Essentials of Time Series for Financial Applications

Chapter 7

Multifactor Heteroskedastic Models, Stochastic Volatility

Abstract

This chapter describes the concepts and methods related to the modeling of stochastic volatility (SV), that is, the occurrence of time heterogeneity in the variances and covariances of asset returns. Section 7.2 explains how to deal with the latent nature of SV using, in particular, the Kalman filter to propose methods of inference. Section 7.3 illustrates how these techniques can be applied to estimate simple SV models using maximum likelihood methods and presents alternative approaches that overcome the major limitations of the linear Kalman filter. Section 7.4 presents a few leading extensions of the SV model described in the recent literature. Finally, Section 7.5 ...