We’ve already analyzed tens of thousands of financial research papers and identified more than 700 attractive trading systems together with hundreds of related academic papers.
Browse Strategies- Unlocked Screener & 300+ Advanced Charts
- 700+ uncommon trading strategy ideas
- New strategies on a bi-weekly basis
- 2000+ links to academic research papers
- 500+ out-of-sample backtests
- Design multi-factor multi-asset portfolios
Upgrade subscription
The high difference between the implied volatility of index options and subsequent realized volatility is a known fact. Trades routinely exploit this difference by selling options with consecutive delta hedging. There is, however, a more elegant way to exploit this risk premium – the dispersion trading.
The dispersion trading uses the known fact that the difference between implied and realized volatility is greater between index options than between individual stock options. The investor, therefore, could sell options on index and buy individual stocks options. Dispersion trading is a sort of correlation trading as trades are usually profitable in a time when the individual stocks are not strongly correlated and loses money during stress periods when correlation rises. Basic trade could be enhanced by buying options of firms with high belief disagreement (high analysts’ disagreement about firms’ earnings).
Fundamental reason
The academic paper shows that dispersion in analysts’ forecasts is strongly related to the implied volatility of index and single-name options. Research shows that option excess returns reflect the different exposure to disagreement risk. Investors who buy options of firms that are more prone to heterogeneity in beliefs are compensated in equilibrium for holding this risk. Volatility risk premia of individual and index options represent compensation for the priced disagreement risk. Hence, in the cross-section of options, the volatility risk premium depends on the size of the belief heterogeneity of this particular firm and the business cycle indicator. As the risk-neutral skewness, the volatility risk premium for index options can be larger or smaller depending on the size of disagreement and of the firm’s share.
- Unlocked Screener & 300+ Advanced Charts
- 700+ uncommon trading strategy ideas
- New strategies on a bi-weekly basis
- 2000+ links to academic research papers
- 500+ out-of-sample backtests
- Design multi-factor multi-asset portfolios
Backtest period from source paper
1996-2007
Confidence in anomaly's validity
Strong
Indicative Performance
15.39%
Notes to Confidence in Anomaly's Validity
Notes to Indicative Performance
per annum after transaction costs, annualized (geometrically) monthly return of 1,2% from table 6
Period of Rebalancing
Monthly
Estimated Volatility
13.86%
Notes to Period of Rebalancing
Notes to Estimated Volatility
annualized monthly standard deviation 4%, data from table 6
Number of Traded Instruments
21
Notes to Number of Traded Instruments
20 options on individual stocks + 1 position in index option
Notes to Maximum drawdown
Complexity Evaluation
Very complex strategy
Notes to Complexity Evaluation
Financial instruments
options
Simple trading strategy
The investment universe consists of stocks from the S&P 100 index. Trading vehicles are options on stocks from this index and also options on the index itself. The investor uses analyst forecasts of earnings per share from the Institutional Brokers Estimate System (I/B/E/S) database and computes for each firm the mean absolute difference scaled by an indicator of earnings uncertainty (see page 24 in the source academic paper for detailed methodology). Each month, investor sorts stocks into quintiles based on the size of belief disagreement. He buys puts of stocks with the highest belief disagreement and sells the index puts with Black-Scholes deltas ranging from -0.8 to -0.2.
Hedge for stocks during bear markets
No - Absolutely not a hedge, dispersion trading strategy is a fat-tail strategy that loses a lot of money during crisis periods…
Out-of-sample strategy's implementation/validation in QuantConnect's framework
(chart+statistics+code)