Equity Options

Is systematically selling covered call options on equity indexes, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider five equity covered call ETFs:

1. Invesco S&P 500 BuyWrite (PBP) – seeks to track the CBOE S&P 500 BuyWrite Index (BXM).
2. Global X S&P 500 Covered Call (XYLD) – seeks to track BXM.
3. Global X NASDAQ 100 Covered Call (QYLD) – seeks to track the CBOE Nasdaq-100 BuyWrite V2 Index (BXNT). We use CBOE NASDAQ-100 BuyWrite Index (BXN) based on availability of historical data.
4. First Trust BuyWrite Income (FTHI) – holds U.S. stocks of all market capitalizations and sells at-the-money to slightly out-of-the-money covered calls on the S&P 500 Index up to 20% of fund assets, laddered with expirations of less than one year (we use BXM as a benchmark).
5. Global X Russell 2000 Covered Call (RYLD) – seeks to track the CBOE Russell 2000 BuyWrite Index (BXRC).

We focus on average monthly return, standard deviation of monthly returns, compound annual growth rate (CAGR) and maximum drawdown (MaxDD) based on monthly data. We consider SPDR S&P 500 ETF Trust (SPY), Invesco QQQ Trust (QQQ) and iShares Russell 2000 ETF (IWM) as underlying stock index proxies. Using monthly dividend-adjusted returns for the five covered call ETFs since inceptions and contemporaneous monthly levels of all benchmarks/underlying index proxies through April 2023, we find that: Keep Reading

Should individuals who trade zero-days-to-expiration (0DTE) S&P 500 Index options expect to make money? In their March 2023 paper entitled “Retail Traders Love 0DTE Options… But Should They?”, Heiner Beckmeyer, Nicole Branger and Leander Gayda examine performance of retail 0DTE S&P 500 Index option trades. They focus on effects of the introduction of daily expirations for such options in mid-May 2022. Using daily S&P 500 Index option trade data from CBOE, including trader-type transaction codes, during January 2021 through February 2023, they find that: Keep Reading

Does the difference in individual stock/market return relationships between good times (relatively low correlations) and bad times (relatively high correlations) present an easy and efficient way to hedge against stock market crashes (tail risk)? In their March 2023 paper entitled “Tail Risk Hedging: The Search for Cheap Options”, Poh Ling Neo and Chyng Wen Tee test the ability of a portfolio of liquid but cheap put options on individual stocks to protect against equity market crashes. They reason that:

• These options are inexpensive compared to equity index put options.
• During good times, the relatively low return correlations across stocks limit option portfolio drag.
• During market crashes, the spike in these correlations confers on the option portfolio tail risk protection comparable to that of equity index put options.

Their tests encompass three stock market regimes: (1) up months have positive monthly returns and no daily return less than -5%; (2) down months have negative monthly returns but no daily return less than -5%; and, (3) tail risk months have at least daily return less than -5%. At the end of each month, they construct a crash protection put option portfolio as follows:

• Select an out-of-the-money put option for each optionable stock with delta closest to -10% and six months to a year until expiration.
• Exclude those with ex-dividend dates prior to expiration.
• Exclude those with bid-ask spreads over 50% of the bid-ask midpoint.
• Allocate 2% of the value of the S&P 500 Index position equally to each of the cheapest 20% of remaining put options.

Most analyses assume option buys and sells occur at bid-ask midpoints (no frictions), but they do look at impacts of effective bid-ask spreads up to 50% of the quoted spread. Using daily returns for the S&P 500 Index, S&P 500 Index put options and individual U.S. stock put options during January 1996 through December 2020, they find that: Keep Reading

Defined outcome Exchange-Traded Funds (ETF) use complex options strategies that buffer against loss but cap gain to generate a defined outcome for investors over a predefined period. Are they attractive? In their February 2023 paper entitled “The Dynamics of Defined Outcome Exchange Traded Funds”, Luis García-Feijóo and Brian Silverstein analyze average performance of the Innovator Defined Outcome ETF Buffer Series from 2019 through 2021. They also model the performance of the underlying strategy and simulate average outcome during January 2013 through August 2022. They consider three benchmarks: SPDR S&P 500 ETF Trust (SPY); 50% allocation to SPY and 50% allocation to iShares Core US Aggregate Bond ETF (AGG); and, iShares MSCI USA Min Vol Factor ETF (USMV). Using actual and simulated returns for the selected defined outcome ETFs/benchmarks as described, they find that:

