures tells you how much you own and where you own it. The analysis is straightforward, so it's easy to act upon. This isn’t a predicted standard deviation of excess returns, and there isn’t a theoretical dimension. The results are risk that everyone can easily understand.

Single Name Security Exposures clearly connects to the key news of the day. When a major market event happens, it isn’t clear when that event will be reflected in ex-ante risk numbers. Also, it isn’t necessarily clear what change will result from the event. With Single Name Security Exposures, the “when” and the “what” don’t have any ambiguity. It isn’t ex-ante. It isn’t ex-post (though you can run historical analysis). It focuses on right now.

The ongoing reporting aspect of Single Name Security Exposures revolves around compliance, while the ad hoc value relates to the news of the day or a big market event, including:

Country exposures like financial concerns in Ireland or political concerns in South Korea
Company exposures, such as outstanding results from AAPL
Industry exposures such as disappointing numbers of U.S. existing home sales
New statistics suggesting, for example, that the number of Americans with diabetes will spike in the next 30 years

As a risk manager or CIO, when you formulate your views on macro trends that will guide the market or as you try to act on the ideas from an investment committee meeting, you want to know your exposure to the companies that embody trends or your key insights. Single Name Security Exposures is a compelling complement to ex-ante risk analysis in that research.

Now, when we talk about this type of analysis, it is easy to cite the Greek debt crisis or Enron as great examples of why we need to consider these exposures. Similarly, when we talk about ex-ante risk analysis, it’s easy to focus in on excessive risk or high risk numbers as always bad. This clearly isn’t true. While the most glaring examples of why Single Name Security Exposure analysis is relevant tend to be negative, the analysis lends itself to positive or bullish investment ideas even more than ex-ante risk analysis.

When a company beats estimates or announces a break-through innovation (in technology or in medicine or in a consumer product), you want to understand your exposure. In the last few years, Apple is a great example of this. In the context of Single Name Security Exposures, it is the counter to Enron.

As another example, I mentioned wanting to understand your exposure to a significant increase in diabetes in the U.S. That’s bad news for society, but from an investment perspective, if it occurs, it is going to mean increased importance and profit for a group of companies.

To support this analysis, we combine portfolio holdings data clients store on our systems with comprehensive parent/child entity data. By having clear connections between parent companies, subsidiaries, and all related securities, you can examine exposure to an individual security or all securities related to an issuer, including common equity, preferreds, debt, equity options, and credit default swaps.

I hope you'll join me this Thursday, December 9 for a first look at Single Name Security Exposures. I'll also be taking your questions. The live webcast will take place at 2:00 p.m. EST/11:00 a.m. PST. Register here.

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Global risk models that aren’t skewed by asynchronous trading

Sebastian Ceria — 2010-11-22T20:01:33Z

by guest blogger Sebastian Ceria, Ph.D., President and CEO, Axioma, Inc.

In a recent abstract, Professor Bernd Scherer warned against the use of unadjusted daily stock market data to update global risk models, a practice he said results in “spuriously low correlations between stock markets.” As a provider of global, regional and multi-country risk models — models that are all updated daily — we couldn’t agree more.

Prof. Scherer correctly illustrates how the “use of daily accounting data would have underestimated the Value at Risk of an equal-weighted portfolio of G7 equity stock markets almost all the time. The use of unadjusted daily data becomes most troubling in periods of market crisis where underestimated correlations suggest a diversification benefit that is not real.

Prof. Scherer goes on to point out that this problem can only be overcome with “data synchronization models.”

Axioma has been acutely aware of this issue since it first began producing risk models, as all of its risk models are fully updated on a daily basis using daily closing return data. To address this issue, Axioma released in May 2010 a proprietary “returns-timing” adjustment methodology that specifically accounts for asynchronous trading between markets.

Most data synchronization models estimate a vector auto-regressive moving average (VARMA) model of returns. Axioma’s synchronization model uses a simple, first order vector auto-regressive (VAR) model. This model relies on just one day's lagged data (the first day is by far the most significant) and provides a robust estimate of the synchronized returns. To date, Axioma has shied away from using moving averages and higher order models as these are more difficult to estimate, require more modeling assumptions, and, in our experience, provide less robust results.

