Bayesian estimation of Multivariate Stochastic Volatility by applying state space models

dc.contributor.author	Agasa, Lameck
dc.contributor.author	Ombasa, Kiame
dc.date.accessioned	2021-05-11T11:46:18Z
dc.date.available	2021-05-11T11:46:18Z
dc.date.issued	2017
dc.description	Paper presented at the 4th Strathmore International Mathematics Conference (SIMC 2017), 19 - 23 June 2017, Strathmore University, Nairobi, Kenya.	en_US
dc.description.abstract	This work seeks to apply a Bayesian analysis in estimating multivariate stochastic volatility (MSV) using state space models. A multiplicative model based on inverted Wishart and multivariate singular beta distributions is proposed for the evolution of the volatility, and a flexible sequential volatility updating is employed. Being computationally fast, the resulting estimation procedure is particularly suitable for on-line forecasting. Bayesian MCMC is applied to estimate high dimensional problems. Three test are conducted on estimates: the log likelihood criterion, the mean of standardized one-step forecast errors, and sequential Bayes factors. The test and procedure are applied in real data set that will comprise ten exchange rate Kenyan shillings versus other currencies in Nairobi stock exchange.	en_US
dc.identifier.uri	http://hdl.handle.net/11071/11816
dc.language.iso	en	en_US
dc.publisher	Strathmore University	en_US
dc.subject	Multivariate Stochastic Volatility (MSV)	en_US
dc.subject	State space models	en_US
dc.title	Bayesian estimation of Multivariate Stochastic Volatility by applying state space models	en_US
dc.type	Article	en_US

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