The mean reversion in volatility that many of us have been looking for finally started to kick in last week. [2] Additionally, the VIX Premium Ratio [8], while slightly off its 52-week highs, still registers as extreme; historically, short term (30 day) implied volatility has tended to rise in such situations. My short-term S&P 500 implied volatility bias is positive, and will likely remain so over the coming weeks for seasonal reasons.

A rise in implied volatility does not necessarily entail a fall in stock prices. At the moment, S&P 500 price movement lacks a clear direction [4], and the index has been less volatile over the past month than had been assumed by option premiums. [6] That relationship has shown no sign of subsiding, and until it does, it may pay to be a net seller of options over the short term.

Short-term S&P 500 Implied Volatility Bias: Positive

Performance Comparison

• S&P 500: +4.34%
• Dow Jones Industrials: +3.28%
• Russell 2000: +7.75%
• S&P 500 Covered Call Fund: +2.88%
• Calendar Options: 0.00%
• Note: the period measured is from expiration to expiration.

We calculate Calendar Options returns based on a model portfolio allocation. We initially size each hypothetical position at 25% of the total portfolio value at the beginning of the cycle; with a maximum of three trades per month, this leaves at least 25% initially in cash for adjustments, if needed. Note that the model portfolio is not intended as a recommended allocation or as investment advice.

To see how the performance of our model portfolio since inception compares to a portfolio that’s 100% long the S&P 500, take a look at our Performance page. All performance figures include slippage (calculations are based on the actual prices at which the participating autotrading brokers were filled), but exclude any other transaction costs.

June Trades

• SPY June/July 85/94 Double-Calendar: 15.79% return – There’s not much to say about this trade, and that’s a good thing. Despite a significant decline in implied volatility, we booked our target 15+ percent return in three weeks.
• IBM June/July 100 Calendar Spread: –11.30% return – Unfortunately, the sanguine review above doesn’t apply to our June IBM trade. A combination of upward-trending price and declining IV kept this position under pressure right from the beginning, and its fate was sealed by a whipsaw top, which triggered a late-cycle adjustment that later worked against us. Nevertheless, our strategy performed as intended—we managed our risk to keep this one loss from exceeding the profit we generally anticipate (and in this case, actually realized in the same month) from one winning trade.

Our July positions are down about 2.5%, but with Thursday’s sell-off, we’re back on target to outperform in what’s increasingly looking like it could be a losing month for the market. More important, we’re entering our time window for August trades, and we’re poised to benefit from the tendency of implied volatility to bottom in July.

Coverage of this augury has included some decent analysis, and Michael Stokes posted an excellent series of studies on the MarketSci blog. Our contribution to quantifying the phenomenon focuses on its value as a short- to intermediate-term signal rather than as a long-term trading system. The table below shows the stats for the S&P 500 one week, one month, three months and six months after a bullish 50-day/200-day moving average crossover:

What’s stands out is that while there’s no significant edge in the very short term, on average it does pay to buy the Golden Cross if you book your profit in the one- to six-month timeframe. The percentage wins and average gain/loss ratio are particularly impressive after six months.

Lest one be lulled into complacency by these results, let us consider the period from 1928 through 1941. Based on a model of the S&P 500 for that time, five out of six 50/200-day crossover long entries ended up with losses six months later, with the largest loss (September 1939–March 1940) being more than 38%.

Despite the unusual severity of this economic downturn, it seems unlikely, given all the government intervention we’ve seen, that we’re going into another Great Depression— nevertheless, it might not hurt to accompany any long bets with a little protection, by using other, shorter-term indicators, stops or (relatively cheap, at the moment) puts.

–F.C.

This helps explain the phenomenon of vertical volatility skew.  In equity markets, the long-only bias resulting from the structure of mutual funds and other institutional factors means that investors are considerably more nervous, on any given day, about a potential 5% decline than they are fearful (or greedy) about the possibility of a 5% rally.  That uneven fear causes investors to overpay for put protection and creates a persistent “volatility smile” in which the implied volatility for deep out-of-the-money options will tend to be significantly higher than at- and in-the-money options in the same expiration cycle – with a volatility “smirk” occurring when skew is more exaggerated on the put side.  In commodities, expectations can differ dramatically, such that the smirk is tilted to the call side.