Axioma’s “returns-timing” model allows a number of important variables to be more accurately estimated in our risk models. First and foremost, it corrects the correlation underestimation between assets that trade at different times. Second, the model corrects the specific returns of ADRs and similar instruments whose underlyings trade at different hours than the ADRs. Third, the model allows returns to be decomposed into local market returns and global market returns. The global market returns can also be easily decomposed into industry and sector returns. This allows users to quantitatively assess whether a section of a market, such as banks, moving on one day in one part of the world moved in that same section of the market in a different part of the world later on the same day or on the next day. Finally, the model ensures that factor returns are synchronized, which improves ex-post attribution analyses and helps portfolio managers to understand the factors underlying performance.

For more, download Axioma’s research paper Returns-Timing: A Solution to Asynchronicity.

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Vlog: How does FactSet account for interest rate moves in its Value at Risk forecast?

Steve Greiner — 2010-11-22T19:34:12Z

Notes from the road: FactSet Balanced Risk hits Asia

Andrew Kovacs — 2010-11-22T19:34:12Z

It's been a busy month for us in Asia and Australia as we've been traveling extensively in the region to introduce the short-term balanced risk model FactSet recently developed with our partner R-Squared.

We developed the model this spring as, over the past several years, it has become apparent that there is a need in the market for a more complete risk offering for balanced fund managers. In our seminars in Tokyo, Singapore, Hong Kong, Sydney, and Melbourne, we covered four topics:

Willett Bird and I kicked things off with a brief introduction to FactSet’s fixed income portfolio analytics. In particular our presentation focused on how we generate derived analytics for a wide variety of fixed income instruments: an essential part of the process which ultimately allows us to produce risk analytics for the fixed income portion of a portfolio.
Jason MacQueen from R-Squared then gave an engaging presentation that first summarized the evolution of risk modeling and the various approaches that have arisen over time. Second, he detailed how, in building the short term equity risk model employed in FactSet balanced risk, R-Squared was able to incorporate many of the best elements of risk modeling developed over the years within the industry.
During the third presentation, Steve Greiner of FactSet got to the heart of the matter in a presentation describing the essential elements of FactSet’s solution for balanced risk. Steve provided details about the methodology used to produce the underlying model, how the model is used to compute Monte Carlo VaR and related statistics, and examples to illustrate the stability and robustness of the model.
In the final presentation of the day, Jason provided some real case studies to remind everyone that risk is a vital part of every investment process, if for no other reason than it allows portfolio managers to focus on their strengths and eliminate unintended bets from their portfolio construction process.

The events were a great way for us to hear directly from investment professionals on their needs in this area, answer questions, and share ideas. We're planning several future events that will include details on FactSet’s balanced risk offering, including the Investment Process Symposium in Miami next March. I hope we will see you at one such event.

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The U.S. elections one week out: What's in store for equity and fixed income markets?

Steve Greiner and Bill McCoy — 2010-11-22T19:34:13Z

There’s been sea of change in control in the Congress of the U.S. in this last election. Many in the media attribute it to a disenchantment with the White House’s treatment of Wall Street and corporate America. Other media believe that Obama tread on traditional American values. Still others point to the bank bailouts and egregious quantitative easing, the Fed money printing, and government spending without limits to the takeover in Congress of the Republican majority.

Since November 2008, the VIX, the CBOE Market Volatility Index, has fallen from 70 to today’s benign level near 20. The S&P 500 has rebounded dramatically from its March 2009 lows but still trails by 300 points its early 2007 highs. Gold, as measured by the SPDR Gold Trust (GLD), has traversed a straight line over the last five years in a single direction: up. This last measure of GLD portrays a dangerous direction for the economy in spite of the lower VIX and attests to the fear the world has for the U.S. dollar going forward.

