Liberty Street Economics

The Private Premium in Public Bonds?

2012-05-16T07:00:00-04:00

Anna Kovner and Chenyang Wei

In a 2012 New York Fed study, Chenyang Wei and I find that interest rate spreads on publicly traded bonds issued by companies with privately traded equity are about 31 basis points higher on average than spreads on bonds issued by companies with publicly traded equity, even after controlling for risk and other factors. These differences are economically and statistically significant and they persist in the secondary market. We control for many factors associated with bond pricing, including risk, liquidity, and covenants. Although these controls account for some of the absolute pricing difference, the price wedge between public and private companies remains. Despite these pricing differences, private companies with public bonds are no more likely to go bankrupt or to be downgraded than are similar public companies. In this post, we briefly summarize the findings of our study.

Private Companies Pay Higher Interest Rates . . .
We begin by documenting a basic empirical regularity—private companies pay higher interest rates than public companies do when issuing public bonds. This is true even though the disclosure requirements are substantively similar (with the exception of the proxy report, companies with public bonds file similar documents with the Securities and Exchange Commission, including 10-Ks and 10-Qs).

. . . But They’re Riskier than Public Companies
But of course, there are lots of differences between public and private companies that would affect bond prices. Public companies are larger, and have much higher credit ratings, on average. Private companies have much more leverage. Public and private companies do have similar issuance patterns, so the difference isn’t the result of differences in average interest rates over time.

Controlling for Risk Factors, Private Companies Still Pay More
Since companies with public bonds are required to file public financial statements, we’re able to control for observable borrower characteristics and make sure that we appropriately control for differences in risk before concluding that there are real differences in the interest rates of bonds of private companies. Our empirical tests include controls for an array of proxies for credit risk, including rating, industry, leverage, and profitability. We also control for issuance quarter and differences in bond characteristics, such as maturity and putability or callability. While borrower and bond characteristics are associated with pricing, the average difference in bond spreads persists, suggesting that the difference isn’t due to observable characteristics. Although we can’t eliminate the possibility that differences exist in unobservable risk, estimates of the difference in bond spreads are actually higher when we compare the pricing of bonds of the same companies under different ownership structures (45 basis points) and when we use propensity matching techniques (45-56 basis points).

Private Companies Aren’t More Likely to Go Bankrupt
Another way to measure risk is to see if the bonds of companies with private equity are more likely to experience price declines or default. We find no evidence that ex post outcomes for bonds of private companies are worse than those of public companies. Private issuers are no more likely to file for bankruptcy or to be downgraded than are their public peers. Among firms with traded credit default swap (CDS) contracts, we don’t observe any significant difference between the CDS pricing of public and private firms. We also do not find evidence that private bonds perform worse after issuance, although the wedge between the pricing of public and private bonds persists in the secondary market.

Are Cash Flows of Private Companies Less Valuable?
After controlling for risk, what remains to account for the economically sizable pricing difference? Some of the spread differential is explained by differences in the value of equity for private issuers. We calculate a “hypothetical” equity value for all issuers based on earnings multiples of companies in the same industry. All else equal, the first billion dollars of hypothetical equity value lowers spreads of public companies by approximately 46 basis points but lowers spreads of private companies by only 42 basis points, almost 9 percent less. This may be because bond issuers don’t value private equity as much as public equity or because private companies aren’t as valuable.

Costs of Information Matter
We examine how much of the premium in bond prices that remains can be attributed to differences in costs of information. Proxies for opacity of the issuer’s assets, such as first bond offering, earnings variability, underwriter quality, split rating, and existence of CDS contracts, reduce the private premium by approximately 7 basis points (more than 25 percent). Results are inconclusive as to whether the penalty for opacity is different for private companies.

Conclusion
Are the high yields on public bonds of private companies an arbitrage opportunity in the making? Maybe. We find that some of the pricing difference is associated with information, so it may be more costly for bond investors to monitor private companies. The pricing difference would then be outweighed by the need to invest in information gathering. In addition, private bonds are more likely to be called; so if an investor prioritizes duration, this may make these bonds less desirable. Our findings suggest a sizable additional cost of being private, especially for companies that choose to be highly levered. These results have important implications for capital structure. While many theories suggest that the highest quality companies should issue risky debt, we find evidence that private issuers are generally riskier companies.

Disclaimer
The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

The Great Moderation, Forecast Uncertainty, and the Great Recession

2012-05-14T07:00:00-04:00

Ging Cee Ng* and Andrea Tambalotti

The Great Recession of 2007-09 was a dramatic macroeconomic event, marked by a severe contraction in economic activity and a significant fall in inflation. These developments surprised many economists, as documented in a recent post on this site. One factor cited for the failure to anticipate the magnitude of the Great Recession was a form of complacency affecting forecasters in the wake of the so-called Great Moderation. In this post, we attempt to quantify the role the Great Moderation played in making the Great Recession appear nearly impossible in the eyes of macroeconomists.

    In the twenty years that preceded the Great Recession, the U.S. economy had displayed remarkable stability—a phenomenon that James Stock of Harvard and Mark Watson of Princeton dubbed “the Great Moderation.” Most economists attributed this relatively sudden reduction in the volatility of macroeconomic variables, starting around 1984, to a combination of “good luck”—in the form of less severe external shocks hitting the economy—and “good policy,” especially in the form of greater Fed vigilance on inflation, as well as to other forms of structural change (see this 2004 speech by then Fed Governor Ben Bernanke for an overview).

    Our interest is to quantify the role that the Great Moderation—and the perception that it represented a permanent shift to more moderate business cycles—might have played in reducing the odds of the Great Recession in the minds of economists. To do so, we conduct an experiment using a fairly standard dynamic macroeconomic model of the type used by central banks around the world for forecasting and policy analysis (the details of the model can be found in a paper by Justiniano, Primiceri, and Tambalotti). We use this model—which we consider representative of mainstream macroeconomic thinking before the crisis—to forecast GDP growth, inflation, and the federal funds rate (the rate at which banks lend funds to each other overnight) over 2008-09 under two alternative scenarios.

    Scenario 1 assumes that “this time is different”—meaning that the Great Moderation permanently changed the structure of the U.S. economy and the nature of the shocks that buffet it. Therefore, the first set of forecasts is based on model estimates from the Great Moderation (fourth-quarter1984 to fourth-quarter 2007). Scenario 2 relies on the same parameter estimates to capture the dynamics of the economy, but uses data going back to 1954 to measure the volatility of the shocks. This forecasting approach is based on the idea that “we have been here before”: in other words, while the structure of the economy—and monetary policy in particular—might have changed following the mid-1980s, the overall level of uncertainty in the environment is better gauged by taking a long-term perspective.

