Comparison of asymmetric stochastic volatility models under different correlation structures
| Autor: | Tony S. Wirjanto, Don McLeish, Zhongxian Men, Adam W. Kolkiewicz |
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| Rok vydání: | 2016 |
| Předmět: |
Statistics and Probability
050208 finance Logarithm Stochastic volatility Estimation theory 05 social sciences Inference Markov chain Monte Carlo Bayesian inference symbols.namesake Constant elasticity of variance model 0502 economics and business symbols Econometrics Leverage (statistics) Statistics Probability and Uncertainty 050205 econometrics Mathematics |
| Zdroj: | Journal of Applied Statistics. 44:1350-1368 |
| ISSN: | 1360-0532 0266-4763 |
| DOI: | 10.1080/02664763.2016.1204596 |
| Popis: | This paper conducts simulation-based comparison of several stochastic volatility models with leverage effects. Two new variants of asymmetric stochastic volatility models, which are subject to a logarithmic transformation on the squared asset returns, are proposed. The leverage effect is introduced into the model through correlation either between the innovations of the observation equation and the latent process, or between the logarithm of squared asset returns and the latent process. Suitable Markov Chain Monte Carlo algorithms are developed for parameter estimation and model comparison. Simulation results show that our proposed formulation of the leverage effect and the accompanying inference methods give rise to reasonable parameter estimates. Applications to two data sets uncover a negative correlation (which can be interpreted as a leverage effect) between the observed returns and volatilities, and a negative correlation between the logarithm of squared returns and volatilities. |
| Databáze: | OpenAIRE |
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