論文

We develop a Markov chain Monte Carlo method for a linear regression model with an ARMA(p, q)-GARCH(r, s) error. To generate a Monte Carlo sample from the joint posterior distribution, we employ a Markov chain sampling with the Metropolis-Hastings algorithm. As illustration, we estimate an ARMA-GARCH model of simulated time series data. (C) 2000 Elsevier Science S.A. All righter reserved. JEL classification: C11; C22.

Three Bayesian methods (Markov chain Monte Carlo, Laplace approximation and quadrature formula) are developed to estimate the parameters of the ARMA-GARCH model. The ARMA-GARCH model is applied to weekly foreign exchange rate data of five major currencies, and their stochastic volatilities are judged by the posterior probabilities of stationarity and other conditions. © 1999 Kluwer Academic Publishers.

In this paper a new class of importance distributions that are based on normal and t-distributions are introduced. They are labeled variable integrating constant distributions or VIC distributions. The main innovation in this class of distributions is that the standard error associated with each prameter is expanded in an exponential polynomial function of the scaled distance from its mode, with a different polynomial used in each direction and for each parameter. Resulting variances differ at each point in the parameter space, thereby breaking the flexibility limitations that are inherent in normal and t-distributions allowing them to be closely matched to the posterior distribution whose moments are the goal of the inference exercise. Two illustrations are presented in which the performance of normal, t, normal-VIC, and t-VIC importance distributions are compared. Results show the VIC distributions to produce globally stable weights and substantial improvements in the numerical efficiency of importance sampling and independence Metropolis estimates. © 2004 Kluwer Academic Publishers.

Regime-shift models of daily returns are estimated for the foreign exchange rates of the Asian currencies that suffered from drastic devaluation during the Asian financial crisis in 1997, and the change points are detected for their volatility structures. Furthermore, how the persistence in the volatility of their exchange rates changed after the crisis is examined. © 2000 Kluwer Academic Publishers.

Based on the maximal invariant principle, we derive two ratio tests (locally best invariant test and point optimal test) for a unit root and compare them with previously proposed ratio tests. We also show that our ratio tests tend to have better powers than the Dickey-Fuller test and the modified Dickey-Fuller test. Copyright © 2000 by Marcel Dekker, Inc.

© 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.

© 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

This study investigates the determinants of life satisfaction in Japan, with a particular focus on health satisfaction as a key longitudinal predictor. Using nationally representative panel data and hierarchical Bayesian estimation with fixed individual effects, it analyzes how health, socioeconomic, and behavioral factors jointly shape life satisfaction. Two models were estimated: a baseline model employing weakly informative priors and an extended model using a horseshoe prior to achieve adaptive shrinkage and identify effectively unshrunk predictors. The results indicate that health satisfaction has a strong and robust positive association with life satisfaction, independent of age and other socioeconomic factors. Meanwhile, the interactions between health satisfaction and age were not significant, suggesting that the relationship remains stable across the life course. As for marital status, exercise, adequate sleep, and household income, they were positively associated with life satisfaction, whereas job dissatisfaction had a negative association. Moreover, substantial heterogeneity in individual effects points to the important role played by unobserved traits, such as personality and resilience, in subjective well-being. The findings are interpreted not as evidence of a fixed deterministic mechanism, but as longitudinal evidence that perceived health remains a central component of life evaluation even when adaptation, shifting expectations, and individual heterogeneity are considered. Policies that foster preventive health care, work-life balance, social participation, and financial security across generations are likely to yield sustained improvements in well-being.

There exist several estimators for valuing the Asian option on the arithmetic mean. Among all variance reduction estimators, the one with the control variate derived from the geometric mean has been shown by Boyle et al. (1997) to perform best so far. In this paper, a new improved control variate estimator for this type of Asian option is proposed and investigated. Simulation results confirm that it does perform better than the control variate derived from the geometric mean. The improvement becomes more significant as the volatility increases and/or as the time to expiration lengthens. © Springer 2006.

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.