中妻 照雄 (ナカツマ テルオ)

Nakatsuma, Teruo

写真a

所属(所属キャンパス)

経済学部 (三田)

職名

教授

外部リンク

その他の所属・職名 【 表示 / 非表示

  • 経済学研究科委員長

経歴 【 表示 / 非表示

  • 1998年04月
    -
    2000年03月

    一橋大学経済研究所 ,専任講師

  • 2000年04月
    -
    2003年03月

    大学専任講師(経済学部)

  • 2003年04月
    -
    2010年03月

    大学准教授(経済学部)

  • 2010年04月
    -
    継続中

    大学教授(経済学部)

学歴 【 表示 / 非表示

  • 1991年03月

    筑波大学, 第3学群社会工学類

  • 1994年03月

    筑波大学, 社会工学研究科

  • 1995年10月

    ラトガーズ大学, 経済学研究科, 計量経済学

    アメリカ合衆国

  • 1998年05月

    ラトガーズ大学, 経済学研究科, 計量経済学

    アメリカ合衆国

学位 【 表示 / 非表示

  • Ph.D.(経済学), ラトガーズ大学

 

研究分野 【 表示 / 非表示

  • 人文・社会 / 経済統計 (計量経済学)

  • 情報通信 / 統計科学 (ベイズ統計学)

 

著書 【 表示 / 非表示

  • Asset Management and Robo-Advisors

    Nakatsuma T., The Economics of Fintech, 2021年01月

  • Machine Learning Principles and Applications

    Nakatsuma T., The Economics of Fintech, 2021年01月

  • The Economics of Fintech

    Kaji S., Nakatsuma T., Fukuhara M., The Economics of Fintech, 2021年01月

     概要を見る

    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年11月,  ページ数: 214

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論文 【 表示 / 非表示

  • 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年08月

     概要を見る

    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.

  • 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年10月

     概要を見る

    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年07月

     概要を見る

    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)  35 ( 2 ) 256 - 277 2022年

    ISSN  17511062

     概要を見る

    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 (Journal of Risk and Financial Management)  14 ( 4 ) 145 2021年03月

    研究論文(学術雑誌), 共著, 査読有り

     概要を見る

    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.

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KOARA(リポジトリ)収録論文等 【 表示 / 非表示

総説・解説等 【 表示 / 非表示

研究発表 【 表示 / 非表示

  • 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年12月

    口頭発表(一般), 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年12月

    口頭発表(一般)

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

    中妻 照雄

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

    2016年12月

    口頭発表(一般), CFEnetwork

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

    中妻 照雄

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

    2016年06月

    ポスター発表, International Society for Bayesian Analysis (ISBA)

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

    中妻 照雄

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

    2015年12月

    口頭発表(一般)

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競争的研究費の研究課題 【 表示 / 非表示

  • 金融市場における指値注文の発生過程に関するベイズ時系列分析

    2019年04月
    -
    2022年03月

    文部科学省・日本学術振興会, 科学研究費助成事業, 中妻 照雄, 基盤研究(C), 補助金,  研究代表者

     研究概要を見る

    本研究では、金融市場における指値注文(売買価格を指定する注文)の発生メカニズムを説明するための新しいモデルとして、日中季節性と板情報(指値注文の価格と数量)を反映させたACD (Autoregressive Conditional Duration) モデルとSCD (Stochastic Conditional Duration) モデルの拡張を提案するとともに、提案モデルをマルコフ連鎖モンテカルロ法でベイズ推定するための新しい効率的アルゴリズムの開発を行なった。そして、提案モデルを東京証券取引所における売買注文の情報に適用し、市場の流動性を示す指標が指値注文の間隔に与える影響を検証した。

  • 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年04月
    -
    2019年03月

    文部科学省・日本学術振興会, 科学研究費助成事業, 中妻 照雄, 基盤研究(C), 補助金,  研究代表者

     研究概要を見る

    本研究では金融市場における高頻度データ(取引単位で記録されたデータ)の特徴を捉えられるモデルをベイズ推定するための手法の開発に取り組んだ。特に(1)取引が成立する(約定する)間隔のモデル化と(2)短時間における資産収益率の分散のモデル化という2つのテーマに注力した。第1のテーマである約定間隔のモデル化においては、日中季節性をモデルの中で他のパラメータと同時に推定する方法を提案した。一方、第2のテーマである分散のモデル化においても分単位で分散が変動するモデルに同じく日中季節性を導入して他のパラメータと同時に推定する方法を提案した。そして、提案手法の有効性を実際の高頻度データを利用して検証した。

 

担当授業科目 【 表示 / 非表示

  • トークンエコノミーの理論と実践

    2024年度

  • スタートアップとビジネスイノベーション

    2024年度

  • 計量経済学演習

    2024年度

  • 研究会d

    2024年度

  • 研究会c

    2024年度

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担当経験のある授業科目 【 表示 / 非表示

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  • 研究プロジェクトa

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  • 研究プロジェクトb

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  • データサイエンス超入門(数値データ)

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