Volatility has been one of the most active and successful areas of research in time series
econometrics and economic forecasting in recent decades. This chapter provides a selective …

Although stochastic volatility (SV) models have an intuitive appeal, their empirical
application has been limited mainly due to difficulties involved in their estimation. The main …

1.1 Introduction Research into time series models of changing variance and covariance,
which I will collectively call volatility models, has exploded in the last ten years. This activity …

In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a
unified, practical likelihood-based framework for the analysis of stochastic volatility models …

This paper develops consistent model and moment selection criteria for GMM estimation.
The criteria select the correct model specification and all correct moment conditions …

We propose using the price range in the estimation of stochastic volatility models. We show
theoretically, numerically, and empirically that range‐based volatility proxies are not only …

We describe an intuitive, simple, and systematic approach to generating moment conditions
for generalized method of moments (GMM) estimation of the parameters of a structural …

The paper provides a survey of the work that has been done in financial econometrics in the
past decade. It proceeds by first establishing a set of stylized facts that are characteristics of …

This paper extends the class of stochastic volatility diffusions for asset returns to encompass
Poisson jumps of time‐varying intensity. We find that any reasonably descriptive continuous …

This paper evaluates the role of various volatility specifications, such as multiple stochastic
volatility (SV) factors and jump components, in appropriate modeling of equity return …