The defacto reserve currency the U.S. dollar has been affords the United States two special interests. First, since the world essentially must buy the dollar -- for most global trade is conducted in dollars -- it allows the U.S. to borrow at cheap rates, relative to other countries, since our debt is in demand. Secondly, the U.S. can print its way out of financial straits, thus it can inflate its way out of debt conveniently if it has too much. Argentina, Greece, Portugal, Latvia, Spain, and Ireland are forced to acquire more stringent budgetary considerations to manufacture a solution to their debt problems, but the U.S. (whose debt is 94% of GDP at this writing) doesn’t have to, all due to the U.S. dollar as reserve currency. Unfortunately, it doesn’t mean our economy is healthier than Greece, it just means we can prolong paying the piper. The citizens of the world recognize this fact and are short the dollar and long gold mostly for this reason, albeit it’s becoming a crowded trade.

Meanwhile, large cap U.S. stocks may have increasing profitability due to their increasing percentages of revenue coming from the advancing emerging markets. In addition, the changeover in government means that Congress will be controlled by the Republicans, while Obama rules from the White House, resulting in gridlock on developing any new government programs. These two situations combine to allow some of the global fear of U.S. future policy to subside. This all portends for small to moderate gains in U.S. markets over the next year, while our burgeoning national debt and large unfunded liabilities (e.g., social security, government pension liabilities, and welfare) continue to grow unabated, leaving the longer term prospects for growth in the U.S. quite dismal.

Steve Greiner

For the U.S. fixed income markets, Tuesday’s election had a modest impact compared to the more significant resumption of Quantitative Easing II (QE2) or the stronger than expected employment numbers. Near term, fiscal policy is expected to be gridlocked, as neither party has a clear majority or mandate to enact legislation. Given that backdrop, the resuscitation of the economy falls on the only player with any hope of maneuver: the Federal Reserve.

However, the Federal Reserve can only do so much. As Chairman Bernanke has indicated, Washington must do its share of the work for fiscal leadership. There are several problems that will require Washington’s attention, and somehow the two parties will have to work together to solve them. Most importantly is the looming crisis in state and local government funding. California, Illinois, and several municipalities are struggling to plug holes in their budgets. Next up are the remaining specifics of financial reform. In addition, Congress must decide on the disposition of Fannie Mae and Freddie Mac. Finally, there is reaction of other countries, as America tries to inflate its way out of the current crisis. Such measures can only be successful as long as everyone is not doing it at the same time.

Bill McCoy

Coming soon: Systematic single-name exposure analysis

Chris Ellis — 2010-11-06T05:35:52Z

I’ve been quiet on the blogging front lately, but I wanted to share with you some of what we’ve been working on in FactSet product development. Over the last two months, I’ve visited 20 of our largest portfolio analytics clients in the U.S. to gather feedback on FactSet’s upcoming single-name exposures application (due to be released before the end of 2010).

This new application brings together four key pieces of FactSet, the first of which is client portfolio holdings data. Over 700 investment managers and plan sponsors use FactSet for performance attribution, characteristics analysis, and ex-ante predictive risk. To facilitate portfolio analytics, those 700 clients are loading over a million portfolios onto our system every night, which means we have a lot of data at hand that is integrated from accounting systems, custodians, and prime brokers. We have more than 50 custodian and prime broker position feeds that come into the system every night.

Second, that portfolio data is combined with the entity data. You can combine ADRs and GDRs with their parents, combine A shares with B shares, equity with corporate debt, or credit default swaps and options. Those are four key security types that get to the heart of issuer exposure.

By having all of that rich entity data about parents and children and securities, FactSet users can look at exposure to an individual security and all related equity securities or all the securities related to the issuer.

The third piece is pulling in screening, which allows you identify the universe of companies using a set number of criteria. When you are doing exposure analysis, sometimes you don’t want to look at an individual security but you want to look at the securities that fit a certain theme. That could be securities that have been downgraded by Moody’s or S&P in the last six months that are in a particular sector and region.

The fourth key piece is that we have wired all of those results into the same reporting and charting tool that we use for attribution analysis, so users can slice and dice the data and turn it into reports and charts that make it easier to digest and act upon the information.

