COMPARING OF BAYESIAN MODEL SELECTION BASED ON MCMC METHOD AND FINANCE TIME SERIES (GARCH MODEL)

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SALEHIRAD M.R.; HABIBIFARD N.

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Paper Information

Journal: FINANCIAL KNOWLEDGE OF SECURITY ANALYSIS (FINANCIAL STUDIES) Year:2012 | Volume:5 | Issue:15 Page(s): 59-67

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Title

COMPARING OF BAYESIAN MODEL SELECTION BASED ON MCMC METHOD AND FINANCE TIME SERIES (GARCH MODEL)

Author(s)

SALEHIRAD M.R. | HABIBIFARD N. | Issue Writer Certificate

Keywords

BAYESIAN MODEL SELECTIONQ4

GARCH MODELQ2

MODEL LIKELIHOODQ4

MARKOV CHAIN MONTE CARLO (MCMC)Q4

Abstract

By using the time series models, we can analysis financial data (in last and future time). In financial discussions, because of heteroskedastic observations, we can not use the classical time series models.We focus on popular practical models for financial time series, GARCH- type models, that were introduced for the first time by Bollerslev (1986). These models represent a very wide class of heteroskedastic econometric models. Time series models (GARCH models too), like regression models, have random errors. These errors have specific distributions.Since that, the GARCH MODELs variability is not clear, thus, we use the BAYESIAN MODEL SELECTION methods to estimate the parameters of the model. In this method, by using the prior distributions on the parameters, we find the posterior distribution which has integral. Then, we can inference about the parameters.To explore the role of the posterior distribution, the most powerful technique is to use Markov Chain Monte Carlo (MCMC) computing methods such as the Gibbs sampler and the Metropolis Hasting (MH) algorithm. These algorithms enable to estimate the posterior distribution, but, they don't readily lend themselves to estimate aspects of the model probabilities. The most widely used one is the group of direct methods, such as the harmonic mean estimator, importance sampling and bridge sampling. Chib (1995 and 2001) proposed an indirect method for estimating MODEL LIKELIHOODs from Gibbs sampling output. This idea has recently been extended to the output of the MH algorithm.We use a reversible jump MCMC strategy for generating samples from the joint posterior distribution based on the standard MH approach.

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APA: Copy

SALEHIRAD, M.R., & HABIBIFARD, N.. (2012). COMPARING OF BAYESIAN MODEL SELECTION BASED ON MCMC METHOD AND FINANCE TIME SERIES (GARCH MODEL). FINANCIAL KNOWLEDGE OF SECURITY ANALYSIS (FINANCIAL STUDIES), 5(15), 59-67. SID. https://sid.ir/paper/200335/en

Vancouver: Copy

SALEHIRAD M.R., HABIBIFARD N.. COMPARING OF BAYESIAN MODEL SELECTION BASED ON MCMC METHOD AND FINANCE TIME SERIES (GARCH MODEL). FINANCIAL KNOWLEDGE OF SECURITY ANALYSIS (FINANCIAL STUDIES)[Internet]. 2012;5(15):59-67. Available from: https://sid.ir/paper/200335/en

IEEE: Copy

M.R. SALEHIRAD, and N. HABIBIFARD, “COMPARING OF BAYESIAN MODEL SELECTION BASED ON MCMC METHOD AND FINANCE TIME SERIES (GARCH MODEL),” FINANCIAL KNOWLEDGE OF SECURITY ANALYSIS (FINANCIAL STUDIES), vol. 5, no. 15, pp. 59–67, 2012, [Online]. Available: https://sid.ir/paper/200335/en

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