| Project/Area Number |
18330039
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Economic statistics
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| Research Institution | The University of Tokyo |
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Principal Investigator |
OMORI Yasuhiro The University of Tokyo, Faculty of Economics, Associate Professor (60251188)
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| Co-Investigator(Kenkyū-buntansha) |
ISHIDA Isao The University of Tokyo, Graduate School of Economics, Assistant Professor (20361579)
WAGO Hajime Kyoto Sangyo University, Faculty of Economics, Professor (00091934)
WATANABE Toshiaki Hitotsubashi University, Institute of Economic Reseacrh, Professor (90254135)
KOZUMI Hideo Kobe University, Graduate School of Business Administration, Professor (10261273)
OGA Takashi Chiba University, Faculty of Law and Economics, Associate Professor (50326005)
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| Project Period (FY) |
2006 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥6,180,000 (Direct Cost: ¥5,400,000、Indirect Cost: ¥780,000)
Fiscal Year 2007: ¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2006: ¥2,800,000 (Direct Cost: ¥2,800,000)
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| Keywords | 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 / ボラティリティ変動モデル / カウントデータ |
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| Research Abstract |
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
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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
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