I present a few systematic strategies for investing into volatility risk-premia and illustrate their back-tested performance. I apply the four factor Fama-French-Carhart model to attribute monthly returns on volatility strategies to returns on the style factors. I show that all strategies have insignificant exposure to the style factors, while the exposure to the market factor becomes insignificant when strategies are equipped with statistical filtering and delta-hedging.

It is becoming acknowledged that volatility strategies should constitute an integral part of allocation to alternatives in portfolios of institutional and HNW investors.

Indeed, both the academic and the practical experience indicate that volatility strategies produce robust risk-adjusted long-term performance, when properly designed and executed. These algorithmic strategies provide multiple solutions to invest and allocate to volatility strategies in a direct and transparent way. Yet, investors and allocators must make the ultimate decision about selecting and allocating to appropriate investment solutions.

Importantly, investors need to carefully consider the following aspects by allocating to volatility strategies:. In this note, I will describe a quantitative approach along with back-test simulations to answer these questions to make the allocation decision. I will present a few examples and draw interesting conclusions. I will only consider the volatility carry strategies which involve selling and shorting the volatility to capture the volatility risk-premia.

Compared to other asset classes, volatility strategies tend to exhibit higher drawdowns relative to their historical volatilities and strongly negative skewness of realized returns. As a result, implementation of these strategies requires the design of the systematic hedging algorithms. If the roll passes the filter, the strategy will sell options and implement the delta-hedging strategy upto the option expiry.

The delta-hedging strategy is only applied for the PUT and Strangle strategies which involve trading in options directly and have well-defined delta exposure. First the strategy applies the filter and, dependent on the signal strength, it enters either short when the VIX futures term structure is in contago or long positions when the VIX futures are in backwardation. In table 2, I present the summary of the nine strategies.

For the ease of visualization, I will use red color for strategies with no hedge, blue color for the strategies with the statistical filter,and green color for strategies with the filter and hedge. As the benchmarks, I use the three assets:. In table 3, I show the back-tested performance of the volatility strategies.

Figure 1 illustrates the Sharpe ratio vs the maximum drawdown. Figure 2 illustrates the strategy alpha vs beta. The monthly alpha from the regression is annualized. Table 4 reports the realized correlation matrix of monthly returns on these strategies. Return is the total annualized return. Vol is the volatility of monthly returns. Sharpe is the Sharpe ratio using monthly volatility.

Skewness and Kurtosis are the skewness and excess kurtosis of monthly returns, respectively. They have beta about 0. They are also strongly correlated among each other with average correlation of 0. They also produce a smaller beta of about 0.

Their average pair-wise correlation is about 0. The strategies with the filter and delta-hedging produce the strongest risk adjusted performance with very small beta and significant alpha.

Their pairwise correlation is 0. Table 5 reports the estimated coefficients of the 4-factor model. We see that all strategies have insignificant exposures to the style factors. Only the put strategy has a significant exposure to the momentum factor, which is intuitive.

The exposure to the market factor is significant for vanilla strategies, while it reduces considerably for the strategies with the filter. The strategies with the delta-hedge have insignificant exposure to the market factor. Estimated exposures to Fama-French-Carhart 4-factor model using monthly returns from to September Alpha is the annualized alpha, MRK is the beta to the market factor, SMB is the beta to the capitalization factor small minus big , HML is the beta to the book-to price value factor high minus low , is the beta to the momentum factor up minus down.

The value of the t-statistics is provided in the parentheses. Now I consider the impact of the volatility strategies on the portfolio level. Again, I use the three benchmarks. The annualized alpha of this regression indicates the marginal contribution of the volatility strategy to generation of the alpha for the benchmarked portfolio.

Figure 3 shows the contribution to the portfolio alpha. However, they do produce significant contribution to portfolios benchmarked to UST bonds. This is because they have equity overlay with helps to off-set the rates risk in bullish market conditions. Furthermore, they improve the risk-adjusted contribution to fixed-income portfolios by reducing the downside of the equity overlay.

