Leverage, heavy-tails and correlated jumps in stochastic volatility models

Authors:
Jouchi Nakajima;Yasuhiro Omori
Affiliations:
Bank of Japan, Tokyo 103-8660, Japan;Faculty of Economics, University of Tokyo, Tokyo 113-0033, Japan
Venue:
Computational Statistics & Data Analysis
Year:
2009

Citing 3
Cited 5

Likelihood-based inference for asymmetric stochastic volatility models

Computational Statistics & Data Analysis - Special issue: Computational econometrics
Block sampler and posterior mode estimation for asymmetric stochastic volatility models

Computational Statistics & Data Analysis
Comparing stochastic volatility models through Monte Carlo simulations

Computational Statistics & Data Analysis

Forecasting volatility under fractality, regime-switching, long memory and student-t innovations

Computational Statistics & Data Analysis
Stochastic volatility model with leverage and asymmetrically heavy-tailed error using GH skew Student's t-distribution

Computational Statistics & Data Analysis
Efficient Bayesian estimation of a multivariate stochastic volatility model with cross leverage and heavy-tailed errors

Computational Statistics & Data Analysis
On marginal likelihood computation in change-point models

Computational Statistics & Data Analysis
A Bayesian semiparametric model for volatility with a leverage effect

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.03

Visualization

Abstract

Efficient and fast Markov chain Monte Carlo estimation methods for the stochastic volatility model with leverage effects, heavy-tailed errors and jump components, and for the stochastic volatility model with correlated jumps are proposed. The methods are illustrated using simulated data and are applied to analyze daily stock returns data on S&P500 index and TOPIX. Model comparisons are conducted based on the marginal likelihood for various SV models including the superposition model.