    By comparing the likelihood of the macroeconomic outcomes associated with the Great Recession under these two forecasting strategies, we are able to quantify the extent to which the relative stability of the U.S. economy during the Great Moderation might have misled the model (and therefore the economists who relied on it) into concluding that an event such as the Great Recession was virtually impossible.

    The results of this exercise are depicted in the chart. The left panel corresponds to Scenario 1 (“this time is different”) and the right column to Scenario 2 (“we have been here before”). From top to bottom, the solid red line plots data for per capita GDP growth, inflation, and the federal funds rate, respectively, through fourth-quarter 2007, the peak of the expansion. This is the last quarter of data used in estimation, so the forecasts start in the first quarter of 2008, when the recession began. The solid blue line represents the model’s median forecast (that is, the forecast in the middle of all the model’s forecasts). The dark and light green areas represent 50 percent and 95 percent probability regions for the forecast, respectively. This means that, given the available data through the end of 2007, the model assessed a 1 in 2 chance that subsequent data would fall within the dark green regions, and a 19 in 20 chance that the data would fall within the light green regions. Wider probability regions therefore reflect a more uncertain forecast. Finally, the red line represents the data observed since 2008 (and through second-quarter 2010), which were not used when estimating the model and computing the forecast.

    Although the median forecast is similar in the two panels (and very far from the realized outcomes, especially for GDP growth and the funds rate), there is a significant difference in the uncertainty surrounding the forecasts. Under Scenario 1, “this time is different” (left panel), realized GDP growth lies outside the light green area for about a year and its trough is far below even the 5^th percentile of the forecast distribution. In other words, as of the end of 2007, the model predicts much less than a 1 in 20 chance of GDP growth falling as much as it did at the height of the Great Recession. It also assesses a similarly low chance of the federal funds rate hitting zero at the end of 2008, although it finds the observed fall in inflation to be less unlikely.

    In contrast, under Scenario 2, “we have been here before” (right panel), the higher forecast uncertainty—which reflects the more volatile shocks hitting the economy—increases the estimated chance of low-to-negative GDP growth during the Great Recession. In addition, the 95 percent probability regions under Scenario 2 encompass the observed developments in all variables during this period, although GDP growth and the funds rate are both at the lower edge of these regions, especially in 2009.

    In sum, our calculations suggest that the Great Recession was indeed entirely off the radar of a standard macroeconomic model estimated with data drawn exclusively from the Great Moderation. By contrast, the extreme events of 2008-09 are seen as far from impossible—if unlikely—by the same model when the shocks hitting the economy are gauged using data from a longer period (third-quarter 1954 to fourth-quarter 2007). These results provide a simple quantitative illustration of the extent to which the Great Moderation, and more specifically the assumption that the tranquil environment characterizing it was permanent, might have led economists to greatly underestimate the possibility of a Great Recession.

*Ging Cee Ng is an associate economist in the Research and Statistics Group.

Disclaimer
The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Just Released: The New York Fed Staff ForecastMay 2012

2012-05-11T10:30:00-04:00

Jonathan McCarthy, Richard Peach, and Simon Potter

We are presenting the New York Fed staff outlook for the U.S. economy to the New York Fed’s Economic Advisory Panel (EAP) at their meeting here today. It is an opportunity we take occasionally to get critical feedback from leading economists in academia and the private sector on the staff forecast; such feedback helps us evaluate the assumptions and reasoning underlying the forecast and the risks to it. Subjecting the staff forecast to such evaluation is important because it is an input assisting New York Fed President William Dudley in his preparation for the monetary policy decisions made at Federal Open Market Committee (FOMC) meetings. In a similar spirit of inviting feedback, we are sharing a short summary of the staff forecast in this post; for more detail, see the material from the EAP meeting on our website.

Staff Forecast Summary
Here we discuss the New York Fed staff forecast for real GDP growth, the unemployment rate, and Consumer Price Index (CPI) inflation in 2012 and 2013.

    The staff forecast anticipates moderate economic growth over the next two years. Real GDP growth in 2012 is expected to be around 2.5 percent, close to the average growth rate in 2011:Q4 and 2012:Q1. Although activity in those quarters probably was supported by some transitory factors, including the warm winter in many parts of the country and the rebuilding of motor vehicle inventories, the staff also expects some of the headwinds that have hampered growth in recent years to subside gradually, allowing improved fundamentals to become more apparent in the second half of the year. Fiscal policy is expected to exert a significant drag on GDP growth in 2013, but the expected further lessening of headwinds—especially as the labor market and financial conditions continue to heal—is projected to lead to growth of around 3 percent that year.

    With the overall economy improving moderately, the staff projects the unemployment rate to continue to fall over the next two years, averaging about 7.2 percent in the fourth quarter of 2013. Besides the expected path of real GDP growth and productivity growth (which we expect to return to near its long-term trend after weak growth over the past year), the staff forecast for the unemployment rate is influenced by the factors cited in the series of posts on the labor market in the Liberty Street Economics blog (as summarized in the concluding post).

    Overall CPI inflation, after being elevated in 2011, is expected to slow notably in 2012 to just over 2 percent, largely reflecting the movements in oil and gasoline prices and the expectations of those prices embedded in futures markets prices. In particular, the recent declines in gasoline prices at a time when they are typically rising suggest a fairly large decline in seasonally adjusted energy prices over the near term. In 2013, the staff expects CPI inflation to be modestly higher as inflation moves toward expectations and the FOMC’s longer-run goal for inflation. (Staff analysis indicates that its 2013 projection of CPI inflation is roughly equivalent to 2 percent inflation as measured by the personal consumption expenditures deflator, the measure followed by the FOMC.) The continued anchoring of longer-term inflation expectations and subdued compensation growth are consistent with inflation remaining near that objective.

Comparison to the Blue Chip Forecasts
Here, we compare the New York Fed staff forecast to the consensus forecast from the May Blue Chip Economic Indicators, which was published on May 10 for a survey conducted on May 2-3.

