Often researchers find parametric models restrictive and sensitive to deviations from the
parametric specifications; semi-nonparametric models are more flexible and robust, but lead …

Publisher Summary The class of stochastic volatility (SV) models has its roots in both,
mathematical finance and financial econometrics. In fact, several variations of SV models …

This book provides the most comprehensive treatment to date of microeconometrics, the
analysis of individual-level data on the economic behavior of individuals or firms using …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

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 …

In this paper we present inference methods which are based on an 'incorrect'criterion, in the
sense that the optimization of this criterion does not directly provide a consistent estimator of …

Non‐Gaussian processes of Ornstein–Uhlenbeck (OU) type offer the possibility of capturing
important distributional deviations from Gaussianity and for flexible modelling of …

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

New techniques for the analysis of stochastic volatility models in which the logarithm of
conditional variance follows an autoregressive model are developed. A cyclic Metropolis …