Nakatsuma, Teruo

写真a

Affiliation

Faculty of Economics (Mita)

Position

Professor

External Links
Page Top ▲

Other Affiliation 【 Display / hide 】

  • Dean, Graduate School of Economics

Page Top ▲

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）

Page Top ▲

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

Page Top ▲

Academic Degrees 【 Display / hide 】

  • Ph.D.（経済学）, ラトガーズ大学

Page Top ▲
 

Research Areas 【 Display / hide 】

  • Humanities & Social Sciences / Economic statistics (Econometrics)

  • Informatics / Statistical science (Bayesian Statistics)

Page Top ▲
 

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

display all >>

Page Top ▲

Papers 【 Display / hide 】

  • 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.

  • 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)  35 ( 2 ) 256 - 277 2023.05

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

  • 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

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

    　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

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

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

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

    　View Summary

    Intraday high-frequency data of stock returns exhibit not only typical characteristics (e.g., volatility clustering and the leverage effect) but also a cyclical pattern of return volatility that is known as intraday seasonality. In this paper, we extend the stochastic volatility (SV) model for application with such intraday high-frequency data and develop an efficient Markov chain Monte Carlo (MCMC) sampling algorithm for Bayesian inference of the proposed model. Our modeling strategy is two-fold. First, we model the intraday seasonality of return volatility as a Bernstein polynomial and estimate it along with the stochastic volatility simultaneously. Second, we incorporate skewness and excess kurtosis of stock returns into the SV model by assuming that the error term follows a family of generalized hyperbolic distributions, including variance-gamma and Student’s t distributions. To improve efficiency of MCMC implementation, we apply an ancillarity-sufficiency interweaving strategy (ASIS) and generalized Gibbs sampling. As a demonstration of our new method, we estimate intraday SV models with 1 min return data of a stock price index (TOPIX) and conduct model selection among various specifications with the widely applicable information criterion (WAIC). The result shows that the SV model with the skew variance-gamma error is the best among the candidates.

display all >>

Page Top ▲

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

display all >>

Page Top ▲

Reviews, Commentaries, etc. 【 Display / hide 】

Page Top ▲

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)

display all >>

Page Top ▲

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.

Page Top ▲
 

Courses Taught 【 Display / hide 】

  • THEORY AND PRACTICE OF TOKEN ECONOMIES

    2024

  • STARTUPS AND BUSINESS INNOVATION

    2024

  • SEMINAR: ECONOMETRICS

    2024

  • RESEARCH SEMINAR D

    2024

  • RESEARCH SEMINAR C

    2024

display all >>

Page Top ▲

Courses Previously Taught 【 Display / hide 】

  • ECONOMETRICS

    Keio University

    2024.04
    -
    2025.03

  • INDEPENDENT RESEARCH PROJECT B

    Keio University

    2024.04
    -
    2025.03

  • INDEPENDENT RESEARCH PROJECT A

    Keio University

    2024.04
    -
    2025.03

  • THEORY AND PRACTICE OF TOKEN ECONOMIES

    Keio University

    2024.04
    -
    2025.03

  • DATA-DRIVEN FINANCE AND CAPITAL MARKET STRATEGY

    Keio University

    2024.04
    -
    2025.03

display all >>

Page Top ▲