    The staff forecast for real GDP growth is somewhat above the Blue Chip consensus, which probably reflects the fact that the staff expects more receding of the headwinds and greater improvement in fundamentals than in the consensus forecast. For the unemployment rate, the staff forecast is notably below the Blue Chip consensus forecast and near the average of the bottom ten forecasts in that survey. The difference between the staff forecast and the consensus reflects the staff’s higher projected real GDP growth path and its assessment that there remains considerable slack in resource utilization. (The concluding post of our labor market series mentioned earlier provides some estimates of how low the unemployment rate could go.)

    Because the latest fall in oil prices occurred after the Blue Chip survey was conducted, the current staff inflation forecast is somewhat below the consensus forecast for 2012. The 2013 staff inflation forecast is slightly above the Blue Chip consensus, as the staff sees a stronger influence on inflation from anchored inflation expectations relative to resource slack than do many private forecasters.

Risks to Staff Forecast
Because modal forecasts are frequently “incorrect” in retrospect, an important part of the staff analysis of the economic outlook is the assessment of the risks to the forecast. In a very uncertain environment, the staff sees the balance of risks to real economic activity as being skewed to the downside. Reasons for this assessment include possible spillovers if the European sovereign debt crisis intensifies, the economic impact from larger-than-expected fiscal restraint, and geopolitical events leading to a renewed surge in oil prices. Because the latter possibility would push up inflation, the staff assesses the near-term inflation risks as roughly balanced, although the medium-term risks are skewed modestly to the downside. For another view on the risks, see the Survey of Professional Forecasters from the Philadelphia Fed (the latest was released today), which provides respondents’ probability assessments for real GDP growth, unemployment, and inflation. Of course, the New York Fed staff monitors developments to see if such risks are beginning to materialize and adjusts its forecast accordingly.

Disclaimer
The views expressed in this post, and in each of the presentations, are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

A Boost in Your Paycheck: How Are U.S. Workers Using the Payroll Tax Cut?

2012-05-09T07:00:00-04:00

Basit Zafar, Grant Graziani, and Wilbert van der Klaauw

Over the past several months, there was a flurry of debate in Washington over the extension of the payroll tax cut. Many supporters of the tax cut—worth about $1,000 to a family earning the median income of slightly more than $50,000 a year—have cited its importance to the nation’s economic recovery, while opponents claim that it will only add to the national deficit without boosting the economy. Exactly how such a tax cut affects the aggregate economy relies heavily on how U.S. workers use the extra funds in their paychecks. Unfortunately, we know little about how such tax cuts are used by workers. So we decided to ask them and, in this post, report the answers they gave us.

    The initial payroll tax cut, passed into law as part of the 2010 Tax Relief Act, reduced workers’ Social Security tax withholding rate from 6.2 percent to 4.2 percent for all of 2011. After much legislative debate, the 2 percent payroll tax cut for nearly 160 million U.S. workers was extended in December 2011 for the first two months of 2012, and then again on February 22, 2012, for the rest of the year. In order to understand how these cuts might affect economic activity, we used the RAND Corporation’s American Life Panel (ALP) to conduct online surveys of 372 individuals, 200 of whom were working, at two points last year: in February 2011, and then in mid-December 2011, close to the expiration of the initial tax cuts.

    In the first survey, we asked respondents how they intended to spend any extra funds from the payroll tax cut in their paychecks. More precisely, respondents were asked to provide the share (out of 100 percent) of funds that they would spend on: consuming, saving, and paying off debt. The table below shows that 8.8 percent of respondents planned to use most of the tax-cut funds for consumption, 39.8 percent planned to use majority of it on saving, and 50.3 percent planned to use a majority of it to pay off debt. Such a low intended rate of consumption is consistent with the permanent income hypothesis, which claims that transitory changes in income should not change consumption behavior, as individuals would use the extra funds to smooth their consumption over the rest of their lifetime.

    To explore the relationship between the perceived permanence of the tax cuts and the intention to spend the funds, we also asked respondents how likely they thought it was that the tax-cut extensions would continue into future years. The table below shows how intended consumption patterns relate to the perceived likelihood of future tax-cut extensions. On average, those who consider long-term extensions to be likely plan to spend about 8 percentage points more of their tax-cut funds than those who consider them to be unlikely (20.5 percent compared with 12.6 percent). Additionally, the last three columns of the table show that when comparing the two groups, a higher proportion of the “likely” group intend to use the majority of their tax-cut funds for consumption (17.9 percent compared with 8.1 percent). This supports the permanent income hypothesis, as those who consider these tax cuts to be more permanent plan to spend more of the extra funds.

    In the second survey, conducted in December 2011, respondents were asked how they had used the extra funds from the tax cut over the past year. The second column in the first table shows that 35.0 percent of individuals actually spent the majority of their tax-cut funds, a sharp increase from the intended use of 8.8 percent. We next compare individuals’ intended use of the tax-cut funds (reported in the early 2011 survey) with what they reported actually doing with the funds. The figure below shows the relative frequency of actual tax-cut-fund use by the three groups based on intended use (that is, mostly consume, mostly save, and mostly pay off debt). For example, of those who planned to spend most of their tax-cut funds, 66.7 percent did in fact spend the majority of it, while 16.7 percent saved the majority of it, and 16.7 percent paid off debt. A similarly high proportion of those who intended to use the majority of their funds to pay off debts did so (60.8 percent), while 46.2 percent of those who planned to save most of their funds actually did so. Two patterns are of note in the chart: (i) while there is a positive correlation between intended and actual uses, there is a high degree of inconsistency; and (ii) there is a systematic shift toward spending for those who did not use their funds in the way they intended, that is, individuals ended up spending more of their tax-cut funds than they had intended.

    We can also go beyond grouping respondents by how they used the majority of their funds. In particular, we can directly estimate the marginal propensity to consume (MPC) by taking the average of the proportion of each person’s tax-cut funds that was reported to be used on spending. This, of course, can also be done for saving and paying off debt. This value is far more useful for policymakers when considering how the total tax cut will be used by households. The chart below summarizes the average breakdown of tax-cut-fund use for the full sample and by demographic groups. On average, respondents used 40.5 percent of their tax-cut funds on spending (that is, an MPC of 0.405), 24.1 percent on saving, and 35.3 percent on paying off debt. These figures are quite different from the intended average proportions reported in the first survey, in which, on average, respondents intended to use 16.3 percent of their tax-cut funds on spending (intended MPC of 0.163), 34.2 percent on saving, and 49.5 percent on paying off debt. That is, on average, individuals ended up spending a significantly larger part of the tax-cut funds than they had intended.

