Equity Options

Overall, how do retail option traders perform compared to institutional counterparts, and what accounts for any performance difference? In their June 2021 paper entitled “Who Profits From Trading Options?”, Jianfeng Hu, Antonia Kirilova, Seongkyu Park and Doojin Ryu use account-level transaction data to examine trading styles and profitability by investor category for KOSPI 200 index options and futures. There are no restrictions in Korean derivatives markets on retail investor participation, and retail participation is high. Using anonymized account-level (153,835 domestic retail, 5,904 domestic institutional, 667 foreign institutional and 604 foreign retail) data for all KOSPI 200 index options and futures trades during January 2010 through June 2014, they find that:

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What is the latest evidence on attractiveness of selling covered calls or buying protective puts on the U.S. stock market? In his February 2021 paper entitled “Revisiting Covered Calls and Protective Puts: A Tale of Two Strategies”, Bryan Foltice examines raw and risk-adjusted returns from systematically selling covered calls and buying protective puts on SPDR S&P 500 ETF Trust (SPY) as a proxy for the stock market. Specifically, at the beginning of each month, he sells fully hedged calls or buys fully hedged puts on SPY with one month to expiration. He estimates option prices via the Black-Scholes option pricing model, using the CBOE VIX Index for SPY volatility and accounting for dividends. He considers a range of option moneyness, ranging from 5% out-of-the-money (OTM) to 5% in-the-money (ITM) in 1% increments. Using monthly SPY price, VIX level and risk-free rate during March 1993 through September 2020, he finds that:

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Do options of individual stocks exhibit momentum and seasonality patterns? In their November 2020 paper entitled “Momentum, Reversal, and Seasonality in Option Returns”, Christopher Jones, Mehdi Khorram and Haitao Mo investigate momentum and seasonality effects for options on U.S. common stocks. They focus on performance of straddles, combining a put and a call with the same strike price and expiration date. They balance needs for liquidity and sample size by requiring positive open interest during the holding period but not the momentum calculation interval. Specifically, on each monthly option expiration date, they:

1. Form two straddles from near-the-money options expiring next month for each for each stock: (1) the pair with call delta closest to 0.5 for calculating momentum; and, (2) the pair with call delta closest to 0.5 and with positive open interest for both the put and the call when selected for calculating momentum portfolio return.
2. Construct from these pairs zero-delta straddles using bid-ask midpoints as prices and calculate monthly straddle excess returns relative to the 1-month Treasury bill yield. This process generates about 1,600 straddles per month with average monthly excess return -5.6% and very large standard deviations.
3. Calculate momentum as average monthly excess return over a specified lookback interval (rather than cumulative return, to suppress effects of return outliers).
4. Rank straddle returns into equal-weighted fifths (quintiles) based on momentum and calculate average return for each quintile and for a portfolio that is long the top quintile and short the bottom quintile.

Using end-of-day open interest and bid-ask quotes for call and put options on U.S. common stocks from OptionMetric and trading data for underlying stocks during January 1996 through June 2019, they find that: Keep Reading

Does nominal stock price matter for associated option returns? In their May 2020 paper entitled “Cheap Options Are Expensive”, Assaf Eisdorfer, Amit Goyal and Alexei Zhdanov evaluate option returns across the range of U.S. stock prices. For each stock each month, they:

• Pick a single call option and a single put option closest to at-the-money and expiring the third Friday of the month, excluding those with zero open interest or zero trading volume to ensure liquidity.
• Sort options into tenths (deciles) based on underlying stock price.
• Construct equal-weighted delta-hedged call and put portfolios and hold to option expiration, computing returns from option quote midpoints (zero effective bid-ask spread).

