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  • 9 - Methods for Estimating Discrete-Time Stochastic Volatility Models

  • from Part III - Bayesian Estimation and Inferences
  • Edited by Shuping Shi, Macquarie University, Sydney, Xiaohu Wang, Fudan University, Shanghai, Tao Zeng, Zhejiang University, China
  • Book:
    Financial Econometrics
    Published online:
    20 February 2025
    Print publication:
    27 February 2025, pp 271-305

  • 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.