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 】

  • 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.

  • 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.

  • Trading and Ordering Patterns of Market Participants in High Frequency Trading Environment: Empirical Study in the Japanese Stock Market

    Saito T, Adachi T, Nakatsuma T, Takahashi A, Tsuda H, Yoshino N

    Asia-Pacific Financial Markets （Asia-Pacific Financial Markets)  25 （ 3 ） 179 - 220 2018.09

    Research paper (scientific journal), Joint Work, Accepted,  ISSN  13872834

    　View Summary

    © 2018, Springer Japan KK, part of Springer Nature. In this study, we investigate ordering patterns of different types of market participants in Tokyo Stock Exchange (TSE) by examining order records of the listed stocks. Firstly, we categorize the virtual servers in the trading system of TSE, each of which is linked to a single trading participant, by the ratio of cancellation and execution in the order placement as well as the number of executions at the opening of the afternoon session. Then, we analyze ordering patterns of the servers in the categories in short intervals for the top 10 highest trading volume stocks. By classifying the intervals into four cases by returns, we observe how different types of market participants submit or execute orders in the market situations. Moreover, we investigate the shares of the executed volumes for the different types of servers in the swings and roundabouts of the Nikkei 225 index, which were observed in September in 2015. The main findings of this study are as follows: Server type A, which supposedly includes non-market making proprietary traders with high-speed algorithmic strategies, executes and places orders along with the direction of the market. The shares of the execution and order volumes along with the market direction increase when the stock price moves sharply. Server type B, which presumably includes servers employing a market making strategy with high cancellation and low execution ratio, shifts its market making price ranges in the rapid price movements. We observe that passive servers in Server type B have a large share and buy at low levels in the price falls. Also, Server type B, as well as Server type A, makes profit in the price falling days and particularly, the aggressive servers in the server type make most of the profit. Server type C, which is assumed to include servers receiving orders from small investors, constantly has a large share of execution and order volume.

  • ティックデータを用いた株式市場における約定予測

    NAKATSUMA TERUO

    ジャフィー・ジャーナル （朝倉書店)     94 - 127 2016

    Research paper (scientific journal), Joint Work, Accepted

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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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