Nakatsuma, Teruo

写真a

Affiliation

Faculty of Economics （Mita）

Position

Professor

External Links
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Other Affiliation 【 Display / hide 】

  • Dean, Graduate School of Economics

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Career 【 Display / hide 】

  • 1998.04
    -
    2000.03

    一橋大学経済研究所 ,専任講師

  • 1998.04
    -
    2000.03

    一橋大学経済研究所 ,専任講師

  • 2000.04
    -
    2003.03

    大学専任講師(経済学部)

  • 2000.04
    -
    2003.03

    大学専任講師(経済学部)

  • 2003.04
    -
    2010.03

    大学准教授（経済学部）

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Academic Background 【 Display / hide 】

  • 1991.03

    University of Tsukuba, 第3学群社会工学類

  • 1991.03

    University of Tsukuba, 第3学群社会工学類

    University, Graduated

  • 1994.03

    University of Tsukuba, Graduate School, Division of Social Engineering

  • 1994.03

    University of Tsukuba, Graduate School, Division of Social Engineering

    Graduate School, Completed, Master's course

  • 1995.10

    Rutgers University, Graduate School of Economics, Econometrics

    United States

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Academic Degrees 【 Display / hide 】

  • Ph.D.（経済学）, ラトガーズ大学

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Research Areas 【 Display / hide 】

  • Humanities & Social Sciences / Economic statistics (Econometrics)

  • Humanities & Social Sciences / Economic statistics (Econometrics)

  • Informatics / Statistical science (Bayesian Statistics)

  • Informatics / Statistical science (Bayesian Statistics)

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Books 【 Display / hide 】

  • Pythonによる計量経済学入門

    中妻照雄, 朝倉書店, 2020.11,  Page： 214

  • フィンテックの経済学

    嘉治佐保子, 中妻照雄, 福原正大, 慶應義塾大学出版会, 2019.08,  Page： 292

  • Pythonによるベイズ統計学入門

    中妻照雄, 朝倉書店, 2019.04,  Page： 214

  • Pythonによるファイナンス入門

    中妻照雄, 朝倉書店, 2018.02,  Page： 168

  • 実践ベイズ統計学

    NAKATSUMA TERUO, 朝倉書店, 2013.01

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Papers 【 Display / hide 】

  • Stochastic Conditional Duration Model with Intraday Seasonality and Limit Order Book Information

    Toyabe T, Nakatsuma T

    Journal of Risk and Financial Management （Journal of Risk and Financial Management)  15 （ 10 ）  2022.10

    　View Summary

    It is a widely known fact that the intraday seasonality of trading intervals for financial transactions such as stocks is short at the beginning of business hours and long in the middle of the day. In this paper, we extend the stochastic conditional duration (SCD) model to capture the pattern of intraday trading intervals and propose a new Markov chain Monte Carlo method to estimate this intraday seasonality simultaneously. To efficiently generate the Monte Carlo sample, we used a hybrid of the Gibbs/Metropolis–Hastings (MH) sampling scheme and also applied generalized Gibbs sampling. In addition to capturing this intraday seasonality, this paper also considers limit order book information. Three-day tick data for three stocks obtained from Nikkei NEEDS are used for estimation, and model selection is performed on smooth parameters, Weibull distribution and Gamma distribution. The typical intraday regularity of frequent trading immediately after the start of trading is confirmed, and the spread of the limit order book information is also found to affect the trading time interval.

  • A positive-definiteness-assured block Gibbs sampler for Bayesian graphical models with shrinkage priors

    Oya S, Nakatsuma T

    Japanese Journal of Statistics and Data Science （Japanese Journal of Statistics and Data Science)  5 （ 1 ） 149 - 164 2022.07

    　View Summary

    Although the block Gibbs sampler for the Bayesian graphical LASSO proposed by Wang (2012) has been widely applied and extended to various shrinkage priors in recent years, it has a less noticeable but possibly severe disadvantage that the positive definiteness of a precision matrix in the Gaussian graphical model is not guaranteed in each cycle of the Gibbs sampler. Specifically, if the dimension of the precision matrix exceeds the sample size, the positive definiteness of the precision matrix will be barely satisfied and the Gibbs sampler will almost surely fail. In this paper, we propose modifying the original block Gibbs sampler so that the precision matrix never fails to be positive definite by sampling it exactly from the domain of the positive definiteness. As we have shown in the Monte Carlo experiments, this modification not only stabilizes the sampling procedure but also significantly improves the performance of the parameter estimation and graphical structure learning. We also apply our proposed algorithm to a graphical model of the monthly return data in which the number of stocks exceeds the sample period, demonstrating its stability and scalability.

  • Hierarchical Bayesian hedonic regression analysis of Japanese rice wine: is the price right?

    Saito W., Nakatsuma T.

    International Journal of Wine Business Research （International Journal of Wine Business Research)   2022

    ISSN  17511062

    　View Summary

    Purpose: This paper aims to formulate a hedonic pricing model for Japanese rice wine, sake, via hierarchical Bayesian modeling estimated using an efficient Markov chain Monte Carlo (MCMC) method. Using the estimated model, the authors examine how producing regions, rice breeds and taste characteristics affect sake prices. Design/methodology/approach: The datasets in the estimation consist of cross-sectional observations of 403 sake brands, which include sake prices, taste indicators, premium categories, rice breeds and regional dummy variables. Data were retrieved from Rakuten, Japan’s largest online shopping site. The authors used the Bayesian estimation of the hedonic pricing model and used an ancillarity–sufficiency interweaving strategy to improve the sampling efficiency of MCMC. Findings: The estimation results indicate that Japanese consumers value sweeter sake more, and the price of sake reflects the cost of rice preprocessing only for the most-expensive category of sake. No distinctive differences were identified among rice breeds or producing regions in the hedonic pricing model. Originality/value: To the best of the authors’ knowledge, this study is the first to estimate a hedonic pricing model of sake, despite the rich literature on alcoholic beverages. The findings may contribute new insights into consumer preference and proper pricing for sake breweries and distributors venturing into the e-commerce market.