    The figure also shows that the MPC varies quite a bit across demographic groups. Most notably, those with annual incomes of more than $75,000 tend to spend a slightly larger fraction than lower-income respondents (MPC of 0.44 compared with 0.37; difference not statistically significant), while those with lower incomes tend to save less and to pay off debt more than higher-income respondents (differences significant). This may seem contrary to the popular notion that tax cuts benefit those with lower incomes—who tend to be more liquidity constrained—by providing them with much needed spending money; earlier studies have in fact found that lower-income households spent a higher fraction of their 2001 and 2008 tax rebates. However, data collected from other surveys we conducted indicate that lower-income households, relative to higher-income households, experienced more credit tightening (they had significantly more involuntary credit card closures over the 2008-10 period), had greater nonmortgage debt during 2010 (median nonmortgage debt-to-income ratio of 16.7 percent compared with 8.2 percent), and perceived greater employment uncertainty during 2010. These results suggest that lower-income households are closer to the margin of being delinquent and further credit-constrained, and hence it should not be surprising that—despite being more liquidity constrained—they are no more likely to spend their tax-cut funds than higher-income households. The higher debt and tighter credit availability for the lower-income respondents also explain why they use more of their funds to pay off debt than the higher-income group (42.8 percent compared with 27.9 percent).

    As shown in the chart above, it is also notable that men tend to consume more than women, and that women use far more of their tax-cut funds to pay off debt than men do. Similarly, college graduates spend more and pay off debt less than non-college graduates.

    Our estimated MPC of 0.405 is at the higher end of the range of estimates from the literature based on recent tax rebates: in examining the use of the 2008 tax rebates, one study estimates an MPC of 0.33, and another an MPC of about 12 to 30 percent in the first three months from receiving the rebate; similarly, the MPC in the first three months after the 2001 tax rebates has been estimated in the 20 to 40 percent range. One possible explanation for why we observe a higher MPC out of tax-cut funds than out of tax rebates is that tax cuts show up in smaller amounts spread over multiple paychecks, which many people claim not to notice. These smaller, multiple payments may be more easily spent than large, lump-sum tax rebates.

    Three of our findings are noteworthy. One, our larger MPC estimates highlight the importance of the design of tax holidays (rebates or cuts) in determining the response of spending to policies. Second, our finding—that people who perceive tax cuts to be more permanent plan to spend more of their funds—has fiscal policy implications as to whether such tax cuts are implemented as long-term extensions or sequential short-term extensions. Third, we find that people spend a large portion of their tax-cut funds to pay off debts—this may be good news considering the large debt issues leading up to and during the financial crisis—and may also suggest that our estimated MPC is an underestimate because by facilitating deleveraging, it can indirectly lead to higher future spending through a reduction in future interest payments.

Disclaimer
The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Basit Zafar is an economist in the Microeconomic Studies Function of the New York Fed's Research and Statistics Group.

Grant Graziani is a research associate in the Regional Analysis Function of the New York Fed's Research and Statistics Group.

Wilbert van der Klaauw is a vice president in the Microeconomic Studies Function of the New York Fed's Research and Statistics Group.

The Flash Crash, Two Years On

2012-05-07T07:00:00-04:00

Adam Biesenbach* and Marco Cipriani

On May 6, 2010, several U.S.-based equity products underwent an extraordinary price decline and recovery, all within less than half an hour. The Dow Jones Industrial Average (DJIA) and other equity indices dropped by more than 5 percent in a matter of minutes, only to rebound as quickly. Individual equity securities experienced similar, if not larger, swings in prices, both up and down. This post describes what happened on that fateful day, and summarizes the findings of the academic literature on this topic.

What Caused the “Flash Crash”?
May 6 opened as a volatile day of trading in U.S. markets. In the hours before the crash, bad news about the European debt crisis increased price volatility and led to a broad decline in equity prices. As noted in the U.S. Commodity Futures Trading Commission (CFTC) and U.S. Securities and Exchange Committee’s (SEC) joint report on the crash, against the backdrop of increased uncertainty and negative market sentiment, at 2:32 p.m. EST an unusually large automated sell order was initiated in the S&P 500 E-mini market, a futures contract traded on the Chicago Mercantile Exchange. Normally, such a large order would not be executed all at once, but rather would be spread out, possibly over several hours. However, on May 6, the sell order was executed with unusual rapidity through an automatic algorithm that took into account only trading volume, disregarding prices.

    The selling pressure was initially absorbed by intermediaries in the E-mini market, and in particular by High Frequency Traders (HFTs). HFTs are market participants that use high-speed, sophisticated computer algorithms to submit large numbers of trades on a daily basis. In general, HFTs do not hold their positions long enough to take directional bets. Instead, they participate on both sides of the market, maximizing turnover of their inventories in order to profit from the bid-ask spread. That is, they provide liquidity to the market, similar to traditional market makers.

    After temporarily building a long position in the E-mini market, HFTs themselves began aggressively selling E-mini contracts in order to reduce their exposure. As a result of the combined selling pressure from the selling algorithm and HFTs, the price of the E-mini contract began to rapidly decline. As other traders withdrew from the market, HFTs began rapidly buying and selling contracts from one another in an effort to control their inventory levels, generating a sharp increase in volume. The selling algorithm responded to this spike in volume by increasing the rate at which it fed new orders to the market, thus exacerbating the precipitous decline in prices.

    At approximately 2:45 p.m., trading in the E-mini contract was paused for five seconds by the Chicago Mercantile Exchange because of the extraordinary decrease in its price. During the pause, selling pressure in the E-mini contract was relieved, and buy orders started arriving in the market. Just after trading resumed, prices in the E-mini market stabilized.

    At around the same time, because of the uncertainty surrounding the price collapse in the E-mini market, liquidity providers (traditional market makers and HFTs) withdrew from several individual equity markets and equity Exchange Traded Funds (ETFs) in order to assess the risk of continuing trading activity and the reliability of their data. This reaction caused liquidity to drop suddenly and prices to swing dramatically. In many markets, liquidity evaporated to the point that trades were executed at prices as low as a penny and as high as $100,000 (at the so-called “stub quotes,” quotes posted by market makers far away from current prices to fulfill their obligation to always quote a bid and ask price). As liquidity providers validated the integrity of their data and trading systems, they reentered the market. By approximately 3:00 p.m., securities that were severely disrupted during the crash converged back to their pre-crash levels.

Trying to Make Sense of the Crash
The “flash crash” has been the subject of several studies that try to understand both the cause of the sudden collapse in prices on May 6, and how similar events can be predicted (and avoided) in the future.

