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9 - Methods for Estimating Discrete-Time Stochastic Volatility Models

from Part III - Bayesian Estimation and Inferences

Published online by Cambridge University Press:  20 February 2025

Edited by
Shuping Shi ,
Xiaohu Wang  and
Tao Zeng
Shuping Shi
Affiliation:
Macquarie University, Sydney
Xiaohu Wang
Affiliation:
Fudan University, Shanghai
Tao Zeng
Affiliation:
Zhejiang University, China
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Summary

This chapter reviews alternative methods proposed in the literature for estimating discrete-time stochastic volatility models and illustrates the details of their application. The methods reviewed are classified as either frequentist or Bayesian. The methods in the frequentist class include generalized method of moments, quasi-maximum likelihood, empirical characteristic function, efficient method of moments, and simulated maximum likelihood based on Laplace-based importance sampler. The Bayesian methods include single-move Markov chain Monte Carlo, multimove Markov chain Monte Carlo, and sequential Monte Carlo.

Keywords

stochastic volatility modeldiscrete timeestimationfrequentistBayesianMarkov chain Monte Carlo
Type
Chapter
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Financial Econometrics
Theory and Applications
 , pp. 271 - 305
Publisher: Cambridge University Press
Print publication year: 2025

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