The strategies with the filter and delta-hedge have a mixed contribution: Both Strangle and VIX strategies has a significant improvement of the risk-profile for all of the three benchmarks. Well-designed algorithmic strategies provide transparent solutions for investing to volatility risk-premia. The risk profile and delta exposures must be explained to investors and tailored to their portfolios and benchmarks.

The volatility strategies with statistical filtering can be applied as overlays in fixed-income portfolios. The delta-hedged option strategies and long-short VIX futures strategies can be applied as absolute return strategies in allocations to alternatives. His focus is on quantitative models for systematic trading strategies, risk-based asset allocation, and volatility trading.

Prior to that, Artur worked as a front office quant in equity and credit at Bank of America, Merrill Lynch and Bear Stearns in New York and London with emphasis on volatility modelling and multi- and cross-asset derivatives valuation, trading and risk-managing. His research area and expertise are on econometric data analysis, machine learning, and computational methods with their applications for quantitative trading strategies, asset allocation and wealth management.

Artur has published several research articles on quantitative finance in leading journals and he is known for his contributions to stochastic volatility and credit risk modelling. He is a member of the editorial board of the Journal of Computational Finance. Artur keeps a regular blog on quant finance and trading at http: The views and analysis presented in this article are those of the author alone and do not represent any of the views of his employer.

This article does not constitute an investment advice. Selling volatility in a low volatility regime: You are commenting using your WordPress.

You are commenting using your Twitter account. You are commenting using your Facebook account. Notify me of new comments via email. Create a website or blog at WordPress. Artur Sepp Research Blog: Importantly, investors need to carefully consider the following aspects by allocating to volatility strategies: The design of a systematic strategy, which most importantly includes what instruments should be traded, the rebalancing frequency, and the delta risk exposure.

Algorithmic strategies for investing in volatility Asset universe I will only consider the volatility carry strategies which involve selling and shorting the volatility to capture the volatility risk-premia. Strangle strategy which involves selling one month out-of-the money put option with the option delta of about and out-of-the money call option with the option delta of about In table 1, I provide some details about these strategies.

The Strangle strategy is a play on market implied volatilities for out-of-the money puts and calls. This strategy is about delta-neutral at the inception in each month and benefits from higher skew for index puts and implied convexity for puts and calls. The contago effect is produce by expectations of higher volatility in the future and higher hedging costs for the future uncertainty.

Design of hedging strategies Compared to other asset classes, volatility strategies tend to exhibit higher drawdowns relative to their historical volatilities and strongly negative skewness of realized returns. For each asset, I will consider the following hedging approaches: The relative value involves applying a time series model which uses data strictly prior to the roll date and computing the expected value of the roll based on the available historical information.

If the expected value of the strategy is less than defined threshold, the roll is not executed at given rebalancing date. The filter enables to make a quantitative judgment about the expected profitability of each roll given prior historical information. If the expected performance falls below the desired threshold, the roll is not executed.

No hedging is executed through the life of the roll. Characteristics of the hedging strategies Strategy name Underlying Statistical Filter? The volatility targeting is implemented in the two steps: The unleveraged strategy is implemented. For the put and strangle strategies, the number of option contract is computed at each roll date as the strategy funds divided by the put strike.

The number of contract for the VIX futures is computed as the ratio of strategy fund to the price of the constant maturity one month VIX futures. The volatility of each of the unleveraged strategies is computed at each roll dates using the time series strictly prior to the roll. The volatility of the strategy with the relative value filter is computed only when strategies have open positions.

Back-tested performance of individual strategies I use the period from January to September the VIX futures trading started in October As the benchmarks, I use the three assets: The realized total performance of the benchmarks includes dividends distributed by these ETFs. The back-tested performance of volatility strategies from to September Notations: Backtested Sharpe ratio vs max drawdown Figure 2.

The correlation matrix of realized monthly returns. Estimated exposures to Fama-French-Carhart 4-factor model using monthly returns from to September Notations:

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