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

  • 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）

  • 2010.04
    -
    Present

    大学教授（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

  • 1994.03

    University of Tsukuba, Graduate School, Division of Social Engineering

  • 1995.10

    Rutgers University, Graduate School of Economics, Econometrics

    United States

  • 1998.05

    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)

  • Informatics / Statistical science (Bayesian Statistics)

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Books 【 Display / hide 】

  • The Mechanism of HFT and Its Merits and Demerits—The Information Efficiency Challenge

    Nakatsuma T, The Economics of Fintech, 2021.01

  • Asset Management and Robo-Advisors

    Nakatsuma T, The Economics of Fintech, 2021.01

  • Machine Learning Principles and Applications

    Nakatsuma T, The Economics of Fintech, 2021.01

  • The Economics of Fintech

    Kaji S, Nakatsuma T, Fukuhara M, The Economics of Fintech, 2021.01

    　View Summary

    This book is a collection of academic lectures given on fintech, a topic that has been written about extensively but only from a business or technological point of view. In contrast to other publications on the subject, this book shows the reader how fintech should be understood in relation to economics, financial theory, policy, and law. It provides introductory explanations on fintech-related concepts and instruments such as blockchains, crypto assets, machine learning, high-frequency trading, and AI. The collected lectures also point to surrounding issues including start-ups, monetary policy, asset management, cyber and other security, and stability of financial systems. The authors include professors, a former central bank official, current officials at Japan's Financial Services Authority, a lawyer, the former dean of the Asian Development Bank Institute, and private sector professionals at the frontline of fintech. The book is most suitable for those both within and outside of academia who are beginning to learn about fintech and wish to successfully take part in the revolution that is certain to have wide-ranging effects on our economy and society.

  • Pythonによる計量経済学入門

    中妻照雄, 朝倉書店, 2020.11,  Page： 214

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Papers 【 Display / hide 】

  • Analysis of the trading interval duration for the Bitcoin market using high-frequency transaction data

    Nakakita M., Nakatsuma T.

    Quantitative Finance and Economics 9 ( 1 ) 202 - 241 2025.03

    Research paper (scientific journal), Joint Work, Last author, Accepted

    　View Summary

    Analyzing the trading interval durations of cryptocurrencies is important both academically and practically, but there has been no previous research using tick data. Therefore, we conducted a time series analysis on the duration of the trading interval between consecutive transactions in the Bitcoin market to identify similarities and differences with conventional financial assets such as stocks and commodities. We applied high-frequency transaction tick data from the Bitcoin market to a stochastic conditional duration (SCD) model and estimated the effects of trade price changes and volumes on the trading interval duration simultaneously with the intraday seasonality of the durations. As a result, we captured the effects of the direction of price movements and trading volume on trading interval durations. We also found that the trading interval duration is strongly persistent for Bitcoin similar to conventional financial assets. In contrast, we could not find any clear pattern of intraday seasonality for duration in the Bitcoin market.

  • A hierarchical Bayesian approach for identifying socioeconomic factors influencing self-rated health in Japan

    Nakakita M., Nakatsuma T.

    Healthcare Analytics 6 2024.12

    Research paper (scientific journal), Joint Work, Last author, Accepted

    　View Summary

    This study identifies socioeconomic factors that potentially influence self-rated health (SRH), an important indicator of health status, in the Japanese population. We used a panel data logit model to simultaneously estimate the effects of personal attributes, living environment, and social conditions. To achieve a stable estimation of the panel data logit model, we applied hierarchical Bayesian modeling and the Markov Chain Monte Carlo (MCMC) method to obtain its estimation. Furthermore, we used the ancillary-sufficiency interweaving strategy (ASIS) algorithm to improve the efficiency of the MCMC method for the panel data logit model. The results indicate that SRH within the Japanese population is affected by demographic and socioeconomic factors (e.g., age, marital status, educational background, and employment status) and daily habits such as frequency of drinking alcohol. We also obtained results that differed from previous studies in the research literature. Differences in the national character among countries may be reflected in these results. Since SRH is a subjective measure of health status and often differs from actual health status, it is crucial to remove the influences of the national character on SRH in evaluating the actual health status of individuals within a population. The study findings provide important insights into addressing these factors to understand SRH in the Japanese context better.

  • Comparative Analysis of Japanese Rice Wine Export Trends: Large Firms in the Nada Region vs. SMEs in Other Regions

    Wakuo Saito, Makoto Nakakita, Teruo Nakatsuma

    World 5 ( 3 ) 700 - 722 2024.08

    Research paper (scientific journal), Joint Work, Last author, Accepted

  • Bayesian Analysis of Stochastic Conditional Duration Models with Intraday and Intra-deferred Future Seasonalities in High-frequency Commodity Market

    Toyabe T., Nakakita M., Nakatsuma T.

