Predicting Realized Volatility Using Skewness and Kurtosis

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Realized volatility refers to the actual volatility experienced by a financial asset over a specific period, typically computed using historical price data. By calculating realized volatility, investors and analysts can gain insights into the true level of price variability in the market, which can be valuable for risk management, portfolio optimization, and trading strategies. Realized volatility is often used in conjunction with implied volatility to assess the accuracy of market forecasts and to adjust trading strategies accordingly.

Reference [1] examines whether realized volatility can be forecasted. Specifically, it studies whether realized skewness and kurtosis can be used to forecast realized volatility. The authors pointed out,

Given the important role of volatility forecasts accuracy in optimal portfolio designs, this study provides strong evidence that realized kurtosis is most useful when one to 22 day ahead forecasts are of interest by taking large and diverse set of data of 452 listed firms at PSX, and thus could assist in improving asset allocation decisions. Thus, the standard HAR model and its extensions containing realized kurtosis predicts the expected realized volatility as a linear function of yesterday’s realieed volatility and its mean over prior week and month as well as yesterday’s realieed kurtosis. Therefore, it is concluded that stocks’ own realieed kurtosis carries meaningful information for stocks’ future volatilities.

Briefly, the author extended the HAR-RV model to incorporate realized skewness and kurtosis. They found that realized kurtosis is most useful when forecasting one to 22 days ahead.

Note that this study was conducted in the Pakistan stock market, but the research framework can be applied to any stock market and asset class.

Let us know what you think in the comments below or in the discussion forum.

References

[1]  Seema Rehman, Role of realized skewness and kurtosis in predicting volatility, Romanian Journal of Economic Forecasting, 27(1) 2024

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