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This study provides an empirical analysis back-testing the implementation of a dispersion trading strategy to verify its profitability. Dispersion trading is an arbitrage-like technique based on the exploitation of the overpricing of index options, especially index puts, relative to individual stock options. The reasons behind this phenomenon have...
Contexts in source publication
Context 1
... order the stocks based on their mean calcu- lated in step 2 and select the first 20 stocks in the ranking (Table 1); 4) regress, without intercept, the daily returns of the index on those of the 20 selected stocks (Table 2); 5) discard the stocks, seven in this case, with a significance of at least 1%; 6) repeat step 4 for the remaining stocks, 13 in this case (Table 3). ...
Context 2
... the implied volatility is calculated as the mean between the implied volatilities of the at-the-money call and put on each stock. Thereafter, to identify a proxy of the mean level of correlations among constituents implied by option prices, equation (2) is modified as follows: Table 3. ...
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Citations
... New York, NY, USA in 2019 with headquarter in New York City, United States), an alternative asset manager with $34.5bn total assets under management [11], set up a dispersion trade worth around $8.8bn on the S&P 100 index [12]. In academia, References [13][14][15] examined the profitability of dispersion trades and delivered evidence of substantial returns across markets. However, Reference [16] reported that returns declined after the year 2000 due to structural changes in options markets. ...
... However, Reference [16] reported that returns declined after the year 2000 due to structural changes in options markets. References [14][15][16] enhanced their returns by trading dispersion based on an index subset. All of those studies try to replicate the index with as few securities as possible, but neglect the individual explanatory power of stocks in their weighting schemes. ...
... The fundamentals of the applied procedure are based on [15,16,41], but we improve the selection process by identifying stocks with the highest explanatory power. Therefore, we are in a position to get a basket of stocks that explains the index in a more accurate way. ...
This paper develops a dispersion trading strategy based on a statistical index subsetting procedure and applies it to the S&P 500 constituents from January 2000 to December 2017. In particular, our selection process determines appropriate subset weights by exploiting a principal component analysis to specify the individual index explanatory power of each stock. In the following out-of-sample trading period, we trade the most suitable stocks using a hedged and unhedged approach. Within the large-scale back-testing study, the trading frameworks achieve statistically and economically significant returns of 14.52 and 26.51 percent p.a. after transaction costs, as well as a Sharpe ratio of 0.40 and 0.34, respectively. Furthermore, the trading performance is robust across varying market conditions. By benchmarking our strategies against a naive subsetting scheme and a buy-and-hold approach, we find that our statistical trading systems possess superior risk-return characteristics. Finally, a deep dive analysis shows synchronous developments between the chosen number of principal components and the S&P 500 index.
