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

Autor:	Samuel Chege Maina, Matilda B. Bosire
Rok vydání:	2021
Předmět:	symbols.namesake Stochastic volatility Computer science Estimation theory Econometrics symbols Univariate Markov chain Monte Carlo Maximization Volatility (finance) Particle filter Hidden Markov model
Zdroj:	Journal of Financial Risk Management. 10:367-395
ISSN:	2167-9541 2167-9533
DOI:	10.4236/jfrm.2021.103021
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::8a1e0e7aaa40a7524a5911ae447ccfa1 https://doi.org/10.4236/jfrm.2021.103021 Zobrazit plný text záznamu