Equity Options

Can investors/speculators use equity options to boost return through buying and selling leverage (calls), and/or buying and selling insurance (puts)? If so, which strategies work best? These blog entries relate to trading equity options.

Page 1 of 812345678

Equity Index Collar Performance

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

Enhancement of Index Covered Calls via Hedging

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

Option Strategies Based on Factor Sorts

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

Stress Test for Equity Index Option Strategies

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 Do Holders of Equity Options Exercise Early?

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

Simple Stock Index Option Strategies

Do simple stock index option strategies (stock-covered calls, cash-covered puts and collars) outperform the underlying index? To investigate, we examine first the performance of the CBOE S&P 500 BuyWrite Index (BXM), the CBOE S&P 500 PutWrite Index (PUT) and the CBOE S&P 500 95-110 Collar Index (CLL), with the S&P 500 Total Return Index SPTR) as a benchmark. Since these series are modeled indexes rather than tradable assets, we then examine the relatively short records of exchange-traded funds (ETF) and notes (ETN) designed to track BXM, iPath CBOE S&P 500 BuyWrite Index ETN (BWV) and PowerShares S&P 500 BuyWrite (PBP), with SPDR S&P 500 (SPY) as a benchmark. Using end-of-month levels/total returns for SPTR, BXM, PUT and CLL since June 1986, and for SPYBWV and PBP since December 2007 (limited by inception of PBP), all through August 2015, we find that: Keep Reading

Betting Against High Downside Risk?

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

Timing Option Trades to Suppress Trading Frictions

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

Options for Retirement?

Is use of long-term stock index call options effective for those approaching retirement with desires of limiting exposure to crashes without sacrificing all benefit of equity exposure? In his January 2015 paper entitled “Individuals Approaching Retirement Have Options (Literally) to Secure a Comfortable Retirement”, Bryan Foltice proposes retirement strategies that employ stock index options during the five years before retirement (when prospective retirees tend to become very risk-averse) to limit equity risk while retaining some reward. These alternatives to conventional (100% stocks, 60%-40% stocks-bonds and 100% minus age in stocks) asset allocation strategies put core funds in Treasury Inflation-Protected Securities (TIPS) to secure retirement income at a real 75% of final working income and funds in excess of the core to buy long-term at-the-money stock index call options. He considers three option-based strategies:

  1. Buy 5-year options at age 60.
  2. Buy a 3-year option at age 60 and a 2-year option at age 63.
  3. Buy 1-year call options each year using the final five annual contributions.

Base modeling assumptions use 1928-2013 historical return statistics, with robustness tests assuming (1) an increased equity risk premium and (2) expectations derived from 2014 data through October. Modeling includes expected costs/fees. Using simulations based on estimates for U.S. stock market capital gains/dividends and for the TIPS real yield, he finds that: Keep Reading

Expected Volatility of Stock Market Volatility as a Predictor

S&P 500 Index options data imply expected S&P 500 Index volatility (VIX) over the next month. In turn, VIX futures options data imply expected volatility of VIX (VVIX) over the next month. Does VVIX predict stock index option and VIX option returns? In their September 2014 paper entitled “Volatility-of-Volatility Risk”, Darien Huang and Ivan Shaliastovich investigate whether VVIX represents a time-varying risk affecting: (1) S&P 500 Index option returns above and beyond the risk represented by VIX; and (2) VIX futures option returns. They measure risk effects via returns on S&P 500 Index options hedged daily by shorting the S&P 500 Index and VIX futures options hedged daily by shorting VIX futures. Using monthly S&P 500 Index returns, VIX futures returns, VIX, VVIX, S&P 500 Index option prices and VIX option prices during February 2006 through June 2013, they find that: Keep Reading

Page 1 of 812345678
Email Subscribe
Current Momentum Winners

ETF Momentum Signal
for February 2016 (Final)

Winner ETF

Second Place ETF

Third Place ETF

Gross Compound Annual Growth Rates
(Since August 2006)
Top 1 ETF Top 2 ETFs
11.5% 11.9%
Top 3 ETFs SPY
12.4% 6.5%
Strategy Overview
Current Value Allocations

ETF Value Signal
for February 2016 (Final)





The asset with the highest allocation is the holding of the Best Value strategy.
Gross Compound Annual Growth Rates
(Since September 2002)
Best Value Weighted 60-40
12.4% 9.4% 7.6%
Strategy Overview
Recent Research
Popular Posts
Popular Subscriber-Only Posts