Equity Options

Are strategies that exploit return autocorrelation good places to look for complementary (diversifying) return streams? In the March 2017 version of their paper entitled “Momentum and Covered Calls almost Everywhere”, Stephen Choi, Gil-Lyeol Jeong and Hogun Park examine trend following and covered call strategies at the asset class level both separately and in combination. Their asset class universe consists of three equity indexes, three bond indexes, three commodity indexes and one real estate investment trust (REIT) index. Their trend following (or time series momentum) strategy, which exploits positive autocorrelation of monthly index returns, is long (short) an index when its end-of-month level is above (below) its 12-month simple moving average. Their covered call strategy, which exploits negative autocorrelation (reversion) of index returns, is continuous, such as specified for the CBOE S&P 500 BuyWrite Index. They compare trend following and covered call strategies, separately and in combination, with buy-and-hold for single-class indexes and for multi-class portfolios of indexes. They consider three ways to construct multi-class portfolios (see “Tests of Strategic Allocations Based on Risk Metrics”): (1) maximum diversification (MDR), which maximizes the ratio of the sum of volatilities for individual assets divided by overall portfolio volatility; (2) equal risk contribution (ERC), a form of risk parity with adjustments for correlation; and, (3) equal weight (EW). They rebalance these portfolios quarterly, with volatility/correlation inputs for MDR and ERC based on a 3-year rolling window of historical data. They focus portfolio testing for only 10 years (2007-2016) based on availability of data for covered call indexes. Using the specified data as available from the end of 1971 through 2016, they find that: Keep Reading

Does option-implied correlation, a measure of the expected average correlation between a stock index and its components over a specified horizon, predict stock market behavior? In their January 2017 paper entitled “Option-Implied Correlations, Factor Models, and Market Risk”, Adrian Buss, Lorenzo Schoenleber and Grigory Vilkov examine option-implied correlation as a stock market return predictor. They consider expected average correlations between:

• Major U.S. stock indexes (S&P 500, S&P 100 and Dow Jones Industrial Average) and their respective component stocks.
• Major U.S. stock indexes the nine Select Sector SPDR exchange-traded funds (ETF).
• The nine Select Sector SPDR ETFs and their respective component stocks.

They calculate a correlation risk premium (CRP) as the implied average correlation minus realized average correlation measured over the past month, quarter or year. For comparison, they also calculate variance risk premium (VRP) as the difference between option-implied and realized return variances. Using daily returns for the specified indexes and ETFs (and component stocks of all) and for associated near-the-money options with 30, 91 and 365 days to maturity since January 1996 for S&P 500 and S&P 100 index, since October 1997 for DJIA and since mid-December 1998 for sector ETFs, all through August 2015, they find that: Keep Reading

“When to Sell Equity Index Put Options” summarizes research finding that the “insurance” premium from systematically selling equity index out-of-the-money (OTM) put options concentrates during the last few days before expiration. An ancillary finding is that a similar, though weaker and more volatile, pattern holds for selling at-the-month (ATM) put options. To test the general finding, we therefore look at the monthly return pattern for the CBOE S&P 500 PutWrite Index (PUT). PUT sells a sequence of one-month, fully collateralized (cash covered) ATM S&P 500 Index put options and holds these options to expiration (cash settlement). Per the referenced research, PUT gains should noticeably concentrate in the week before monthly option expiration. Using daily levels of PUT during mid-July 1986 through mid-February 2017, we find that: Keep Reading

Can speculators squeeze the “insurance” premium from shorting equity index put options in just the few days before expiration? In their January 2017 paper entitled “The Timing of Option Returns”, Adriano Tosi and Alexandre Ziegler investigate the timing of returns from shorting out-of-the-money (OTM) S&P 500 Index put options. Specifically, they compute daily excess returns (accruing return on cash for open short positions) for the two front contracts (“front-month” and “back-month”) up through expiration. They translate findings into strategies that open equally weighted short positions in the most liquid OTM puts a certain number of days before expiration and hold to the cash-settled expiration. They also consider delta-hedged positions via long S&P 500 Index futures. In most calculations, they account for market frictions by opening (closing) short positions at the bid (ask). Using daily data for S&P 500 Index levels, options and futures, and contemporaneous stock and option pricing model factors, as available during January 1996 through August 2015, they find that: Keep Reading

Do retail investors tend to underprice equity options in monthly series when the interval between expirations from third Friday to third Friday is five weeks instead of the more frequent (65% versus 35%) four weeks? In their November 2016 paper entitled “Inattention in the Options Market”, Assaf Eisdorfer, Ronnie Sadka and Alexei Zhdanov examine differences in U.S. equity option return behaviors for “months” with five weeks versus four weeks. They focus on stocks and exchange-traded funds (ETFs) with liquid options (relatively large size and high institutional ownership) and exclude options not expiring on the third Friday. Specifically, they each month on the third Friday form equally weighted portfolios of one-month-to-expiration, at-the-money long straddles (call and put with same strike price), delta-hedged calls and delta-hedged puts (both short the underlying stocks) and hold to maturity on the third Friday of the next month. They also run regressions of average weekly returns for these portfolios versus expiration interval and several control variables found in prior research to affect option returns (index option return, gap between implied and historical volatilities, return skewness and kurtosis, firm size, firm book-to-market ratio, past stock return and idiosyncratic volatility. Using daily returns (from closing bid-ask midpoints for options) for the specified options and underlying stocks/ETFs during 1996 through 2014, they find that: Keep Reading

