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Bayesian econometric analysis of semiparametirc model

半参数模型的贝叶斯计量经济学分析

基本信息

批准号：
18330039
负责人：
OMORI Yasuhiro
金额：
$ 3.96万
依托单位：
The University of Tokyo
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2006
资助国家：
日本
起止时间：
2006 至 2007
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-18330039/
关键词：
Markov chain Monte Carlo Bayesian Statistics Stochastic volatility Latent variable Sample selection model Endogenous switching model Realized Volatjlity Ultra high frequency data 確率的ボラティリティサンプル・セレクションモデル確率的フロンティアモデルマルコフ切替モデルボラテ

项目摘要

In this research project, we construct Bayesian econometric models for financial time series, macro-economic time series and socio-economic panel data, and proposed efficient estimation methods using Markov chain Monte Carlo (MCMC) methods. Omori considered stochastic volatility models with leverage effects, jumps and heavy-tailed error distributions and proposed two highly efficient estimation methods using MCMC. Further, the models are extended to the multivariate factor stochastic volatility models. Omori also derived the effective MCMC acceleration step to improve the convergence rate of Gibbs sampler for the well-known sample selection models.Ishida conducted empirical studies of the realized volatilities computed from ultra high frequency data for Nikkei 225, and found that there exist a mean reversion, along memory property and a time-varying volatility in the time series of realized volatilities.Wago estimated the spatio-temporal model for the panel data of the crime rates in J … More apan using Bayesian approach and conducted model selections. Watanabe conducted a literature survey on the realized volatility (known as a nonparametric estimator of volatility of asset returns) and showed that realized volatilities are useful to predict future volatilities. Model-free implied volatilities for the Nikkei 225 stock index are analyzed using option prices. Furthermore, Watanabe estimated ARCH models using daily returns and ARFIMAX models (with long memory property) using realized volatilities, and compared the model performances in predicting the volatilities and the evaluation of VaR.Kozumi considered alternative specifications of endogenous switching models which combine probit models and Poisson regression models, and derived efficient estimation methods using MCMC. Further, he proposed the MCMC estimation method for stochastic frontier model with latent gamma variables using the data augmentation. Oga applied Markov switching model to the differenced composite indices using Bayesian approach, and also proposed a model to detect asymmetry in recessions and expansions in business cycles in Japan. Less

本研究针对金融时间序列、宏观经济时间序列和社会经济面板数据构建贝叶斯计量经济模型，并提出基于马尔可夫链蒙特卡罗（MCMC）方法的有效估计方法。Omori考虑了具有杠杆效应、跳跃和重尾误差分布的随机波动模型，并提出了两种基于MCMC的高效估计方法。进一步，将模型推广到多变量因子随机波动模型。大森还推导出了有效的MCMC加速步长，以提高吉布斯采样器对著名样本选择模型的收敛速度。石田对日经225指数的超高频数据计算的已实现波动率进行了实证研究，发现存在均值回复，在已实现波动率的时间序列中存在沿着记忆性和随时间变化的波动率。日本犯罪率面板数据的时间模型 ...更多信息 apan使用贝叶斯方法和进行模型选择。Watanabe对已实现波动率（称为资产收益波动率的非参数估计）进行了文献调查，并表明已实现波动率对预测未来波动率是有用的。使用期权价格分析了日经225指数的无模型隐含波动率。此外，Watanabe对使用日收益率的ARFIMAX模型和使用已实现波动率的ARFIMAX模型（具有长记忆性）进行了估计，并比较了模型在预测波动率和评估VaR方面的性能。Kozumi考虑了将联合收割机probit模型和Poisson回归模型相结合的内生转换模型的替代规格，并使用MCMC导出了有效的估计方法。此外，他还提出了基于数据增广的带潜在伽玛变量的随机前沿面模型的MCMC估计方法。Oga使用贝叶斯方法将马尔可夫转换模型应用于差分综合指数，并提出了一个模型来检测日本经济周期衰退和扩张的不对称性。少