Title :
Bayesian Analysis of Stock Index Return Volatility
Author :
Zhu, Huiming ; Yu, Keming
Author_Institution :
Coll. of Bus. Adm., Hunan Univ., Changsha
Abstract :
The stochastic volatility is a universal phenomenon in financial time series, and an important issue in risk management research. In this paper, through the statistical structure of the standard stochastic volatility model, we infer the SV model´s likelihood function, design the parameters´ conjugate prior distribution, obtain the corresponding model parameter according to the Bayesian theorem, and examine their condition distribution. Furthermore, in order to obtain the model parameter estimation and their confidence intervals, we use Gibbs sampling to devise an MCMC computational procedure, and carry out an empirical analysis using the Shanghai composite index and the Shenzhen component index data. The results indicate that the Bayesian method is an effective tool to explore the financial time series data.
Keywords :
Bayes methods; financial management; maximum likelihood estimation; sampling methods; statistical distributions; stochastic processes; stock markets; Bayesian analysis; Gibbs sampling; MCMC computational procedure; Shanghai composite index; Shenzhen component index; confidence intervals; conjugate prior distribution; financial time series; likelihood function; parameter estimation; risk management research; stochastic volatility model; stock index return volatility; Bayesian methods; Computational modeling; Educational institutions; Parameter estimation; Risk analysis; Risk management; Sampling methods; Stochastic processes; Stock markets; Time series analysis;
Conference_Titel :
Wireless Communications, Networking and Mobile Computing, 2008. WiCOM '08. 4th International Conference on
Conference_Location :
Dalian
Print_ISBN :
978-1-4244-2107-7
Electronic_ISBN :
978-1-4244-2108-4
DOI :
10.1109/WiCom.2008.2302