In “How Asymmetric is U.S. Stock Market Volatility?“, Ederington and Guan explore the asymmetry of volatility not by analyzing skewness, but by tracing the effects of equally large positive and negative return shocks on implied volatility, realized volatility, and models that attempt to predict volatility for asymmetric time series:

This paper explores differences in the impact of equally large positive and negative surprise return shocks in the aggregate U.S. stock market on:  1) the volatility predictions of asymmetric time series models, 2) implied volatility, and 3) realized volatility.  Both asymmetric time series models and implied volatility predict an increase in volatility following large negative surprise returns and ex post realized volatility normally rises as predicted.   However, while asymmetric time series models, such as the EGARCH and GJR models, predict an increase in volatility following a large positive return shocks (albeit a much smaller increase than following a negative shock of the same magnitude), both implied and realized volatility generally fall sharply. While asymmetric time-series models predict a decline in volatility following near-zero returns,
both implied and realized volatility are normally little changed from levels observed prior to the stable market.  Reasons for the differences are explored.

So the problem tackled here is that the stochastic models developed precisely to deal with the asymmetry of stock markets don’t seem to make correct predictions when it comes to these discrete events.  Which metric does the best job? The implied volatility read straight from option prices – the VIX, actually – appears to correspond to future realized volatility more closely in the types of situations under review.  Every approach gets the impact of large negative shocks correct: volatility rises sharply.  But following a large positive return, the time series models predict a small increase in volatility even though implied and realized volatility tend to decline afterward.  The authors offer an explanation that the GARCH models may tend to overweight extreme return observations.  They conclude that the adage that “volatile markets beget volatile markets” may not be exactly right: “Volatile markets do tend to follow bear markets but implied and realized volatility both tend to fall following bull markets.  Following stable markets (near-zero returns), implied and realized volatility are little changed from the levels observed prior to the near-zero return.”

Most traders we know aren’t relying heavily on GARCH or related models for volatility predictions anyway, but it is helpful to get some confirmation of the usefulness of options prices for estimating future volatility.  Microcosmically, then, options markets appear efficient in the weak sense (even if markets in general seem these days like a maniacally inefficient means for structuring society).

Performance Comparison

• S&P 500: 4.34%
• Dow Jones Industrials: 3.28%
• Russell 2000: 7.75%
• S&P 500 Covered Call Fund: 2.88%
• Condor Options VAMI: 5.72%
• Note: the period measured is from expiration to expiration.

Our Performance page compares the value-added monthly indexes of the Condor Options newsletter, the Credit Suisse/Tremont Equity Market Neutral Hedge Fund Index, and the S&P 500.  It includes slippage (the prices displayed in the trade list spreadsheet are the actual prices at which the participating autotrading brokers were filled), but excludes any other transaction costs.

June Iron Condors

• Our position entries throughout June were predicated on the simple principle of keeping our directional bias in check.  As the initial IWM trade came under pressure, we opened a SPY trade for June that considerably offset the existing delta bias. Mid-cycle, the market reversed back higher, so we entered a second SPY trade to further balance out our risk.  From there it was simply a question of letting time decay work its inexorable magic, and of peeling off trades as conditions dictated. While the bulk of the net 5.72% return came from our initial IWM position, it is important to note that meaningful returns like that are only possible within a context where risk is carefully managed throughout the expiration cycle. If you’d like more information about a subscription to our iron condors newsletter, click here.
• IWM 41/43/56/58: 5.23% return.
• SPY 78/80/95/97: -1.65% return.
• SPY#2 91/93/100/102: 2.13% return.

June Reading

Here are some posts from the past month that are worth checking out if you didn’t catch them the first time around:

• Technical Analysis Fails to Give You a Pony
• Testing the Vervoort Crossover Part 2
• How Meaningful is a VIX Below 30?
• Exponentially Curb Your Enthusiasm
• Iron Condors and Vertical Skew