Are overnight option returns consistently different from intraday returns? In their July 2016 paper entitled “Why Do Option Returns Change Sign from Day to Night?”, Dmitriy Muravyev and Xuechuan Ni decompose the negative risk premium of S&P 500 Index options into intraday (open-to-close) and overnight (close-to-open) components. They apply delta hedging to distinguish the options premium from movement in the underlying asset. For robustness tests, they also consider return decompositions for options on individual stocks and exchange-traded funds (ETF) and S&P 500 Implied Volatility Index (VIX) futures. Using intraday bid and ask prices for options and underlying assets during January 2004 through April 2013, they find that: Keep Reading

What are the principal strategies for exploiting the volatility and volatility skew risk premiums? In his May 2016 workshop presentation package entitled “Volatility Modelling and Trading”, Artur Sepp provides an overview of systematic volatility risk premium capture strategies. He focuses on simple rule-based strategies with monthly reformation suitable for an investable index or a proprietary strategy. He covers delta-hedged strategies for capturing the volatility/volatility skew risk premiums (straddles/strangles) and buy-write and put-write options strategies as applied to major stock indexes and liquid exchange-traded funds (ETF). He covers the following strategy elements:

1. Measuring realized volatility.
2. Forecasting expected volatility.
3. Measuring and forecasting implied and realized volatility skew.
4. Computing option delta.
5. Trading off transaction costs versus delta risk.
6. Managing tail risk.

Using relevant data for target assets during January 2005 through January 2016, he finds that: Keep Reading

Does trading of out-of-the-money (OTM) equity options expose exploitable private information? In their July 2016 paper entitled “Stock Return Predictability of Out-of-the-Money Option Trading”, Chang Mo Kang, Donghyun Kim and Geul Lee investigate relationships between OTM option trading volume and future returns of underlying stocks. They define OTM based on a range of option deltas and normalize OTM volume by dividing by total option volume for a stock. They first test the power of normalized OTM put and call volumes to predict stock returns at a daily frequency. They then test the power of the OTM put-call volume ratio (OTM put volume divided by all OTM option volume) to predict stock returns at daily and weekly frequencies. Finally, they test a hedge strategy that is each week long (short) the ranked tenth, or decile, of stocks with the lowest (highest) prior-week put-call volume ratios. Using daily and weekly data for relatively liquid near-term options and returns for underlying stocks during 1996 through 2015, they find that: Keep Reading

Does trading in stock index options exploitably predict the behavior of the stock market? In their June 2016 paper entitled “The Informational Role of Index Option Trading”, Tarun Chordia, Alexander Kurov and Avanidhar Subrahmanyam examine the relationship between U.S. equity index option net order imbalance and future S&P 500 Index returns. They calculate net order imbalance as the weekly difference between position-opening buy and position-opening sell contract volumes, divided by the total weekly position-opening volume. They calculate this net order imbalance separately for calls and puts across four equity indexes: ISE 250 Index, Russell 2000 Index, Nasdaq 100 Index and S&P MidCap 400 Index. For robustness tests, they compute analogous net order imbalances for E-mini S&P 500 futures contracts, individual NYSE stocks and SPDR S&P 500 (SPY). They also investigate interactions of equity index option net order imbalance with the S&P 500 Implied Volatility Index (VIX) and detrended weekly average values of the short-term interest rate (3-month U.S. Treasury bill yield), the term spread (difference between 10-year and 3-month U.S. Treasury yields) and the credit spread (difference between the Moody’s BAA and AAA yields). Using daily records of equity index option buys and sells (including whether each trade opens or closes a position) and other specified data during January 2006 through December 2013, they find that: Keep Reading

Do volume spikes in specific equity options signal abnormal returns for underlying stocks? In his June 2016 paper entitled “Investor Attention Strategy”, Xuewu Wang examines the motivation, construction and profitability of a strategy that selects stocks based on sudden attention to associated options. He defines sudden attention as volume spikes of at least 10 contracts after a week of no trades for equity options with minimum bid $0.10 and maturity no longer than 120 days. He further discriminates among option spikes by ranking them into thirds (terciles) based on either: (1) ratio of call volume to total volume; or, (2) call implied volatility minus put implied volatility (matched by strike price and maturity and averaged across all pairs for a given stock). He forms stock portfolios by buying stocks immediately after volume spikes of associated options and holding them for 30 calendar days. Using daily returns and associated option volumes for all U.S. stocks having options during January 1996 through  December 2013, he finds that: Keep Reading