We have previously discussed the price dynamics of the SP500 volatility index, VIX. A recent article [1] investigated the trending/mean-reverting properties of several volatility indices. The paper’s objective is to use the autoregressive fractionally integrated moving average (ARFIMA) model to extend research on Hurst exponent to the universe of volatility indices, including VIX, VXN, VXO, VXD, RVX, VPD, OVX, VVIX, and SKEW. The author used daily data from October 5, 2007, to October 5, 2020, which covers both calm and crisis periods. The Hurst exponent was used based on the ARFIMA model and the fractal dimension served as a confirmation tool. The authors reached the following conclusions,
Firstly, the consistent presence of long memory in various volatility indices supports the FMH. Past volatility certainly provides information about future prediction. Secondly, these empirical findings provide a theoretical premise for trading strategies. Evidence of the consistent presence of long memory points to the utility of applying trend-based trading strategies such as moving average convergence divergence (MACD). Thirdly, the SKEW might be used as a predictor of various probable crises (both financial and non-financial). Fourthly, the relative instability in long-memory traits is visible in all volatility indices. Hence, trading strategies might need to switch periodically for more consistent performance.
In short, the consistent presence of long memory in various volatility indices supports the fractal market hypothesis (FMH) and provides evidence for using trend-following trading strategies. However, we note that,
- The study was conducted on the spot volatility indices which are not tradable. In order to extract PnL from the indices, one should trade volatility Exchange-Traded Notes, (for example VXX), whose price dynamics might be different from their spot counterparts.
- It’s well-known that volatility-based strategies occasionally suffer heavy tail losses. Designing a volatility trading strategy that has an acceptable reward/risk profile is not trivial.
What do you think about the paper’s findings? Let us know in the comments below.
References
[1] B. Ghosh, E. Bouri, Long Memory and Fractality in the Universe of Volatility Indices, Complexity, vol. 2022, Article ID 6728432, 8 pages, 2022. https://doi.org/10.1155/2022/6728432
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