Equity Options

Does accounting for realistic trading frictions support beliefs that equity index out-of-the money (OTM) put options and at-the-money (ATM) straddles are systematically overpriced? In their October 2018 paper entitled “Index Option Anomalies: How Real Are They?”, Michal Czerwonko and Stylianos Perrakis re-examine assumptions and data used in several high-profile studies finding that OTM put options and ATM straddles for the S&P 500 Index are overpriced, and that shorting these positions is therefore reliably profitable. They focus on the following aspects of option pricing: accounting for realistic trading frictions (bid-ask spreads); differences in pricing of same-strike price puts and calls; and, inconsistency in pricing across maturities. Using groomed intraday prices and quotes for S&P 500 Index (cash-settled) options 28, 14, and seven days to maturity during January 1990 through February 2013 (278 settlement dates), they find that:

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Under what conditions should speculators buy protective equity options when they expect realized stock market volatility to increase? In their September 2018 paper entitled “Being Right is Not Enough: Buying Options to Bet on Higher Realized Volatility”, Roni Israelov and Harsha Tummala analyze the relationship between: (1) long volatility return (delta-hedged options) and same-interval changes in realized volatility; and, (2) the volatility risk premium (VRP, implied volatility minus realized volatility) and same-interval changes in realized volatility. They specify long volatility as a portfolio of cash-settled equity index options, reformed monthly, that:

• On each options expiration date, buys one-third of a -25 delta put option, one-third of a +25 delta call option and one-sixth each of at-the-money put and call options. All options initially have about a month to expiration.
• Each day until expiration, hedges option deltas via equity index futures. 
• Holds the options to expiration.

They also examine sensitivity of outcome to different portfolio initiation and termination points relative to significant volatility increases. They focus on the S&P 500 Index, using VIX as implied volatility and hedging via S&P 500 Index futures, during January 1996 through December 2016. They also consider for robustness testing corresponding data for Eurostoxx 50, FTSE 100 and Nikkei 225. Using daily data as specified, they find that:

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Can investors refine portfolio rebalancing while capturing a volatility risk premium (VRP) by systematically shorting options matched to target allocations of the underlying asset? In their October 2017 paper entitled “An Alternative Option to Portfolio Rebalancing”, Roni Israelov and Harsha Tummala explore multi-asset class portfolio rebalancing via an option selling overlay. The overlay sells out-of-the-money options such that, if stocks rise (fall), counterparties exercise call (put) options and the portfolio must sell (buy) shares. They intend their approach to counter short-term momentum exposure between rebalancings (when the portfolio is overweight winners and underweight losers) with short-term reversal exposure inherent in short options. For testing, they assume: (1) a simple 60%-40% stocks-bonds portfolio; (2) bond returns are small compared to stock returns (so only the stock allocation requires rebalancing); and, (3) option settlement via share transfer, as for SPDR S&P 500 (SPY) as the stock/option positions. They each month sell nearest out-of-the-money S&P 500 Index  call and put options across multiple economically priced strikes and update the overlay intramonth if new economically priced strikes become available. Once sold, they hold the options to expiration. Using daily S&P 500 Total Return Index returns, Barclays US Aggregate Bond Index returns and closing bid/ask quotes for S&P 500 Index options equity options (with returns calculated in excess of the risk-free rate) during 1996 through 2015, they find that:

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Do aggregate positions in put and call options on individual stocks, as indicators of sentiment of informed traders, predict future market returns? In their July 2017 paper entitled “Stock Return Predictability: Consider Your Open Options”, Farhang Farazmand and Andre de Souza examine the power of average value-weighted put option open interest divided by average value-weighted call option open interest in individual U.S. stocks (PC-OI) to predict U.S. stock market returns. Specifically, they:

• Compute for each stock each day total put option open interest and total call option open interest.
• Average daily values for each stock by month and weight by market capitalization.
• Calculate PC-OI by dividing the sum of monthly capitalization-weighted average put option open interest by the sum of monthly capitalization-weighted call option open interest.
• Each month, relate via regression monthly PC-OI to stock market return the next three months to determine the sign of the future return coefficient.
• Each month, create a net signal from the sum of the signs of these coefficients from the last three monthly regressions. A positive (negative) sum indicates a long (short) position in the stock market and an offsetting short (long) position in the risk-free asset.

They further test whether PC-OI predictive power concentrates in stocks with unique informativeness as represented by high idiosyncratic volatility (individual stock return volatility unexplained via regression versus market returns). For comparison, they also test their model with S&P 500 index options. Using daily open interest for options on AMEX, NYSE and NASDAQ common stocks and on the S&P 500 Index with moneyness 0.8-1.2 and maturities 30-90 days, associated stock characteristics, and contemporaneous U.S. stock market returns during January 1996 through August 2014, they find that:

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How well do stock index covered call strategies work across markets worldwide? In their June 2017 paper entitled “Covering the World: Global Evidence on Covered Calls”, Roni Israelov, Matthew Klein and Harsha Tummala test covered call strategies for 11 global equity indexes. They measure overall returns and return contributions from equity exposure, short volatility exposure and equity timing. They also test a risk-managed covered call strategy that sells at-the-money covered calls with hedging of estimated dynamic equity exposure deviations from 0.5 (from an option pricing model) using index futures. Using call options data for the 11 equity indexes as available (all by January 2006) through September 2015, along with associated index values and futures returns, they find that: Keep Reading

Which short index options offer the best overall performance? In their June 2017 paper entitled “Which Index Options Should You Sell?”, Roni Israelov and Harsha Tummala explore return and risk properties of short delta-hedged out-of-the-money S&P 500 Index put and call options of various moneyness and maturities. They consider moneyness of -2.5 to +1.0 standard deviations relative to the forward index price. They consider maturities of one, two, three, six and 12 months. They assume daily delta-hedge rebalancing with S&P 500 Index futures to isolate volatility and time effects. They calculate average returns and estimate alphas and betas relative to S&P 500 Index returns. They then calculate three beta-adjusted risk metrics for the returns: (1) volatility; (2) stress-test losses (specified for a 20% one-day adverse S&P 500 Index move as on October 19, 1987); and, (3) 0.1% value at risk (VAR), which approximately translates to a once-in-four-years worst loss. Using daily data for S&P 500 Index options with standard monthly expiration dates (3rd Friday of the month) and for the index itself during late March 1996 through December 2015, they find that:

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Is the conventional wisdom that equity index put options are effective tail risk hedges for a stock portfolio correct? In his March 2017 paper entitled “Pathetic Protection: The Elusive Benefits of Protective Puts”, Roni Israelov compares the hedging properties of put protection strategies with those of daily rebalanced stocks-cash (divested) portfolios that generate the same compound annualized return in excess of cash. He considers put protection portfolios based on: (1) the CBOE S&P 500 5% Put Protection Index (PPUT), which systematically purchases monthly put options that are 5% out of the money; and, (2) Monte Carlo simulations with and without a volatility risk premium (difference between implied and realized volatilities). For simulations, he assumes compound annualized equity return 4% with 20% annualized volatility, zero risk-free rate and dividend yield and monthly purchases of 5% out-of-the-money put options held to expiration. For simulations with a volatility risk premium, he assumes annualized implied volatility 22%. Using monthly PPUT and S&P 500 Total Return Index (SPTR) returns during July 1986 through mid-May 2016, he finds that: Keep Reading

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