Abstract
This paper proposes an improved procedure for stochastic volatility model estimation with an application in risk management. This procedure is composed of the following instrumental components: Fourier transform method for volatility estimation with a price correction scheme, and importance sampling for extremal event probability estimation with applications to estimate Value-at-Risk and conditional Value-at-Risk. Then we conduct a Value-at-Risk backtesting for some foreign exchange data and the S&P 500 index data. In comparison with empirical results obtained from RiskMetrics, historical simulation, and the GARCH(1,1) model, we find that our improved procedure outperforms on average.