    Using transaction level data in the E-mini market during May 2-6, 2010, Kirilenko et al. (2011) study the behavior of HFTs during the crash. As we mentioned, HFTs act similarly to traditional market makers and when market conditions are normal, they provide liquidity to the market. In contrast to traditional market makers, however, HFTs trade aggressively when prices are about to change in an effort to keep their inventories down. This behavior was apparent on May 6, when the speed of their trading algorithms allowed them to unwind their inventories quickly before the price decline of the E-mini contract accelerated. As a result, they aggressively drained liquidity from the market, amplifying selling pressures on the E-mini market. The authors conclude that “although HFTs did not trigger the ‘flash crash,’ their responses to the unusually large selling pressure exacerbated market volatility.” The increase in trading aggressiveness on May 6 is also documented by Chakravarty et al. (2011), who show a sharp increase in the use of Intermarket Sweeping Orders, a particular type of order that allows a trader to “sweep” liquidity across all market centers simultaneously.

    According to Easley et al. (2010), liquidity providers (traditional intermediaries and HFTs) suddenly exited the market on May 6 because they believed they were trading at a loss with better informed parties. The authors call this phenomenon “order flow toxicity”: order flow (that is, the buy and sell orders arriving in the market) is considered toxic when intermediaries providing liquidity trade at a loss with market participants who are more informed. Easley and coauthors develop a measure of toxicity suited to markets in which HFTs comprise a large fraction of the trading volume, and trading horizons are extremely short. This measure, which they label the Volume-Synchronized Probability of Informed Trading (VPIN), captures the degree of order flow toxicity by estimating the volume imbalance for any given period of trading activity. Volume imbalance is a proxy for toxicity since informed traders mostly trade on one side of the market only. By their calculations, the VPIN measured on the S&P 500 E-mini was unusually high the day of the flash crash, and reached its highest level ever at 2:30 p.m., just before the crash. Because of the high toxicity of the order flow, HFTs and traditional intermediaries left the market, causing the sharp contraction in liquidity and decline in prices.

    Madhavan (2011) suggests that the extreme price volatility during the flash crash is partially the result of market fragmentation (that is, the fact that in today’s economy trading activity is dispersed across different trading venues). Because of fragmented trading activity, market depth in each trading venue is lower, increasing market fragility. Madhavan measures fragmentation through the Herfindahl-Hirshman Index (HHI), an index often used in the industrial organization literature, computed as the sum of the square of the share of each trading venue. The index equals one if all activity is concentrated in one venue, and decreases as market fragmentation increases. Madhavan finds that securities experiencing greater market fragmentation in the month before the flash crash were disproportionately affected during the crash, and that the level of fragmentation was higher on May 6 than in the previous twenty days of trading. Moreover, the level of market fragmentation has increased dramatically over the last ten years, raising the prospect that flash crash episodes may occur again.

    The studies summarized above help us understand swings in prices such as those observed two years ago. Since then, regulators have mandated changes in market operations that should make the occurrence of crashes less likely (see, for instance, the NASDAQ OMX Group, Inc.’s list of current regulatory initiatives). Particularly important is the adoption of circuit-breakers at the individual security level, the establishment of risk controls when brokers provide customers with market access, and the prohibition of markets makers’ stub quotes. The challenge facing researchers and policy makers is to understand how such changes will affect the behavior of market participants, whether they will be enough to reduce financial markets fragility, and which additional policy measures, if any, are still needed.

*Adam Biesenbach is an assistant economist in the Federal Reserve Bank of New York’s Research and Statistics Group.

Disclaimer
The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Historical Echoes: Pneumatic Tubes and Banking

2012-05-04T07:00:00-04:00

Amy Farber, New York Fed Research Library

Pneumatic tubes—a system in which cylinder-shaped containers (that could contain messages, money, small objects, and even food) are propelled through a network of tubes via compressed air or partial vacuum—are a relatively old technology. (Pneumatic tubes were patented in the United States in 1940, with earlier forms existing prior to this date). But when used in innovative ways in the past, they were viewed as futuristic. What may come to mind first is the use of pneumatic tubes in George Orwell’s 1984 (1949) to transport messages and newspapers. The 1954 film of the book depicts the use of the technology (starting three minutes into this clip).

In Bill Bryson’s homage to growing up in the 1950s, The Life and Times of the Thunderbolt Kid: A Memoir, he describes the use of pneumatic tubes in a department store:

Every commercial enterprise had something distinctive to commend it. The New Utica department store downtown had pneumatic tubes rising from each cash register. The cash from your purchase was placed in a cylinder, then inserted in the tubes and fired—like a torpedo—to a central collection point, such was the urgency to get the money counted and back into the economy. A visit to the New Utica was like a trip to a future century.

What does this have to do with banks? According to Kent Mustoe, an engineering student and member of a group called the Pneumanics (a team of six students in the L05 section of ECE4007, Georgia Tech's senior design course for electrical engineers), banking is one of the industries with the longest running use of this technology. He states in his paper, “Pneumatic Transport System Uses and Advances”:

The banking industry is one of the longest running users of a pneumatic transport system. Their method of transportation consists of a single input, single output tube system that connects the bank teller from inside the bank to the customer at an outside terminal . . . This is one of the most practical uses of a pneumatic system due to the fact that the distance between the two end ports is so short. Also the material of the objects being moved is usually paper, which is light weight . . .

Many youtube.com videos about pneumatic tubes in drive-thru banking feature cars crashing into bank structures, but there is also a short video that features a Canadian woman who is delighted to use the bank drive-through.

According to an article in Adweek in 2010, Citadel Bank was running ads featuring a pneumatic tube. However, instead of cash, the tube carried rate announcements and customer inquiries, which were received and read by a nerdy spokesman named Lewis.

Disclaimer
The views expressed in this post are those of the author and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the author.

Euro Area Spending Imbalances and the Sovereign Debt Crisis

2012-05-02T07:00:00-04:00

Matthew Higgins and Thomas Klitgaard

Euro area periphery countries borrowed heavily from abroad in the run-up to the sovereign debt crisis. How were these funds used? In this post, we recap our recent Current Issues study, showing that pre-crisis borrowing by the periphery countries (Greece, Ireland, Portugal, and Spain) went mainly to finance private consumption or housing booms rather than productivity-enhancing investments. Most analysis of the crisis has focused on the need for fiscal adjustment in the periphery. A look at the drivers of the run-up in foreign borrowing, however, suggests that private spending in the periphery will also need to move to a lower plane. The fact that debts were built up without adding to these countries’ productive capacity is likely to make the needed adjustment in spending all the more difficult.

