Title | Bayesian analysis of stochastic volatility-in-mean model with leverage and asymmetrically heavy-tailed error using generalized hyperbolic skew Student's t-distribution. |
Publication Type | Journal Article |
Year of Publication | 2017 |
Authors | Leão, William L., Carlos A. Abanto-Valle, and Ming-Hui Chen |
Journal | Stat Interface |
Volume | 10 |
Pagination | 529-541 |
Date Published | 2017 |
ISSN | 1938-7989 |
Abstract | A stochastic volatility-in-mean model with correlated errors using the generalized hyperbolic skew Student-t (GHST) distribution provides a robust alternative to the parameter estimation for daily stock returns in the absence of normality. An efficient Markov chain Monte Carlo (MCMC) sampling algorithm is developed for parameter estimation. The deviance information, the Bayesian predictive information and the log-predictive score criterion are used to assess the fit of the proposed model. The proposed method is applied to an analysis of the daily stock return data from the Standard & Poor's 500 index (S&P 500). The empirical results reveal that the stochastic volatility-in-mean model with correlated errors and GH-ST distribution leads to a significant improvement in the goodness-of-fit for the S&P 500 index returns dataset over the usual normal model. |
DOI | 10.4310/SII.2017.v10.n4.a1 |
Alternate Journal | Stat Interface |
Original Publication | Bayesian analysis of stochastic volatility-in-mean model with leverage and asymmetrically heavy-tailed error using generalized hyperbolic skew Student's t-distribution. |
PubMed ID | 29333210 |
PubMed Central ID | PMC5766051 |
Grant List | P01 CA142538 / CA / NCI NIH HHS / United States R01 GM070335 / GM / NIGMS NIH HHS / United States |
Bayesian analysis of stochastic volatility-in-mean model with leverage and asymmetrically heavy-tailed error using generalized hyperbolic skew Student's t-distribution.
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