Integrating these pieces has been relatively straightforward, but the devil is in the details. For example, when you define a parent/child relationship between a structured product and the issuer of that structured product, when should the structured product make the security link to the bank that created the security and when is it just an issuer? That gets into the detail of where there are guarantees or links between the issuer and the bond holder. For the next month or so, we’ll work to tie up loose ends and add functionality, such how we integrate and update history of the data for time series historical trend analysis.

In my visits this summer and fall, I was startled by how many very sophisticated clients were performing this analysis in an incredibly manual, non-systematic way. Let me know in the comments: will this single-name exposures application change your approach, or have you perfected this process? What’s your biggest challenge when doing this on your own? What would you want to see in a FactSet application?

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Why Default Correlation Matters (Part 2)

David Mieczkowski — 2010-11-06T02:28:30Z

In part 1 of this series, I highlighted how important default correlation is to credit risk management. In particular, I showed through some simple examples how default correlation dominates the risk calculus. I provided a simple link between default correlation and asset correlation using a simple one factor model. While this might seem highly stylized, it is worth pointing out that this setup is essentially the mechanism that is used to quote CDO tranches in terms of base correlation.

In this post, I’ll continue the discussion by showing how to pull information from the equity markets in order to estimate asset covariance. The solution for corporate bonds is twofold. First, we will apply a firm value or structural model of default (e.g., Merton Model) to link equity return correlation to asset return correlation, and asset return correlation to default correlation. Second, we employ a factor model structure on the equity returns to dimension reduce the problem.

The original and simplest of the firm value models is the Merton model. In the Merton model, firm asset value is modeled as a geometric Brownian motion, and firm equity is modeled as a European call option on the value of the assets, where the barrier (strike) is the notional K on the outstanding debt. Because assets equal debt plus equity, owning the debt is equivalent to being long the assets and short a call option on the same. Although this model is highly simplified in its assumptions about a firm’s capital structure, the appeal of the model is that it provides a direct tractable link, in the form of the Black-Scholes option pricing, between the debt, the assets, and the equity. Mathematically, the Merton model provides us with two non-linear equations which we can attempt to solve for the asset value and the asset volatility.

Where the A is the value of the assets, D the notional on the debt, E the value of the equity, C is the value of the European call option, and the s’s represent the volatility of the assets and equity respectively. Once we solve the system, we have the pieces needed to determine the probability of default P, and then by adding a recovery assumption, we can approximate the bond spread by

Tunring to factor models, we recall that in a factor model, the goal is to express the N individual returns in terms of a linear combination of common factors as,

Where,

The main benefit of this is that the covariance/correlation structure is highly simplified. If the factos are orthogonal (and we can usually perform a little technical magic called "factor rotation" to make this so) then the variance and covariance structure is simply given by, With this more robust framework I can now show you the effect of a correlation shift on credit VaR in a real world setting. To see this framework in action, we just need a portfolio of credits, and an equity factor model. For the sample portfolio, I chose the Bank of America Merrill Lynch U.S. Corporates Large Cap/Industrials (5-10 Y) (MLCIL6) index, which has a weighted average rating of A3. For the equity factor model, I did a simple principal component analysis (PCA) on the daily returns for the prior 250 days and retained the top 10 principal components. I use PCA for the factor model in my example because the principal components are already orthogonal (uncorrelated), hence it gives me a simple way to alter the correlation structure while preserving the individual variances within a Monte Carlo based simulation by using equations (6) and (7). This will let me show the effect on the 99% VaR of a correlation shift for a real life portfolio, while at the same time not altering any of the marginal probabilities of default.

The scree plot for this is shown below. Effectively the first 10 PCs explain roughly 58% of the daily variation in returns, with the first component accounting for more than 40% of the variation alone. It is worth pausing for a moment to compare this to the simple one factor asset value model presented in part 1. The scree plot indicates that such a simple model may not be terribly inaccurate.