    A country’s current account balance is equal to the difference between domestic saving and domestic investment spending. A country that saves more than is needed to support domestic capital expenditures sends the surplus abroad to purchase foreign assets. Conversely, a country where domestic saving is insufficient to finance domestic capital expenditures must borrow from abroad to make up the shortfall. After all, a country that saves more than it invests also produces more than it consumes. Through cross-border lending and borrowing, countries with surpluses build up a store of foreign wealth, while countries with deficits are able to maintain higher levels of consumption and investment spending than would be possible without foreign financing.

    In 2007, during the run-up to the global financial crisis, Greece, Portugal, and Spain borrowed amounts equivalent to 10 to 15 percent of GDP, the largest in the euro area (chart below). Ireland’s borrowing, though smaller, was still a substantial 5 percent of GDP. Now, these countries face pressure to bring spending in line with income.

    Whether a current account deficit develops because of higher investment or reduced saving matters for assessing risks to growth and financial stability. Foreign borrowing to finance productive investment projects raises national income and should result in a surplus over debt service costs. Foreign borrowing undertaken because of lower levels of saving, in contrast, supports current consumption while building up a debt burden on future income. The composition of investment can also matter. For example, foreign borrowing to support investment in nontradable sectors such as housing generates no foreign income stream to support repayment.

    The saving-investment mix in the euro area periphery countries was not healthy from a growth and stability perspective in the period leading up to the global recession. Consider first developments on the saving side. Greece and Portugal saw large drops in domestic saving (chart below). In Greece, saving fell to just 11 percent of GDP in 2007; in Portugal, it fell to just 13 percent. More detailed national accounts data from the Organisation for Economic Co-operation and Development show that Greece’s decline in saving was mostly attributable to lower private saving but also to larger government budget deficits. The decline in Portugal was entirely attributable to lower private saving. Spain showed a modest decline in its saving rate up to 2007, with lower private saving outweighing the impact of improved government fiscal balances in the period leading up to the recession. Ireland displayed a relatively stable saving rate, with little change on either the private or the government side. Germany, in contrast, saw a marked increase in its saving rate, from 21 percent of GDP in 1999 to 27 percent by 2007, mostly driven by higher private saving.

    The divergence in saving trends is mirrored in consumption trends, as euro area periphery households responded to lower interest rates by borrowing and spending. Real consumption spending in Ireland and Greece increased roughly 55 percent from 1999 to 2007 (chart below). In Spain, the corresponding figure was around 35 percent. Again, Germany stands at the other extreme. Consumption remained essentially flat after 2001, leaving the country with ample funds to lend abroad. Similarly, household liabilities ballooned in the periphery countries, far outpacing growth in disposable income, while liabilities declined relative to disposable income in Germany. These divergent consumer spending trends were a key driver of euro area imbalances.

    Now consider developments on the investment side. Investment spending as a share of GDP declined slightly in Greece leading up to the crisis, while the investment share in Portugal was flat (chart below). In contrast, investment spending as a share of GDP saw a marked rise in Spain and Ireland. But higher investment shares in these two countries reflected booms in residential construction—both countries saw enormous house-price bubbles—rather than spending on business plants and equipment.

    In sum, heavy foreign borrowing offset low saving rates in Greece and Portugal, while in Spain and Ireland it was used to fuel a housing boom. But the outcome for all four countries was the same: they accumulated foreign debt but did not use these funds to build up the higher productive capacity that would enable them to repay or service the debt.

    Reduced foreign borrowing in the euro area periphery countries will require higher domestic saving relative to domestic investment spending. One set of adjustment policies focuses on increasing national saving through fiscal austerity measures. Needless to say, cutbacks in government spending or hikes in taxes put downward pressure on output and incomes. The advantage of fiscal policy is that tightening measures are simple to identify, even if painful to implement. The drawback is that austerity measures can be self-defeating by choking off growth and undermining the economy’s tax base (see recent analysis in the International Monetary Fund’s World Economic Outlook).

    Private sector behavior will also play an important role in the future evolution of periphery countries’ saving-investment balances. Household saving rates typically rise during downturns, with consumers turning cautious amid increased uncertainty. And indeed, household saving rates rose substantially after 2007 in Ireland, Portugal, and Spain, the three periphery countries for which we have data. Business saving rates, in contrast, typically fall, reflecting weakness in corporate profits during downturns. However, business investment generally declines at least as sharply, so that the private saving-investment balance improves and thereby reduces external borrowing. In line with this pattern, Ireland, Portugal, and Spain saw business investment other than housing fall by more than 20 percent in real terms from 2007 to 2011. The rub, of course, is that lower business investment spending is the least attractive way of responding to reduced foreign financing, given the link between business investment and future productivity.

    The key question is how the periphery countries will adjust going forward. Foreign borrowing remains sizeable outside Ireland, although current account deficits in 2011 were well below pre-crisis peaks. The positive shift in current account balances in all four countries reflects lower saving rates alongside larger declines in investment spending, with residential as well as business investment weakening. Notably, sizeable foreign borrowing has been maintained despite the drying up of private investment inflows; indeed, once-substantial inflows have given way to liquidation of existing positions. Ongoing current account deficits in the periphery have been financed instead by official creditors, including adjustment assistance by the International Monetary Fund and European Union, and the automatic extension of central bank credit from surplus countries in the euro area via the region’s banking payments system (see our recent Liberty Street blog post for details). (Liquidation of periphery countries’ own external assets has also played a role.) However, the ability or willingness of the official creditors to provide additional funding surely has limits. With the loss of access to foreign capital, periphery countries now face the difficult task of bringing private and public spending back in line with domestic incomes.

Disclaimer
The views expressed in this blog are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York, or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

The Impact of Trade Reporting on the Interest Rate Derivatives Market

2012-04-30T07:00:00-04:00

Michael Fleming, John Jackson*, Ada Li*, Asani Sarkar, and Patricia Zobel*

In recent years, regulators in the United States and abroad have begun to strengthen regulations governing over-the-counter (OTC) derivatives trading, driven by concerns over the decentralized and opaque nature of current trading practices. For example, the Dodd-Frank Act will require U.S.-based market participants to publicly report details of their interest rate derivatives (IRD) trades shortly after those transactions have been executed. Based on an analysis of new and detailed data on the trading activity of major dealers, this post discusses the possible costs and benefits of reporting requirements on the IRD market. In a previous post, we examined the same question for the credit default swap (CDS) market.