Using price and characteristics data for 257,107 call options and 204,123 put options on a broad sample of U.S. common stocks during 1996 through 2017, they find that:

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How do market makers and sophisticated investors/traders determine option value? In his July 2019 essay entitled “Trading Volatility”, Emanuel Derman outlines the history and shortcomings of option valuation as described by the Black-Scholes model, which estimates the value of an option on an asset as a function of the asset’s price and volatility. He also addresses extensions of this model. Based on mathematical derivations and his knowledge of option markets, he concludes that:

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What steps should investors consider to mitigate impact of inevitable large U.S. stock market corrections? In their May 2019 paper entitled “The Best of Strategies for the Worst of Times: Can Portfolios be Crisis Proofed?”, Campbell Harvey, Edward Hoyle, Sandy Rattray, Matthew Sargaison, Dan Taylor and Otto Van Hemert compare performances of an array of defensive strategies with focus on the eight worst drawdowns (deeper than -15%) and three NBER recessions during 1985 through 2018, including:

1. Rolling near S&P 500 Index put options, measured via the CBOE S&P 500 PutWrite Index.
2. Credit protection portfolio that is each day long (short) beta-adjusted returns of duration-matched U.S. Treasury futures (BofAML US Corp Master Total Return Index), scaled retrospectively to 10% full-sample volatility.
3. 10-year U.S. Treasury notes (T-notes).
4. Gold futures.
5. Multi-class time-series (intrinsic or absolute) momentum portfolios applied to 50 futures contract series and reformed monthly, with:
   • Momentum measured for 1-month, 3-month and 12-month lookback intervals.
   • Risk adjustment by dividing momentum score by the standard deviation of security returns.
   • Risk allocations of 25% to currencies, 25% to equity indexes, 25% to bonds and 8.3% to each of agricultural products, energies and metals. Within each group, markets have equal risk allocations.
   • Overall scaling retrospectively to 10% full-sample volatility.
   • With or without long equity positions.
6. Beta-neutral factor portfolios that are each day long (short) stocks of the highest (lowest) quality large-capitalization and mid-capitalization U.S. firms, based on profitability, growth, balance sheet safety and/or payout ratios.

They further test crash protection of varying allocations to the S&P 500 Index and a daily reformed hedge consisting of equal weights to: (1) a 3-month time series momentum component with no long equity positions and 0.7% annual trading frictions; and, (2) a quality factor component with 1.5% annual trading frictions. For this test, they scale retrospectively to 15% full-sample volatility. Throughout the paper, they assume cost of leverage is the risk-free rate. Using daily returns for the S&P 500 Index and inputs for the specified defensive strategies during 1985 through 2018, they find that:

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Does return persistence of individual stocks predict associated option returns? In their March 2019 paper entitled “Stock Return Autocorrelations and the Cross Section of Option Returns”, Yoontae Jeon, Raymond Kan and Gang Li investigate relationships between equity option returns and return autocorrelations of underlying stocks. They consider call options, put options and straddles (long both a call and a put with the same strike price). Each month on standard option expiration date, they:

• Measure one-step monthly stock return autocorrelations using a 36-month rolling window of monthly returns for U.S. stocks with over 20 monthly observations.
• Rank stocks (and respective options) by autocorrelation into fifths (quintiles).
• Construct a hedge portfolio that is long (short) the equal-weighted or market capitalization-weighted stocks in the top (bottom) quintile of autocorrelations, to calculate stock portfolio return as a control variable.
• Construct corresponding hedge portfolios of call options, put options or straddles, limiting choices to reasonably liquid options with moneyness closest to 1.0 and time to expiration closest to 30 days. 
• Hold these portfolios until the next standard option expiration date.

They further explore out-of-sample use of results via modified mean-variance optimization of a portfolio consisting of the S&P 500 Index, the risk-free asset and equity options with bid-ask spreads no greater than 10% of price. They size individual option positions as a function of underlying stock volatility, variance risk premium and stock return autocorrelation. They assume investor utility derives from constant relative risk aversion level 3. For the frictionless case, they base option returns on the bid-ask midpoint. For the case with frictions, they assume buys (sells) occur at the ask (bid). Using specified stock and options data during January 1996 through December 2017, they find that: Keep Reading

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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