This chapter reviews the recent developments in Markov chain Monte Carlo simulation
methods. These methods, which are concerned with the simulation of high dimensional …

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 …

This paper is concerned with simulation-based inference in generalized models of
stochastic volatility defined by heavy-tailed Student-t distributions (with unknown degrees of …

Stochastic volatility is the main concept used in the fields of financial economics and
mathematical finance to deal with time-varying volatility in financial markets. This book …

Many complex dynamical phenomena can be effectively modeled by a system that switches
among a set of conditionally linear dynamical modes. We consider two such models: the …

In the paper we study the relationship between macroeconomic and stock market volatility,
using S&P500 data for the period 1970–2001. We find evidence of a twofold linkage …

Along with many practical applications, this book presents an array of Bayesian inference
and model selection procedures. It thoroughly explains the concepts, illustrates the …

In this paper we review sequential Monte Carlo (SMC) methods, or particle filters (PF), with
special emphasis on its potential applications in financial time series analysis and …

Bayesian Methods in Finance provides a detailed overview of the theory of Bayesian
methods and explains their real-world applications to financial modeling. While the …

In this paper we propose a flexible model to describe nonlinearities and long-range
dependence in time series dynamics. The new model is a multiple regime smooth transition …