Interest Rate Derivatives Defined
An interest rate derivative is an agreement to exchange payments based on different interest rates over a specified period of time. IRD products enable interest rate risks to be hedged, and are thus valuable tools for allowing both financial and industrial firms to manage their risk exposures. For example, the owner of a bond who receives 5 percent annual interest in U.S. dollars could reduce interest rate risk by entering into an interest rate swap (IRS) denominated in U.S. dollars that obliged him to make fixed interest payments that precisely offset those expected on the bond, and entitled him to receive variable payments based on the prevailing market rate of interest at the time each payment was made.

Costs and Benefits of Trade Reporting Rules
The introduction of mandatory trade reporting rules may have costs and benefits for market participants. The vast majority of IRD products are currently traded in the OTC markets where, because of the markets’ decentralized nature, information on traded prices is not widely available. Providing price information may be valuable to investors because it could make it easier for them to evaluate the competitiveness of a price quote. It could also help risk managers more accurately measure the value of their positions. Prices can change rapidly in these markets, so these benefits increase with the timeliness of the price information.

    The risk of introducing reporting rules is that, if details of a large trade by a dealer, say, are rapidly made public, participants on the sideline may anticipate the dealer seeking to offset its position and may execute trades to profit from such knowledge. As a consequence of such “front-running,” dealers would be obliged to hedge at worse prices. This risk of being front-run might in turn make dealers reluctant to provide liquidity for large trades or more inclined to widen bid-ask spreads to reflect the increased cost of hedging.

Trading Frequency of Interest Rate Derivatives Is Low and Variable
The frequency of trading in most IRD instruments is low, suggesting that the benefits of price reporting may be limited by the scarcity of activity. Our analysis, which covers approximately 70 percent of all transactions in the global IRD market between June and August 2010, identifies only 2,500 transactions per day spread across eight derivative products and twenty-eight currencies.

    At the same time, there is considerable variation in trading frequencies across different products and currencies, so the quantity and timeliness of reported data will vary substantially. The most active product, IRS, accounts for over 1,900 transactions per day, while the least active product, interest rate options known as “Caps/Floors,” account for an average of eleven transactions per day. While 78 percent of all transactions take place in the four most active currencies (U.S. dollar, euro, yen, and sterling), there are several other currencies with less than five transactions per day across all product types.

Product Standardization Is High in Some Features and Low in Others
We find evidence that some important contractual terms are standardized in the IRD market. For example, all U.S. dollar, euro, and sterling IRS transactions reference a single floating rate index in each currency for calculating payments, and a high percentage of transactions have the same frequency of payments. Such standardization likely increases the benefits of transparency because it means that reported prices are more comparable over time for similar IRD transactions.

    By contrast, the maturity of IRD contracts varies considerably across transactions. In the IRS market, 57 percent of all activity in the four most active currencies (or 855 transactions per day) is concentrated in the five most commonly traded maturities (that is, two, three, five, ten, and thirty years), but the remaining 43 percent of transactions (or 645 trades per day) are dispersed over numerous different maturities. In the most active maturities, reported data is likely to be sufficiently timely to benefit customers, but for the less actively traded maturities, the benefit may be more limited.

Participation Is Low but Active Customers Have Many Dealer Counterparts
Liquidity in the OTC IRD market is typically provided by dealer firms, which act as market makers by quoting prices and facilitating transactions for their customers. Our data indicate that in the most active sectors of the IRD market the majority of participants have trading relationships with several major dealers. For example, moderately active participants (such as those who trade IRS between two and five times per day on average) trade with ten dealers over the sample period. Only forty-three participants, most of whom are relatively inactive traders, transact with just a single dealer. The opportunity to trade with multiple dealers enhances the ability of participants to compare prices prior to trading, and may limit the additional benefits of post-trade price transparency.

Strong Evidence of Dealer Hedging
In order to make markets and provide liquidity to customers continuously, dealers need to manage the interest rate risk they take on when trading with customers. We find strong evidence in the case of IRS denominated in the most active currencies that dealers are able to offset large risk exposures promptly—in about thirty minutes. Our results mitigate the concern that introducing price reporting would hinder dealers’ ability to hedge their risks, at least for actively traded IRS products.

Implications for Public Reporting
Our findings suggest that the benefits from trade reporting rules are likely to accrue dissimilarly to different segments of the IRD market. The benefits are likely to be limited for segments of the IRD market that have low levels of trading activity and where activity is dispersed across a wide range of products, currencies, and contract maturities. In contrast, trade reporting rules are likely to provide greater benefit to participants in the relatively active segments of the IRD market. For these market segments, timely trade information may broaden the market as firms that currently have limited access to dealers are encouraged to participate. We also show that dealers offset their risks quickly in these more active market segments, alleviating concerns about the potential negative impact on liquidity of reporting requirements.

*Patricia Zobel is an assistant vice president in the Markets Group and Ada Li a senior bank examiner in the Financial Institution Supervision Group (FISG) of the Federal Reserve Bank of New York; John Jackson is on secondment to FISG from the Bank of England.

Disclaimer
The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Forecasting the Great Recession: DSGE vs. Blue Chip

2012-04-16T07:02:00-04:00

Marco Del Negro, Daniel Herbst,* and Frank Schorfheide

Dynamic stochastic general equilibrium (DSGE) models have been trashed, bashed, and abused during the Great Recession and after. One of the many reasons for the bashing was the models’ alleged inability to forecast the recession itself. Oddly enough, there’s little evidence on the forecasting performance of DSGE models during this turbulent period. In the paper “DSGE Model-Based Forecasting,” prepared for Elsevier’s Handbook of Economic Forecasting, two of us (Del Negro and Schorfheide), with the help of the third (Herbst), provide some of this evidence. This post shares some of our results.

    We find that it really matters what information you feed into your model: Feed in the right information, and even a dingy DSGE model may not do so poorly at forecasting the recession. We also compare how the models perform relative to the “Blue Chip Economic Consensus” forecasts. The answer is: About the same, if not better, in fall 2007, in summer 2008, before the Lehman crisis, and at the beginning of 2009–provided one incorporates up-to-date financial data into the DSGE model. (By the way, if you don’t know what a DSGE model is, check out Wikipedia or this primer.)

