Financial markets embed expectations of central bank policy into asset prices. This paper compares two approaches that extract a probability density of market beliefs. The first is a simulated moments estimator for option volatilities described in Mizrach (2002); the second is a new approach...

A new and quite general model class for modeling asset returns and forecasting Value at Risk is proposed. It combines a dynamic multi-component GARCH structure with the stable Paretian distributional assumption. The new model nests several successful models for modeling asset returns, including...

The recently proposed class of MixN-GARCH models, which couple a mixed normal distributional structure with linked GARCH-type dynamics, has been shown to offer a plausible decomposition of the contributions to volatility, as well as admirable out-of-sample forecasting performance, for financial...

A resampling method based on the bootstrap and a bias-correction step is developed for improving the Value-at-Risk (VaR) forecasting ability of the normal GARCH model. Compared to the use of more sophisticated GARCH models, the new method is fast, easy to implement, numerically reliable, and,...