Equity Options

Is systematically selling cash-covered equity index put options an attractive strategy? In his January 2016 paper entitled “An Analysis of Index Option Writing with Monthly and Weekly Rollover”, Oleg Bondarenko analyzes the performance of the CBOE S&P 500 PutWrite Index (PUT), launched in 2007, and the CBOE S&P 500 One-Week PutWrite Index (WPUT), launched in 2015. These indexes track gross performance of a strategy that each month (for PUT) or each week (for WPUT) sells at-the-money S&P 500 Index options fully secured at sale by U.S. Treasury bills (T-bills) and holds to cash settlement at expiration. His principal benchmark is the S&P 500 Total Return Index. Using monthly price history for PUT since the end of June 1986 and weekly price history for WPUT since the end of January 2006 all through December 2015, he finds that: Keep Reading

Is systematic selling of equity index put or call options an attractive strategy? In their December 2015 paper entitled “Index Options Realized Returns Distributions from Passive Investment Strategies”, Jose Dapena and Julian Siri analyze equity index call and put option returns from the perspective of a seller. They view a systematic option seller as an insurance company: (1) collecting option premiums at the time of sale; (2) maintaining capital in the form of a margin requirement; and, (3) cash-settling at expiration. They consider options written on the Dow Jones Industrial Average, the S&P 500 Index or the NASDAQ 100 Index with maturities of approximately 60, 180 or 365 days. They consider options with moneyness between 0.95 and 1.05. They estimate return by subtracting value at expiration from initial (bid) price and dividing the difference by initial, average or maximum margin required per the CBOE Rulebook. They calculate average and maximum margin requirements based on daily values while each option is active. Using bid prices for specified options during January 1996 through July 2013, they find that: Keep Reading

What are the best ways to buy or sell tail risk protection (crash insurance)? In his May 2015 paper entitled “Should You Buy or Sell Tail Risk Hedges? A Filtered Bootstrap Approach”, Lorenzo Baldassini uses filtered bootstrap simulations to estimate whether and how an investor can enhance an equity index return distribution (a buy-and-hold benchmark) by buying or selling index put options. He compares benchmark returns to those of the index plus one of the following four put option overlay strategies for investment intervals of one, five or 15 years:

1. Static Purchase – iteratively buy put options as crash protection and hold them to expiration.
2. Static Sale – iteratively sell put options to collect their premiums and hold them to expiration.
3. Dynamic Purchase – iteratively buy put options as crash protection but sell and replace them before expiration if they appreciate above a set percentage threshold.
4. Dynamic Sale – iteratively sell put options to collect their premiums but buy them back and replace them if they depreciate below a set percentage threshold. 

He considers two values for each of the three overlay parameters used in the simulation model: (1) the budget for option overlay transactions (0.5% or 1.5% of portfolio value); (2) option maturity (3 or 12 months); and, (3) for dynamic overlays, option exit appreciation/depreciation threshold (50% or 500%). For the benchmark and each overlay variation, he runs the simulation 300 times to generate a return distribution. Using daily S&P 500 index returns and daily index put option implied volatilities for different expirations and moneyness during January 2003 through December 2012 to calibrate simulation inputs, he finds that: Keep Reading

Is selling market index call options to finance, at least partly, buying crash protection in the form of put options a shrewd tactic? In their December 2016 paper entitled “Risk and Return of Equity Index Collars”, Roni Israelov and Matthew Klein investigate the performance of such equity index collars by decomposing their returns into equity risk premium and volatility risk premium components. They treat the CBOE S&P 500 95-110 Collar Index (CLL) that buys three-month put options about 5% out-of-the-money and sells one-month call options about 10% out-of-the-money as representative of equity index collars. They consider four alternatives to CLL (normalized via leverage to provide the same compound return as CLL):

1. Reduce equity exposure with cash.
2. Buy put options per CBOE S&P 500 5% Put Protection Index (PPUT).
3. Sell covered calls per CBOE S&P 500 BuyWrite Index (BXM).
4. Sell covered calls and adjust the equity index position to maintain a constant equity exposure (hedged covered calls).