    The chart below shows DSGE model and Blue Chip forecasts for output growth obtained at three junctures of the crisis (the dates coincide with Blue Chip forecast releases): October 10, 2007, right after the turmoil in the financial markets had begun in August of that year; July 10, 2008, not long before the default of Lehman Brothers; and January 10, 2009, at the apex of the crisis. Specifically, each panel shows the current real GDP growth vintage (solid black line), the DSGE model’s mean forecasts (red line), and bands of the forecast distribution (shaded blue areas; these are the 50, 60, 70, 80, and 90 percent bands for the forecast distribution, in decreasing shade), the Blue Chip forecasts (green diamonds), and the actual realizations according to the May 2011 vintage (dashed black line). All the numbers are in percent, quarter-over-quarter.

    When interpreting these results, bear in mind that the information available to the DSGE econometrician consists only of the data used for estimation that are available at the time of the forecasts (these data are real GDP growth; inflation; the federal funds rate; total hours worked; growth in real wages, investment, and consumption; and long-run inflation expectations–all at a quarterly frequency). This implies two things. First, the estimation is done in a “real-time” context (as opposed to using revised data–yes, the GDP growth and other data are revised all the time, so the numbers you read about in the paper often end up being quite different from the final numbers). Second, the DSGE econometrician has only lagged information on the state of the economy. For instance, on January 10, 2009, she would only have the information contained in 2008:Q3 data. The information set used by Blue Chip forecasters contains the same data, but also includes a plethora of current indicators on the quarter that just ended (namely, 2008:Q4), information from financial markets, and all the qualitative information available from the media, speeches by government officials, etc.

    The chart shows the forecasts for three different DSGE specifications. We call the first one SWπ; this is essentially the popular Smets-Wouters model. The second is the Smets-Wouters model with financial frictions, as in Bernanke et al. and Christiano et al., which we call SWπ-FF. The other difference between this model and SWπ is the use of observations on the Baa-ten-year Treasury rate spread, which captures distress in financial markets.

    The last specification is still the SWπ-FF, except that we use observations for the federal funds rate and spreads for the quarter that just ended and for which no National Income and Product Accounts data are yet available (for the January 10, 2009, forecasts, these would be the 2008:Q4 average fed funds rate and spreads). Of course, this information was also available to Blue Chip forecasters. We refer to this specification as SWπ-FF-Current.

    The October 10, 2007, Blue Chip forecasts for output were relatively upbeat, at or above .5 percent quarter-over-quarter (that is, 2 percent annualized). The SWπ forecasts were more subdued: The model’s mean forecasts are for growth barely above zero in 2008:Q1, with some probability of negative growth in 2008–that is, a recession. The forecasts for the two SWπ-FF specifications were in line with those of the SWπ model, although a bit more subdued. SWπ-FF-Current in particular assigns a likelihood of negative growth that’s above 25 percent. These models capture the slowdown in the economy that occurred in late 2007 and early 2008, but of course miss the post-Lehman episode. The decline in real GDP that occurred in 2008:Q4 is far in the tails of the forecast distribution for the SWπ model, but less so for the two SWπ-FF specifications.

    In July 2008, the Blue Chip forecast and the mean forecast for the SWπ model were roughly aligned. Both foresaw a weak economy–but not a recession–in 2008, and a rebound in 2009. The two SWπ-FF specifications were less sanguine: Their forecast for 2008 was only slightly more pessimistic than the Blue Chip’s for 2008; but, unlike the Blue Chip, these models did not foresee a strong rebound in the economy in 2009. Both models failed to grasp what was coming in 2008:Q4, but at least they put enough probability on negative outcomes that the economy’s strikingly negative growth rate in 2008:Q4 is almost within the 90 percent bands of their forecast distributions.

    By January 2009, conditions had worsened dramatically: Lehman Brothers had filed for bankruptcy a few months earlier (September 15, 2008), stock prices had fallen, financial markets were in disarray, and various current indicators had provided evidence that real activity was tumbling. None of this information was available to the SWπ model, which on January 10, 2009, would have used data up to 2008:Q3. Not surprisingly, the model is, so to say, in “la-la land” concerning the path of the economy in 2008:Q4 and after. Unlike the SWπ model, the SWπ-FF specification does not forecast a rebound, but at the same time does not foresee the steep decline in growth that occurred in 2008:Q4. The SWπ-FF uses spreads as an observable, but since the Lehman bankruptcy occurred only later in Q3, it had minor effects on the average Baa-ten-year Treasury rate spread for the quarter, and therefore the SWπ-FF has little indirect information on the turmoil in the financial markets.

    The forecasts of the SWπ-FF-Current specification, which uses 2008:Q4 observations on spreads and the fed funds rate, are a completely different story. The model produces about the same forecast as the Blue Chip for 2008:Q4. Considering that the agents in the laboratory DSGE economy had not seen Federal Reserve Chairman Bernanke and Treasury Secretary Paulson on TV painting a dramatically bleak picture of the U.S. economy–which the Blue Chip forecasters likely did see–we regard this as a small achievement. But the main lesson may be that structural models can actually produce decent forecasts as long as you’re using appropriate information.

*Daniel Herbst is an assistant economist at the Federal Reserve Bank of New York; Frank Schorfheide is a professor of economics at the University of Pennsylvania.

Disclaimer
The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Just Released: The Federal Reserve in the 21st Century 2012 Symposium

2012-04-16T07:00:00-04:00

Eric Tucker*

The Federal Reserve in the 21st Century (Fed 21) symposium on monetary policy and financial stability recently brought together over 225 college professors from around the region and the world. The annual two-day event gives professors who teach economics, finance, or business the opportunity to hear presentations from top Federal Reserve Bank of New York economists and senior staff and ask them questions. Fed 21 is part of the Bank’s broader efforts to increase public understanding of the Federal Reserve System’s role in conducting monetary policy and ensuring financial stability.

The first day, Monday, March 19, was devoted to new issues in U.S. monetary policy. The second day, Tuesday, March 20, focused on financial stability policy. The second day also featured a webcast of Federal Reserve Chairman Ben Bernanke speaking live to a class at George Washington University on the Federal Reserve and the financial crisis.

The presentations, which include mostly unedited videos of the lecture and slides from each presenter, are available for viewing on our website. Lectures that are accompanied by a question-and-answer session at the end of a presentation are indicated with an asterisk (*). To best capture the presentation experience, you can download the PDF file of the slides and view them simultaneously with the recording of the lecture.

*Eric Tucker is a director in the Communications Group.

Disclaimer
The views expressed in this post, and in each of the presentations, are those of the author(s) and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the author.