Abstract

JESUS-GUTIERREZ, Raúl de. The Use of Implied Volatility in Conditional Variance Modeling Can Improve Volatility Forecasting and VaR and CVaR Estimation. Econ: teor. práct [online]. 2023, n.58, pp.173-198.  Epub June 26, 2023. ISSN 2448-7481.  https://doi.org/10.24275/etypuam/ne/582023/jesus.

This paper aims to incorporate the S&P/BMV IPC VIX index into the variance equation of the GARCH, EGARCH, FIGARCH and FIEGARCH models to improve conditional volatility forecasts and VaR and CVaR estimates for the short and long positions on the Mexican Stock Exchange index. The results of the statistical test for spa show that the S&P/BMV IPC VIX index provides additional information for improving the accuracy of conditional volatility forecasting, but its value is economically small. Backtesting results reveal that the VaR-FIEGARCHVIX, VaR-EGARCHVIX, CVaR-FIGARCHVIX and CVaR-GARCHVIX measures are optimal for correctly estimating risk at conventional confidence levels for both financial positions, although the FIGARCH model is clearly superior for short position CVaR forecasts. Findings have important implications for risk management and financial regulation.

Keywords : implied volatility; volatility forecasting; value at risk.

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