Risk management based on stochastic volatility
Risk management approaches that do not incorporate randomly changing volatility tend to under- or overestimate the risk, depending on current market conditions. We show how some popular stochastic volatility models in combination with the hyperbolic model introduced in Eberlein and Keller (1995) can be applied quite easily for risk management purposes. Moreover, we compare their relative performance on the basis of German stock index data.
Online References:
Andersen, T., Chung, H., and Sorensen, B. (1999). Efficient method of moments estimation ofa stochastic volatility model: a Monte Carlo study. Journal of Econometrics 91, 61–87.
Barndorff-Nielsen, O. (1998). Processes of normal inverse Gaussian type. Finance & Stochastics 2, 41-68.
Barndorff-Nielsen, O., and Shephard, N. (2001). Non-Gaussian Ornstein
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