Using monthly returns for the S&P 500 Index, CLL, PPUT and BXM as available (starting July 1986 or March 1996) through December 2014, they find that: Keep Reading

What are the moving parts of an equity index covered call strategy, and what can investors do to enhance its performance? In the October 2015 update of their paper entitled “Covered Calls Uncovered”, Roni Israelov and Lars Nielsen decompose equity index covered call strategy returns into three risk premiums: (1) long equity; (2) short equity volatility; and, (3) long equity reversal (market timing). They then test a hedged covered call strategy designed to eliminate uncompensated risk from market timing through hedging. This hedged strategy each day measures the delta of the covered call and takes an offsetting position in the underlying index (via futures), continuing to collect the equity and volatility risk premiums without market timing risk. Using daily levels of the S&P 500 Index (plus dividends), the CBOE S&P 500 BuyWrite Index (BXM) and the CBOE S&P 500 2% OTM BuyWrite Index (BXY) during March 1996 through December 2014, they find that: Keep Reading

Do stock pricing factors predict option returns that are incremental to the factor premiums in underlying stock returns? In the December 2015 version of their paper entitled “Option Return Predictability”, Jie Cao, Bing Han, Qing Tong and Xintong Zhan examine whether 12 factors known to predict stock returns also predict delta-hedged (stock price-neutral) equity option returns. The 12 factors are: size, book-to-market, one-month reversal, momentum, accruals, asset growth, cash-to-assets, analyst earnings forecast dispersion, net stock issuance, idiosyncratic volatility, profitability and standardized unexpected earnings. For portfolio realism, they focus on monthly delta-neutral call writing. Specifically, for each factor each month, they:

• Rank stocks with dividend-unaffected options into tenths (deciles) based on the factor.
• Write an at-the-money call option with about 50 days to expiration and buy delta shares of each underlying stock (no daily hedge adjustments).
• Reform a portfolio that is long (short) the decile of delta-hedged written calls with the highest (lowest) expected factor returns.

They also look at a symmetric put strategy (buy a put and sell delta shares of the underlying stock). Using price/firm data for a broad (but groomed) sample of U.S. common stocks with options and daily closing bid and ask quotes for the specified options during January 1996 through December 2012 (a total of 5,179 underlying stocks), they find that: Keep Reading

How well do equity index option strategies work during crises? In his October 2015 paper entitled “The Performance of Equity Index Option Strategy Returns during the Financial Crisis”, Dominik Schulte tests the profitability of long and short equity index option strategies during the financial crisis of 2008, including long (as defined) and short (opposite) versions of:

• Call: buy a call.
• Put: buy a put.
• Straddle: buy a call and sell a put with the same maturity and strike.
• Strangle: buy a call and a put with the same maturity, but with the call at a higher strike.
• Butterfly: buy in-the-money and out-of-the-money calls and sell two at-the-money calls, all with the same maturity.
• Put spread: sell an out-of-the-money put and buy an at-the-money put.
• Put-call spread: sell an out-of-the-money put and buy an at-the-money call.

He considers maturities of one to three months and moneyness from 90% to 110% as allowed. To assess the import of non-normal return distributions, he considers strategy return skewness, kurtosis and Omega ratio (which incorporates all moments). He estimates trading frictions from bid-ask spreads. Using end-of-month bids and asks for calls and puts on the S&P 500, the EuroStoxx 50 and the DAX indexes during January 2006 through September 2010, he finds that: Keep Reading

When does it make sense to exercise equity options early? Does it happen frequently? In the September 2015 version of their paper entitled “Early Option Exercise: Never Say Never”, Mads Jensen and Lasse Pedersen investigate the interaction of investment frictions (shorting, trading and funding costs) and early exercise of equity options. They estimate shorting frictions via daily cost-of-borrow rankings for underlying stocks. They estimate trading frictions via option bid-ask spread rankings. They estimate funding friction as cost of required margins in excess of the risk-free (Federal Funds) rate. Using groomed data for U.S. equity options along with associated stock prices and borrowing cost data spanning 2003 through 2010, they find that: Keep Reading

Do low-volatility strategies work for all stocks? In their April 2015 paper entitled “Low Risk Anomalies?”, Paul Schneider, Christian Wagner and Josef Zechner examine relationships between low-beta/low-volatility stock anomalies and implied stock return skewness. They compute ex-ante (implied) skewness for each stock via a portfolio of associated options that is long (short) out-of-the-money calls (puts). The more investors are willing to pay for downside risk protection (puts), the more negative this measure becomes. Using stock and option price data for 5,509 U.S. stocks for which options are available during January 1996 through August 2014, they find that: Keep Reading

Do equity option traders really bear the relatively large quoted bid-ask spreads as trading frictions? In their March 2015 paper entitled “Option Trading Costs Are Lower Than You Think”, Dmitriy Muravyev and Neil Pearson examine whether the predictability of changes in quoted option prices enables sophisticated investors to suppress option trading frictions. Instead of the bid-ask midpoint, they use a regression-based estimate of the “true value” of an option based on high-frequency publicly available information that reflects trade timing. Because trades tend to occur when true value estimates differ from respective bid-ask midpoints, their adjusted effective spreads (quoted versus true value) differ from the conventionally measured effective spreads. Using tick-level data for 37 individual U.S. stocks and two exchange-traded funds from both the equity and option markets during April 2003 through October 2006 (882 trading days, during which algorithmic trading grows to dominate option markets), they find that: Keep Reading