Modelling volatility by variance decomposition

Forecasting realized exchange rate volatility by decomposition

We compare forecasts of the realized volatility of the exchange rate returns of the Euro against the U.S. Dollar and the Japanese Yen obtained both directly and through decomposition. Decomposing the realized volatility into its continuous sample path and jump components, and modeling and forecasting them separately instead of directly forecasting the realized volatility, is shown to lead to im...

Stochastic volatility of volatility and variance risk premia

This paper introduces a new class of stochastic volatility models which allows for stochastic volatility of volatility (SVV): Volatility modulated non–Gaussian Ornstein–Uhlenbeck (VMOU) processes. Various probabilistic properties of (integrated) VMOU processes are presented. Further we study the effect of the SVV on the leverage effect and on the presence of long memory. One of the key results ...

Practical Bias Variance Decomposition

Bias variance decomposition for classifiers is a useful tool in understanding classifier behavior. Unfortunately, the literature does not provide consistent guidelines on how to apply a bias variance decomposition. This paper examines the various parameters and variants of empirical bias variance decompositions through an extensive simulation study. Based on this study, we recommend to use ten ...

Modelling Bilinear Stochastic Volatility

Some relationships between ARCH-type and Stochastic Volatility models are investigated. New model formulations are derived through a transformation of a GARCH-M process and the name Generalized Bilinear Stochastic Volatility is suggested. Markovian-type representations are presented and estimation algorithms are proposed.

Modelling and measuring volatility∗