  • Bayesian Analysis of Intraday Stochastic Volatility Models of High-Frequency Stock Returns with Skew Heavy-Tailed Errors

    Nakakita, Makoto and Nakatsuma, Teruo

    Journal of Risk and Financial Management 14 （ 4 ） 145 2021.03

    Research paper (scientific journal), Joint Work, Accepted

  • Volatility forecasts using stochastic volatility models with nonlinear leverage effects

    McAlinn K, Ushio A, Nakatsuma T

    Journal of Forecasting （Journal of Forecasting)  39 （ 2 ） 143 - 154 2020.03

    Research paper (scientific journal), Joint Work, Accepted,  ISSN  02776693

    　View Summary

    © 2019 John Wiley & Sons, Ltd. The leverage effect—the correlation between an asset's return and its volatility—has played a key role in forecasting and understanding volatility and risk. While it is a long standing consensus that leverage effects exist and improve forecasts, empirical evidence puzzlingly does not show that this effect exists for many individual stocks, mischaracterizing risk, and therefore leading to poor predictive performance. We examine this puzzle, with the goal to improve density forecasts, by relaxing the assumption of linearity of the leverage effect. Nonlinear generalizations of the leverage effect are proposed within the Bayesian stochastic volatility framework in order to capture flexible leverage structures. Efficient Bayesian sequential computation is developed and implemented to estimate this effect in a practical, on-line manner. Examining 615 stocks that comprise the S&P500 and Nikkei 225, we find that our proposed nonlinear leverage effect model improves predictive performances for 89% of all stocks compared to the conventional stochastic volatility model.

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Papers, etc., Registered in KOARA 【 Display / hide 】

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Reviews, Commentaries, etc. 【 Display / hide 】

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Presentations 【 Display / hide 】

  • Bayesian analysis of intraday stochastic volatility models with skew heavy-tailed error and smoothing spline seasonality

    Teruo Nakatsuma

    Bayesian analysis of intraday stochastic volatility models with skew heavy-tailed error and smoothing spline seasonality (University of Pisa, Italy) , 

    2018.12
    , 

    Oral presentation (general), 12th International Conference on Computational and Financial Econometrics

  • Bayesian analysis of intraday stochastic volatility models with leverage and skew heavy-tailed error

    Teruo Nakatsuma

    11th International Conference on Computational and Financial Econometrics (University of London, UK) , 

    2017.12
    , 

    Oral presentation (general)

  • Hierarchical Bayes Modeling of Autocorrelation and Intraday Seasonality in Financial Durations

    NAKATSUMA TERUO

    10th International Conference on Computational and Financial Econometrics (University of Seville, Spain) , 

    2016.12
    , 

    Oral presentation (general), CFEnetwork

  • Hierarchical Bayes Modeling of Autocorrelation and Intraday Seasonality in Financial Durations

    NAKATSUMA TERUO

    International Society for Bayesian Analysis (ISBA) World Meeting 2016 (Sardinia, Italy) , 

    2016.06
    , 

    Poster presentation, International Society for Bayesian Analysis (ISBA)

  • Nonlinear Leverage Effects in Asset Returns Evidence from the U.S. and Japanese Stock Markets

    NAKATSUMA TERUO

    9th International Conference on Computational and Financial Econometrics (London, U.K.) , 

    2015.12
    , 

    Oral presentation (general)

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Research Projects of Competitive Funds, etc. 【 Display / hide 】

  • Bayesian Time Series Analysis of Limit Order Processes in Financial Markets

    2019.04
    -
    2022.03

    MEXT,JSPS, Grant-in-Aid for Scientific Research, 中妻 照雄, Grant-in-Aid for Scientific Research (C), Principal investigator

  • データ駆動型アプローチによる高頻度での金融資産価格形成メカニズムの研究

    2016.04
    -
    2019.03

    MEXT,JSPS, Grant-in-Aid for Scientific Research, 中妻 照雄, Grant-in-Aid for Scientific Research (C), Principal investigator

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Courses Taught 【 Display / hide 】

  • THEORY AND PRACTICE OF FINTECH

    2022

  • STARTUPS AND BUSINESS INNOVATION

    2022

  • SEMINAR: ECONOMETRICS

    2022

  • RESEARCH SEMINAR D

    2022

  • RESEARCH SEMINAR C

    2022

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Courses Previously Taught 【 Display / hide 】

  • ECONOMETRICS

    Keio University

    2022.04
    -
    2023.03

  • STARTUPS AND BUSINESS INNOVATION

    Keio University

    2022.04
    -
    2023.03

  • INTRODUCTION TO DATA-DRIVEN FINANCE

    Keio University

    2021.04
    -
    2022.03

  • SEMINAR: ECONOMETRICS

    Keio University

    2019.04
    -
    2020.03

  • GENERAL EDUCATION SEMINAR

    Keio University

    2019.04
    -
    2020.03

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