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Kim, J. (2005). Parameter Estimation in Stochastic Volatility Models with Missing Data Using Particle Methods and the Emalgorithm. Doctoral Dissertation, University of Pittsburgh.
http://d-scholarship.pitt.edu/8265/

has been cited by the following article:

• TITLE: Modelling Stochastic Volatility in the Kenyan Securities Market Using Hidden Markov Models

  AUTHORS: Matilda B. Bosire, Samuel Chege Maina

  KEYWORDS: Hidden Markov Models, Stochastic Volatility, Nairobi Securities Exchange 20 (NSE 20) Share Index, Volatility Regimes

  JOURNAL NAME: Journal of Financial Risk Management, Vol.10 No.3, September 30, 2021

  ABSTRACT: This paper models stochastic volatility using Hidden Markov Models in Kenya. The univariate Stochastic volatility Model is calibrated to the Nairobi Securities Exchange 20 share index daily data from January 2012 to February 2021. The Hidden Markov model (HMM) is employed to establish volatility regimes while the Expected Maximization (EM) algorithm is applied in parameter estimation. Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC) techniques are employed in filtering out noisy observations in parameter estimation. The 4-state model, which divides the economy into periods of very high, high, low, and very low volatility, is established to be optimal.