To use the details of how to use the above framework to generate a VaR number is as follows:

Given the observable equity value, equity volatility, and outstanding firm debt, calculate the asset value and asset volatility by solving equations (1) and (2).
Calibrate the debt level to force the spread relation (3) to match the observed starting bond spread.
With the equity factor model in hand, run a bunch of simulated returns of the common and idiosyncratic factors to generate a bunch of equity return scenarios. Assume debt is unchanged, and then calculate asset value changes as A(sim) = A(base) + E(sim) – E(base).
Hold the asset volatility constant and use the asset value changes to compute new probabilities of default, and then through the spread relation (3), compute a spread change.
Use the simulated spread changes to compute the portfolio weighted average returns (due solely to spread moves), and calculate VaR.

To see the effect of an equity return correlation change on my real life portfolio of credits, I did two tests. First, I perturbed the issuer equity factor loadings, while preserving the % of variation due to idiosyncratic risk. In general, this spread the systematic risk out across the factors more evenly, and as a result, lowered the average pair-wise equity return correlations. As expected, the VaR is a function of the average correlation. The results of this are below. Recall that the equity factor model is using daily returns, so the analysis effectively shows the impact on 1-day VaR on the BofA Merrill Lynch U.S. Corporates Large Cap / Industrials (5-10 Y) (MLCIL6) index portfolio, in basis points.

Comparing the trend line to the single factor model discussed in part 1, the impact of increasing the correlation from .3 to .4 is roughly 10 bps, which represents an increase of 25% to the VaR. This compares to a 20% increase in VaR when going from a correlation of 0.3 to 0.4, for the simple single factor model.

The second test was to shift variance from the idiosyncratic component to the systematic components in a relatively uniform way. The graph confirms that shifting risk from uncorrelated idiosyncratic components to the systematic factors increases average correlation and the VaR. Slightly different than in the stylized single factor model, the magnitude of the effect declines with rising correlation. This is mostly due to the fact that the idiosyncratic exposures are heterogeneous in our real life portfolio.

Finally, to emphasize the point that correlation dominates, I examined the effect of holding the correlation structure constant, but simply increasing the individual risk neutral probabilities of default (achieved by raising the equity volatilities). The base case weighted average probability of default was roughly 4%. The 95%-VaR level for the base case was 44bps. There are a few really interesting aspects of the graph to note. First, there is a clear pivot point in the risk at about 9%. In a real life portfolio, the linear increase in marginal risk of the stylized example does break down at some point.

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FactSet's Taking Risk blog

Empirical durations: A tool to gauge mortgage price sensitivity

A retrospective on U.S. debts, and the logic of ceilings (Part 2)

A retrospective on U.S. debts, and the logic of ceilings (Part 1)

ETFs: The next bubble? (Part 2)

ETFs: The next bubble? (Part 1)

From News Corp to national crises, don't judge market volatility by the headlines

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What is the true size of China's monetary might?

Is the world running out of commodities?

Getting a picture of your portfolio holdings, from any angle

Defining "Quant" After the Credit Crisis

Three minutes on Ben Graham Was a Quant

The Value of Updating Risk Models Daily

Long-term Investment Approaches Cannot Ignore Short-term Volatility

Is Diversifying in Asia Dead?

Balanced Risk Explained

Our Most Anticipated Sessions at GARP's Annual Risk Management Convention

Capturing Euro-Sovereign Default Correlation through Contingent Claims Analysis

Vlog: What's the equivalent of idiosyncratic risk for fixed income?

Risk and Reward: Risk Budgeting and Cross-Asset Correlations

Taking Risk's Top Five Posts of 2010

What area of risk management presents firms with their most acute challenges?

What’s your risk? Single name security exposure analysis

Global risk models that aren’t skewed by asynchronous trading

Vlog: How does FactSet account for interest rate moves in its Value at Risk forecast?

Notes from the road: FactSet Balanced Risk hits Asia

The U.S. elections one week out: What's in store for equity and fixed income markets?

Coming soon: Systematic single-name exposure analysis

Why Default Correlation Matters (Part 2)

Long-term Investment Approaches Cannot Ignore
Short-term Volatility