Nakatsuma, Teruo

写真a

Affiliation

Faculty of Economics ( Mita )

Position

Professor

External Links
Scopus Paper Info  
Paper Count: 28 Citation Count: 66 h-index: 4

Click to view the Scopus page. The data was downloaded from Scopus API in May 17, 2026, via http://api.elsevier.com and http://www.scopus.com .

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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 】

  • Determinants of Sports Participation in Japan: The Interplay of Sociodemographic Factors, Social Roles, and Behavioral Change

    Kubota N., Nakakita M., Nakatsuma T.

    Social Sciences 15 ( 3 )  2026.03

    　View Summary

    Sports participation is a widely recognized facilitator of physical health, mental well-being, and social inclusion, but persistent and substantial disparities have been observed across socioeconomic groups. Focusing on Japan, this study examined the socioeconomic determinants of sports participation, particularly the roles of gender, age, employment, and caregiving responsibilities. It used nationally representative repeated cross-sectional data to analyze participation rates and annual participation days across multiple sports at the population-segment level, defined by combinations of demographic and social attributes. Results revealed prominent sport-specific gender differences, heterogeneous age effects across sports, significant age–gender interaction effects, and distinctive behavioral changes during the COVID-19 pandemic. During the pandemic, participation in competitive and group sports declined with age, but walking increased among middle-aged and older adults. In addition, constraints in employment and caregiving had limited overall effects but significantly reduced engagement in walking. These findings suggest the crucial influence of the interaction among social roles, life-stage transitions, and historical context, rather than biological sex differences alone, on sports participation patterns, highlighting the urgency of designing sports policies as inclusive social interventions that consider diverse motivations and limitations across population groups.

  • Who Chooses to Marry? A Bayesian Analysis of Marital Status and Sociodemographic Outcomes in Japan

    Nakakita M., Toyabe T., Saito W., Kubota N., Nakatsuma T.

    Societies 16 ( 3 )  2026.03

    　View Summary

    Delayed marriage and non-marriage have become increasingly important issues in Japan, where marriage remains closely related to household formation and well-being. This study examines which sociodemographic characteristics are associated with being married and how marital status correlates with economic conditions, health behaviors, subjective well-being, and COVID-19-related measures. Using annual panel data from 2014 to 2022, we first conducted descriptive comparisons between married and non-married individuals and then estimated a Bayesian panel logit model with respondent-specific effects to account for unobserved heterogeneity. The analysis was designed to identify associations rather than causal effects. The results showed the strongest positive associations with being married for individuals aged 30–49 years, consistent with delayed marriage. Employment attributes such as holding side work and managerial positions were positively associated with marriage, whereas nonprofit employment and self-employment were negatively or imprecisely associated. Financial assets and total debt were positively correlated with marriage, consistent with joint household formation. Higher happiness and life hope were positively associated with being married; regular exercise and longer weekend sleep were negatively associated, whereas longer weekday sleep was positively associated. In addition, respondent-specific effects revealed substantial heterogeneity beyond observed covariates. These findings identify key socioeconomic and behavioral domains associated with marriage in Japan, highlight the importance of unobserved heterogeneity, and provide evidence that may help identify groups prone to delayed marriage under changing social and economic conditions.

  • An analytical study of worker well-being and COVID-19 impact using Bayesian panel modeling

    Nakakita M., Toyabe T., Kubota N., Saito W., Nakatsuma T.

    Healthcare Analytics 8 2025.12

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

    　View Summary

    This study investigates how the determinants of Japanese workers’ well-being shifted before and during the COVID-19 pandemic. We estimate a Bayesian hierarchical panel model and Markov chain Monte Carlo sampling is implemented with the ancillarity–sufficiency interweaving strategy to handle the high parameter-to-sample ratio efficiently. Consequently, we observed that positive drivers include marriage, good health, job satisfaction, and conversion from nonregular to regular employment, whereas male gender, turnover intention, reduced family contact, and pandemic-related financial concerns lower well-being. Age traces a U-shape, and weekday sleep shows an inverse-U pattern. Although the evidence is correlational and confined to self-reported data from one country, the analysis clarifies how socio-economic and workplace factors interact with a major external shock.