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Is put-spread collar strategy performance sensitive to the portfolio rebalancing schedule (due to rebalance timing luck). In their January 2023 paper entitled “The Hidden Cost in Costless Put-Spread Collars: Rebalance Timing Luck”, Steven Braun, Corey Hoffstein, Roni Israelov and David Nze Ndong analyze the performance of the following quarterly put-spread collar strategy: (1) long passive exposure to the S&P 500 Index; (2) a put spread on the index (long a 5% out-of-the-money put and short a 20% out-of-the-money put); and, (3) short an out-of-the-money call option on the index equal in value to the put combination. All option positions commence three months from expiration, and option positions are reset at each expiration (third Friday of expiration month). They consider three expiration schedules: December, March, June, September (DMJS); January, April, July, October (JAJO); and, February, May, August, November (FMAN). As a benchmark neutralizing rebalance timing effects, they further construct an equal-weighted DMJS-JAJO-FMAN portfolio. Using monthly S&P 500 Index returns and returns for the specified index options positions during 2008 through 2022, they find that:

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A subscriber requested confirmation that a strategy of holding SPDR S&P 500 ETF Trust (SPY) at all times except options expiration week beats holding SPY all the time. To investigate, we look at holding SPY at all times except from the close on the second Friday of each month to the close on the third Friday of each month (Strategy). When the market is closed on Friday, we use the Thursday or next earliest close. We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as essential performance statistics. We apply round-trip trading frictions of 0.1% for SPY-cash switches. Given settlement/cash-sweep delays, we assume zero return on cash. Using daily dividend-adjusted closes of SPY from inception in January 1993 through December 2022, we find that: Keep Reading

Is option gamma, which indicates how aggressively option market makers must trade underlying stocks to hedge their option positions, a systematic driver of those stock returns? In his October 2022 paper entitled “Option Gamma and Stock Returns”, Amar Soebhag investigates the relationship between option gamma for individual stocks and future returns of those stocks. He defines net gamma exposure of a stock as a hedge-adjusted, gamma-weighted sum of open interest for options written on the stock. He each month sorts stocks into value-weighted tenths (deciles) by net gamma for the previous month and calculates next-month returns on the decile portfolios, with focus on the difference in returns between extreme deciles. He then looks at behavior of net gamma across stocks, interactions of net gamma with other stock return predictors and time variation of aggregate net gamma. Using daily gamma, open interest, implied volatility and trading volume for each option contract on listed U.S. common stocks price over $5 as available during January 1996 through December 2021, as well as contemporaneous returns for underlying stocks and data for other widely accepted stock return predictors, he finds that:

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Do simple stock index option strategies (stock-covered calls, cash-covered puts and collars) outperform the underlying index? To investigate, we examine performances of:

1. CBOE S&P 500 BuyWrite Index (BXM),CBOE S&P 500 PutWrite Index (PUT) and CBOE S&P 500 95-110 Collar Index (CLL), with S&P 500 Total Return Index (SPTR) as a benchmark.
2. Invesco S&P 500 BuyWrite ETF (PBP), designed to track BXM, and WisdomTree CBOE S&P 500 PutWrite Strategy Fund (PUTW), designed to track PUT, with SPDR S&P 500 ETF Trust (SPY) as a benchmark.

We focus on monthly return statistics, compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) for comparisons. Using end-of-month levels for SPTR, BXM, PUT and CLL since June 1986, total returns for SPY and PBP since December 2007 (limited by PBP) and total returns for SPY and PUTW since February 2016 (limited by PUTW), all through March 2022, we find that:

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Are there anomalies for U.S. stock market returns around equity option expiration (OE) days (normally the third Friday of each month, but the preceding Thursday when the market is closed on the third Friday)? To investigate, we examine close-to-close S&P 500 Index returns from five trading days before through five trading days after a moderately large sample of OE days. Using daily closing prices for the index during January 1990 through February 2022 (386 OE days), we find that:

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How well do derivatives traders perform, and why? In the July 2021 version of their paper entitled “Derivatives Leverage is a Double-Edged Sword”, Avanidhar Subrahmanyam, Ke Tang, Jingyuan Wang and Xuewei Yang study the performance of Chinese derivatives (futures) traders across 1,086 contracts on 51 underlying assets. They consider gross and net daily trader returns, turnover and degree of leverage implied by contracts held. They further investigate sources of profits/losses for these traders. To identify clearly skilled (unskilled) traders, they identify those in the top (bottom) 5% of Sharpe ratios who trade on at least 24 days during the first year of the sample period and isolate those with statistically extreme performance. They then analyze trading behaviors and results for these extreme performers the next two years. Using data from a major futures broker in China, including transaction histories, end-of-day holdings and account flows (injections and withdrawals) for 10,822 traders (315 institutional) during January 2014 through December 2016, they find that:

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