Details of a Researcher - Nakatsuma, Teruo

Papers - Nakatsuma, Teruo

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Relationships Between Self-Esteem and Personal Attributes, Income, Consumption, and Assets: Japanese Panel Study

Makoto Nakakita, Sakae Oya, Naoki Kubota, Tomoki Toyabe, Teruo Nakatsuma

European Journal of Investigation in Health, Psychology and Education 15 ( 5 ) 78 2025.05

Research paper (scientific journal), Joint Work, Last author, Accepted, ISSN 21748144

Self-esteem is a key topic in psychology and health research. It influences well-being, happiness, and even medicine. However, existing studies on self-esteem have yielded conflicting results, suggesting that a global consensus remains elusive. This study examines how demographic and socioeconomic factors influence self-esteem in Japan. We analyzed panel data sampled from the entire Japanese population, with separate estimates performed for marital status and gender to account for potential differences in the impact of these factors. Using a Bayesian panel logit model with the Markov chain Monte Carlo method and the ancillarity–sufficiency interweaving strategy for efficient estimation, we found similarities and differences compared with studies from other countries. Furthermore, when comparing the overall data with data stratified by marital status and gender, we observed significant differences in how these factors influenced self-esteem, even among the same individuals. These findings underscore the importance of considering such variations when incorporating self-esteem into medical and healthcare contexts.

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Panel Data Analysis of Socioeconomic Factors and COVID-19’s Impact on Drinking Habits: Evidence from a Japanese Survey

Makoto Nakakita, Naoki Kubota, Tomoki Toyabe, Sakae Oya, Teruo Nakatsuma

International Journal of Environmental Research and Public Health 22 ( 5 ) 663 2025.04

Research paper (scientific journal), Joint Work, Last author, Accepted, ISSN 16617827

Alcohol use is closely related to health, emotional state, and social behavior. However, no clear consensus exists on how socioeconomic factors influence drinking habits or how the COVID-19 pandemic affected alcohol use. This study examines these relationships in Japan using panel data from a questionnaire survey (2014–2022), consisting of 10,836 responses provided by 1289 respondents, and logistic regression analysis. The results revealed that males, individuals aged 40–69, cigarette smokers, and those who exercise regularly are more likely to drink alcohol, whereas those working at least 2 days per week, earning higher incomes, and in good health are less likely to drink alcohol. The impact of COVID-19 on drinking behavior varied by region, with significant effects observed in specific regions. Additionally, an identified decline in alcohol use since 2016 highlights rising health awareness and shifting values, particularly among younger generations. These findings underscore the strong association between drinking behavior and socioeconomic attributes and emphasize the need to consider regional differences in policy and cultural influences. Overall, this study provides key insights for future research and public health policies on alcohol use in Japan.

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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, ISSN 2573-0134

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.

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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

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.

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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

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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

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.

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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

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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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

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.

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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, ISSN 1911-8066

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.

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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, ISSN 2520-8756

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.

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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, ISSN 1911-8066

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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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, Last author, Accepted, ISSN 02776693

© 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.

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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

© 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.

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ティックデータを用いた株式市場における約定予測

NAKATSUMA TERUO

ジャフィー・ジャーナル (朝倉書店) 94 - 127 2016

Research paper (scientific journal), Joint Work, Accepted

分位点回帰による期待ショートフォール最適化とポートフォリオ選択

NAKATSUMA TERUO

ジャフィー・ジャーナル (朝倉書店) 26 - 63 2016

Research paper (scientific journal), Joint Work, Accepted

ベイズ統計学の基礎

NAKATSUMA TERUO

経済時系列ハンドブック (朝倉書店) 367 - 382 2012