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.