  • Blockchain-Based Digital Vouchers as a Key Driver for Japanese Regional Tourism

    Ducroux M., Franzese G., Hamahira M., Nakakita M., Nakatsuma T., Pagani A., Saito W., Toyabe T.

    Smart Innovation Systems and Technologies 441 SIST   199 - 212 2025.10

    Research paper (international conference proceedings), Joint Work, Accepted,  ISSN  21903018

    　View Summary

    The tourism sector in Japan has been one of the key sectors put forward by the Japanese government to rejuvenate its economy. In recent years, significant efforts have been made to attract tourists and enrich their overall experience. However, a significant challenge remains: the lack of convenient payment methods beyond cash in rural areas. In this study, we propose a blockchain-based digital voucher system to facilitate digital payments for international tourists visiting Japan. Our research is two-fold. First, we design a token system that seamlessly integrates with existing voucher solutions commonly issued by local governments in Japan, streamlining the processes of issuance and management. Second, we explore the potential of digital vouchers to enhance the development of the tourism industry in rural areas, thereby promoting a more inclusive and sustainable tourism ecosystem across Japan.

  • Bayesian Analysis of Bitcoin Volatility Using Minute-by-Minute Data and Flexible Stochastic Volatility Models

    Nakakita M., Toyabe T., Nakatsuma T.

    Mathematics (MDPI AG)  13 ( 16 ) 2691 - 2691 2025.08

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

    　View Summary

    This study analyzes the volatility of Bitcoin using stochastic volatility models fitted to one-minute transaction data for the BTC/USDT pair between 1 April 2023, and 31 March 2024. Bernstein polynomial terms were introduced to accommodate intraday and intraweek seasonality, and flexible return distributions were used to capture distributional characteristics. Seven return distributions—normal, Student-t, skew-t, Laplace, asymmetric Laplace (AL), variance gamma, and skew variance gamma—were considered. We further incorporated explanatory variables derived from the trading volume and price changes to assess the effects of order flow. Our results reveal structural market changes, including a clear regime shift around October 2023, when the asymmetric Laplace distribution became the dominant model. Regression coefficients suggest a weakening of the volume–volatility relationship after September and the presence of non-persistent leverage effects. These findings highlight the need for flexible, distribution-aware modeling in 24/7 digital asset markets, with implications for market monitoring, volatility forecasting, and crypto risk management.

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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

    [International presentation]  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

    [International presentation]  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

    [International presentation]  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 presentation]  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

    [International presentation]  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 】

  • Modeling of fluctuations in crypto asset prices and its application to portfolio optimization and risk management

    2025.04
    -
    2030.03

    中妻 照雄, 基盤研究(B), Principal investigator

  • 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 】

  • RESEARCH SEMINAR (THESIS)

    2026

  • RESEARCH SEMINAR D

    2026

  • DATA SCIENCE PROJECT

    2026

  • RESEARCH SEMINAR B

    2026

  • DATA SCIENCE CONSULTING

    2026

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Courses Previously Taught 【 Display / hide 】

  • STARTUPS AND BUSINESS INNOVATION

    Keio University

    2026.04
    -
    2027.03

  • ECONOMETRICS

    Keio University

    2026.04
    -
    2027.03

  • BAYESIAN STATISTICS

    Keio University

    2026.04
    -
    2027.03

  • THEORY AND PRACTICE OF TOKEN ECONOMIES

    Keio University

    2026.04
    -
    2027.03

  • INTRODUCTION TO DATA SCIENCE (NUMERICAL DATA)

    Keio University

    2026.04
    -
    2027.03

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