    Proceedings - 2024 16th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2024    305 - 311 2024

    Research paper (international conference proceedings), Joint Work, Last author, Accepted

    　View Summary

    We propose an extension of the stochastic conditional duration (SCD) model to capture intraday seasonality and patterns specific to each bimonthly grouped contract month in gold futures trading intervals. Additionally, we have also modeled the effects of the limit order book information and news on trading intervals simultaneously. The trading time intervals of financial data are known to have very slow-decaying autocorrelations, and the Ancillarity-Sufficiency Interweaving Strategy (ASIS) overcomes the disadvantage of instability in estimation by incorporating a centered parameterization of the model. The results shows that the trading intervals exhibit an inverted U-shaped intraday seasonality, consistent with the prior research, and the impacts of the limit order book information as well as the news effects are also naturally interpretable.

  • Hierarchical Bayesian analysis of racehorse running ability and jockey skills

    Nakakita M, Nakatsuma T

    International Journal of Computer Science in Sport (International Journal of Computer Science in Sport)  22 ( 2 ) 1 - 25 2023.08

    Research paper (scientific journal), Joint Work, Last author, Accepted

    　View Summary

    In this paper, we proposed a new method of evaluating horse ability and jockey skills in horse racing. In the proposed method, we aimed to estimate unobservable individual effects of horses and jockeys simultaneously with regression coefficients for explanatory variables such as horse age and racetrack conditions and other parameters in the regression model. The data used in this paper are records on 1800-m races (excluding steeplechases) held by the Japan Racing Association from 2016 to 2018, including 4063 horses and 143 jockeys. We applied the hierarchical Bayesian model to stably estimate such a large amount of individual effects. We used the Markov chain Monte Carlo (MCMC) method coupled with Ancillarity- Sufficiency Interweaving Strategy for Bayesian estimation of the model and choose the best model with Widely Applicable Information Criterion as a model selection criterion. As a result, we found a large difference in the ability among horses and jockeys. Additionally, we observed a strong relationship between the individual effects and the race records for both horses and jockeys.

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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

    　View Summary

    In this study, as a model to explain the generating mechanism of limit orders (orders that specify the bid or ask price) in financial markets, we proposed an extension of the ACD (Autoregressive Conditional Duration) model as well as the SCD (Stochastic Conditional Duration) model in which intraday seasonality and limit order book information (the price and quantity of limit orders) are incorporated. We also developed a new efficient algorithm for Bayesian estimation of the proposed models via Markov chain Monte Carlo. We estimated the proposed models with the data of limit orders in the Tokyo Stock Exchange, and examined influences of indicators related to the market liquidity upon time intervals between limit orders.

  • 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

    　View Summary

    In this study, we propose a novel estimation technique for time series models of financial high-frequency data. Specifically, we consider two types of time series models; one is a model of duration between executions of financial transactions while the other is a model of time-varying volatility (variance) in very short intervals. To make these models more realistic, we propose to incorporate intraday seasonality (a cyclical pattern of duration or volatility during trading hours) explicitly into both models and estimate it simultaneously with the model parameters. Since the proposed models are too complex to be estimated with traditional maximum likelihood estimation, we developed an efficient Bayesian Markov chain Monte Carlo (MCMC) method for these models. We applied our new method to real-world high-frequency data (commodity futures and stock prices) and demonstrated their advantage over the conventional models.

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Courses Taught 【 Display / hide 】

  • THEORY AND PRACTICE OF TOKEN ECONOMIES

    2025

  • STARTUPS AND BUSINESS INNOVATION

    2025

  • SEMINAR: ECONOMETRICS

    2025

  • RESEARCH SEMINAR D

    2025

  • RESEARCH SEMINAR C

    2025

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Courses Previously Taught 【 Display / hide 】

  • STARTUPS AND BUSINESS INNOVATION

    Keio University

    2025.04
    -
    2026.03

  • ECONOMETRICS

    Keio University

    2025.04
    -
    2026.03

  • INDEPENDENT RESEARCH PROJECT B

    Keio University

    2025.04
    -
    2026.03

  • INDEPENDENT RESEARCH PROJECT A

    Keio University

    2025.04
    -
    2026.03

  • BAYESIAN STATISTICS

    Keio University

    2025.04
    -
    2026.03

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