Equity Options

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

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