VaR/CVaR ESTIMATION UNDER STOCHASTIC VOLATILITY MODELS

Chuan-Hsiang Han (), Wei-Han Liu () and Tzu-Ying Chen ()
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Chuan-Hsiang Han: Department of Quantitative Finance, National Tsing-Hua University, Hsinchu, Taiwan 30013, R.O.C.
Wei-Han Liu: Department of Finance, La Trobe University, Melbourne, Australia
Tzu-Ying Chen: Department of Finance, National Taiwan University, Taipei, Taiwan

International Journal of Theoretical and Applied Finance (IJTAF), 2014, vol. 17, issue 02, 1-35

Abstract: This paper proposes an improved procedure for stochastic volatility model estimation with an application to Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) estimation. This improved procedure is composed of the following instrumental components: Fourier transform method for volatility estimation, and importance sampling for extreme event probability estimation. The empirical analysis is based on several foreign exchange series and the S&P 500 index data. In comparison with empirical results by RiskMetrics, historical simulation, and the GARCH(1,1) model, our improved procedure outperforms on average.

Keywords: Stochastic volatility; Fourier transform method; importance sampling; (conditional) Value-at-Risk; backtesting; C13; C14; C63 (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1142/S0219024914500095

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