A Stochastic Volatility Model of Scale Mixtures Using Bayesian Analysis for Stock Index in China

2016 ◽  
Author(s):  
Special Issues Editor
Keyword(s):  
Bayesian Analysis ◽  
Stochastic Volatility ◽  
Stochastic Volatility Model ◽  
Stock Index ◽  
Scale Mixtures ◽  
Volatility Model

scholarly journals BAYESIAN INFERENCE OF STOCHASTIC VOLATILITY MODEL BY HYBRID MONTE CARLO

2009 ◽  
Vol 18 (08) ◽  
pp. 1381-1396 ◽  
Author(s):  
TETSUYA TAKAISHI
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Bayesian Inference ◽  
Stochastic Volatility ◽  
Metropolis Algorithm ◽  
Stochastic Volatility Model ◽  
Financial Data ◽  
Stock Index ◽  
Hybrid Monte Carlo ◽  
Volatility Model

The hybrid Monte Carlo (HMC) algorithm is applied for the Bayesian inference of the stochastic volatility (SV) model. We use the HMC algorithm for the Markov chain Monte Carlo updates of volatility variables of the SV model. First we compute parameters of the SV model by using the artificial financial data and compare the results from the HMC algorithm with those from the Metropolis algorithm. We find that the HMC algorithm decorrelates the volatility variables faster than the Metropolis algorithm. Second we make an empirical study for the time series of the Nikkei 225 stock index by the HMC algorithm. We find the similar correlation behavior for the sampled data to the results from the artificial financial data and obtain a ϕ value close to one (ϕ ≈ 0.977), which means that the time series has the strong persistency of the volatility shock.

Estimation of SV Model with Leverage Effect Based on MCMC Technique

2014 ◽  
Vol 530-531 ◽  
pp. 605-608
Author(s):  
Xiao Cui Yin
Keyword(s):  
Monte Carlo ◽  
Stochastic Volatility ◽  
Stochastic Volatility Model ◽  
Stock Index ◽  
Leverage Effect ◽  
Model Parameters ◽  
Convergence Diagnostics ◽  
Volatility Model ◽  
Mcmc Simulation ◽  
Series Convergence

This paper is to study the estimation of stochastic volatility model with leverage effect using Bayesian approach and Markov Chain Monte Carlo (MCMC) simulation technique. The data used is China's Shenzheng stock index. Estimations of model parameters are achieved by using MCMC technique in Openbugs Software, results show that there is leverage effect in Shenzheng stock series, convergence diagnostics suggest that parameters of the model are convergent.

scholarly journals Forecasting Return Volatility of the CSI 300 Index Using the Stochastic Volatility Model with Continuous Volatility and Jumps

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Xu Gong ◽  
Zhifang He ◽  
Pu Li ◽  
Ning Zhu
Keyword(s):  
Stochastic Volatility ◽  
Prediction Accuracy ◽  
Financial Risk ◽  
Sample Path ◽  
Realized Volatility ◽  
Stochastic Volatility Model ◽  
Stock Index ◽  
Return Volatility ◽  
Volatility Model ◽  
Continuous Volatility

The logarithmic realized volatility is divided into the logarithmic continuous sample path variation and the logarithmic discontinuous jump variation on the basis of the SV-RV model in this paper, which constructs the stochastic volatility model with continuous volatility (SV-CJ model). Then, we use high-frequency transaction data for five minutes of the CSI 300 stock index as the study sample, which, respectively, make parameter estimation on the SV, SV-RV, and SV-CJ model. We also comparatively analyze these three models' prediction accuracy by using the loss functions and SPA test. The results indicate that the prior logarithmic realized volatility and the logarithmic continuous sample path variation can be used to predict the future return volatility in China's stock market, while the logarithmic discontinuous jump variation is poor at its prediction accuracy. Besides, the SV-CJ model has an obvious advantage over the SV and SV-RV model as to the prediction accuracy of the return volatility, and it is more suitable for the research concerning the problems of financial practice such as the financial risk management.

Bayesian Analysis of a Threshold Stochastic Volatility Model

2016 ◽  
Vol 35 (5) ◽  
pp. 462-476 ◽  
Author(s):  
Tony S. Wirjanto ◽  
Adam W. Kolkiewicz ◽  
Zhongxian Men
Keyword(s):  
Bayesian Analysis ◽  
Stochastic Volatility ◽  
Stochastic Volatility Model ◽  
Volatility Model
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