Evaluation of realized volatility predictions from models with leptokurtically and asymmetrically distributed forecast errors

Stavros Degiannakis and Alexandra Livada

Journal of Applied Statistics, 2016, vol. 43, issue 5, 871-892

Abstract: Accurate volatility forecasting is a key determinant for portfolio management, risk management and economic policy. The paper provides evidence that the sum of squared standardized forecast errors is a reliable measure for model evaluation when the predicted variable is the intra-day realized volatility. The forecasting evaluation is valid for standardized forecast errors with leptokurtic distribution as well as with leptokurtic and asymmetric distributions. Additionally, the widely applied forecasting evaluation function, the predicted mean-squared error, fails to select the adequate model in the case of models with residuals that are leptokurtically and asymmetrically distributed. Hence, the realized volatility forecasting evaluation should be based on the standardized forecast errors instead of their unstandardized version.

Date: 2016
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Working Paper: Evaluation of Realized Volatility Predictions from Models with Leptokurtically and Asymmetrically Distributed Forecast Errors (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:43:y:2016:i:5:p:871-892

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DOI: 10.1080/02664763.2015.1079306

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