Bayesian Estimation of a Skew-Student-t Stochastic Volatility Model
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| 024 | 7 | 0 | |a 10.1007/s11009-013-9389-9 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s11009-013-9389-9 | ||
| 245 | 0 | 0 | |a Bayesian Estimation of a Skew-Student-t Stochastic Volatility Model |h [Elektronische Daten] |c [C. Abanto-Valle, V. Lachos, Dipak Dey] |
| 520 | 3 | |a In this paper we present a stochastic volatility (SV) model assuming that the return shock has a skew-Student-t distribution. This allows a parsimonious, flexible treatment of skewness and heavy tails in the conditional distribution of returns. An efficient Markov chain Monte Carlo (MCMC) algorithm is developed and used for parameter estimation and forecasting. The MCMC method exploits a skew-normal mixture representation of the error distribution with a gamma distribution as the mixing distribution. The developed methodology is applied to the NASDAQ daily index returns. Bayesian model selection criteria as well as out-of-sample forecasting in a value-at-risk (VaR) study reveal that the SV model based on skew-Student-t distribution provides significant improvement in model fit as well as prediction to the NASDAQ index data over the usual normal model. | |
| 540 | |a Springer Science+Business Media New York, 2013 | ||
| 690 | 7 | |a Markov chain Monte Carlo |2 nationallicence | |
| 690 | 7 | |a Non-Gaussian and nonlinear state space models |2 nationallicence | |
| 690 | 7 | |a Skew-Student-t |2 nationallicence | |
| 690 | 7 | |a Stochastic volatility |2 nationallicence | |
| 690 | 7 | |a Value-at-risk |2 nationallicence | |
| 700 | 1 | |a Abanto-Valle |D C. |u Department of Statistics, Federal University of Rio de Janeiro, CP 68530, CEP 21945-970, Rio de Janeiro, RJ, Brazil |4 aut | |
| 700 | 1 | |a Lachos |D V. |u Department of Statistics, Campinas State University, CP 6065, CEP 13083-859, Campinas, SP, Brazil |4 aut | |
| 700 | 1 | |a Dey |D Dipak |u Department of Statistics, University of Connecticut, Storrs, CT, USA |4 aut | |
| 773 | 0 | |t Methodology and Computing in Applied Probability |d Springer US; http://www.springer-ny.com |g 17/3(2015-09-01), 721-738 |x 1387-5841 |q 17:3<721 |1 2015 |2 17 |o 11009 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s11009-013-9389-9 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s11009-013-9389-9 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Abanto-Valle |D C. |u Department of Statistics, Federal University of Rio de Janeiro, CP 68530, CEP 21945-970, Rio de Janeiro, RJ, Brazil |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Lachos |D V. |u Department of Statistics, Campinas State University, CP 6065, CEP 13083-859, Campinas, SP, Brazil |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Dey |D Dipak |u Department of Statistics, University of Connecticut, Storrs, CT, USA |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Methodology and Computing in Applied Probability |d Springer US; http://www.springer-ny.com |g 17/3(2015-09-01), 721-738 |x 1387-5841 |q 17:3<721 |1 2015 |2 17 |o 11009 | ||