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Econometric Analysis of Stock Markets in Japan using Models of Changing Volatility

使用波动率变化模型对日本股票市场进行计量经济学分析

基本信息

批准号：
15530221
负责人：
WATANABE Toshiaki
金额：
$ 2.18万
依托单位：
Tokyo Metropolitan University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2003
资助国家：
日本
起止时间：
2003 至 2004
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-15530221/
关键词：
Volatility Stochastic Volatility Model Markov Chain Monte Carlo Bayesian Estimation Multi-move sampler GARCH Option Trading Volume マルコフ連鎖モンテカルロ法 Realized Volatility

项目摘要

1. Development of Models of Changing VolatilityIn a MCMC (Markov Chain Monte Carlo) Bayesian analysis of stochastic volatility models, we must sample the latent volatilities from their posterior distribution.. One efficient method for sampling volatilities is the multi-move sampler proposed by Shephard and Pitt (1997). We showed that their method is incorrect and we proposed a correct multi-move sampler We also developed a MCMC Bayesian method far the analysis of extended stochastic volatility models such as a stochastic volatility model with non-normal errors, a Markov switching stochastic volatility model and a dynamic bivariate mixture model We also develop a MCMC Bayesian method for the analysis of GARCH models. This method enables us to forecast future volatilities and evaluate option prices considering the estimation errors of GARCH parameters.2. EmpiricalAnalysis of Stock Markets in JapanStochastic volatility model usually assume that the distribution of asset returns conditional on the latent volatility is normal We showed that t distribution fits TOPIX better than the normal and other distributions such as the GED and the normal mixture. We also showed that the Markov switching model that allows for a shift in the mean of volatility is favored over the standard stochastic volatility model using weekly returns of the TOPDL We also showed that the dynamic bivariate mixture models proposed by Thuchen and Pitts (1983) and Andersen (1996) cannot fully explain t behavior of prioe and trading volume in the Nikkei 225 stock index futures market3. Option Price Evaluation using Models of Changing VolatilityWe develop a MCMC Bayesian method for evaluating toption price when the price of underlying asset follows a GARCH model and showed that this method performed well in the evaluation of the price of Nikkei 225 option.

1.在随机波动率模型的MCMC（Markov Chain Monte Carlo）贝叶斯分析中，我们必须从其后验分布中抽取潜在波动率。Shephard和Pitt（1997年）提出的一种有效的挥发性物质取样方法是多次移动取样器。我们证明了他们的方法是不正确的，我们提出了一个正确的多步抽样。我们还发展了MCMC贝叶斯方法，用于分析扩展的随机波动率模型，如具有非正态误差的随机波动率模型，马尔可夫切换随机波动率模型和动态二元混合模型。该方法使我们能够预测未来的波动率和评估期权价格考虑估计误差的Gestival参数.日本股票市场的实证分析随机波动率模型通常假设资产收益率在潜在波动率的条件下服从正态分布我们证明了t分布比正态分布和其他分布如GED和正态混合分布更适合TOPIX。我们还表明，马尔可夫转换模型，允许在波动率的平均值的移动是优于标准的随机波动率模型使用的TOPDL的周收益率。我们还表明，动态二元混合模型提出的Thuchen和Pitts（1983）和Andersen（1996）不能完全解释日经225股票指数期货市场的价格和交易量的行为3。基于波动率变化模型的期权价格评估我们提出了一种MCMC贝叶斯方法，用于评估标的资产价格遵循GARCH模型时的期权价格，并在日经225期权的价格评估中显示了该方法的良好性能。