arXiv subjects

Yuta Yamauchi

Publications and source records attributed to Yuta Yamauchi.

10 recordsLinked to original sources

arXiv2026

Realized Stochastic Volatility Models with Skew-t Distributions for Volatility and Tail Risk Forecasting

Accurate forecasting of volatility is essential for financial risk management and for the evaluation of tail risk measures such as value-at-risk (VaR) and expected shortfall (ES). This study proposes the realized stochastic volatility (RSV) model, an extension of the traditional stochastic volatility (SV) model that incorporates realized volatility as an efficient proxy for latent volatility. To better capture the stylized features of financial return distributions, particularly skewness and heavy tails, we consider three variants of skew-t distributions, two of which also admit skew-normal components to flexibly model asymmetry. The models are estimated using a Bayesian Markov chain Monte Carlo approach and applied to daily returns and realized volatility measures for major U.S. and Japanese stock indices. Empirically, RSV models robustly improve volatility forecasts relative to SV models across both indices, all four realized volatility proxies, and both pairwise and joint evaluation procedures. The evidence on VaR and ES forecasts is more heterogeneous and the advantage of RSV and skew-t specifications over their SV counterparts is mixed. Across both volatility and tail risk forecasting, RSV and skew-t specifications are useful in several settings, but no single specification dominates uniformly across indices, risk levels, and sample periods.

econ.EM↗

arXiv2026

The total absolute curvature of submanifolds with singularities

In this paper, we give a generalization of the Chern-Lashof theorem for submanifolds with singularities called frontals in Euclidean space. We prove that, for an $n$-dimensional admissible compact frontal in $(n+r)$-dimensional Euclidean space $\boldsymbol{R}^{n+r}$, its total absolute curvature is greater than or equal to the sum of the Betti numbers. Furthermore, if the total absolute curvature is equal to $2$, and all singularities are of the first kind, then the image of the frontal coincides with a closed convex domain of an affine $n$-dimensional subspace of $\boldsymbol{R}^{n+r}$.

math.DG↗

arXiv2026

Minimal total absolute curvature for equiaffine immersions

Koike (2001) defined the Lipschitz--Killing curvature and established a Chern--Lashof type inequality for equiaffine immersions of arbitrary codimensions. In this paper, we study the equality case. We prove that the total absolute curvature of an $n$-dimensional equiaffine immersion is equal to $2$ if and only if the image is a convex hypersurface embedded in an $(n+1)$-dimensional affine subspace.

math.DG↗

arXiv2026

Dynamic Bayesian regression quantile synthesis for forecasting outlook-at-risk

This paper proposes dynamic Bayesian regression quantile synthesis (DRQS), a novel method for quantile forecasting within the Bayesian predictive synthesis (BPS) framework designed to combine quantile-specific information from multiple agent models. While existing BPS approaches primarily focus on mean forecasting, our method directly targets the conditional quantiles of the response variable by utilizing the asymmetric Laplace distribution for the synthesis function. The resulting framework can be interpreted as a dynamic quantile linear model with latent predictors. We extend the univariate DRQS to a multivariate setting-factor DRQS (FDRQS)-by introducing a time-varying latent factor structure for the synthesis weights. This allows the model to leverage cross-sectional dependencies and shared information across multiple time series simultaneously. We develop an efficient Markov chain Monte Carlo (MCMC) algorithm for posterior inference, utilizing data augmentation and forward-filtering backward-sampling. Empirical applications to US inflation and global GDP growth demonstrate the improved performance of the proposed methods for quantile forecasting. In particular, FDRQS exhibits superior resilience during periods of extreme economic stress, such as the COVID-19 pandemic, by adaptively rebalancing agent contributions and capturing emergent global dependencies.

stat.ME↗

arXiv2025

General Bayesian quantile regression for counts via generative modeling

Count data frequently arises in biomedical applications, such as the length of hospital stay. However, their discrete nature poses significant challenges for appropriately modeling conditional quantiles, which are crucial for understanding heterogeneous effects and variability in outcomes. To solve the practical difficulty, we propose a novel general Bayesian framework for quantile regression tailored to count data. We seek the regression parameter on the conditional quantile by minimizing the expected loss with respect to the distribution of the conditional quantile of the latent continuous variable associated with the observed count response variable. By modeling the unknown conditional distribution through a Bayesian nonparametric kernel mixture for the joint distribution of the count response and covariates, we obtain the posterior distribution of the regression parameter via a simple optimization. We numerically demonstrate that the proposed method improves bias and estimation accuracy of the existing crude approaches to count quantile regression. Furthermore, we analyze the length of hospital stay for acute myocardial infarction and demonstrate that the proposed method gives more interpretable and flexible results than the existing ones.

stat.ME↗

arXiv2024

Bayesian factor zero-inflated Poisson model for multiple grouped count data

This paper proposes a computationally efficient Bayesian factor model for multiple grouped count data. Adopting the link function approach, the proposed model can capture the association within and between the at-risk probabilities and Poisson counts over multiple dimensions. The likelihood function for the grouped count data consists of the differences of the cumulative distribution functions evaluated at the endpoints of the groups, defining the probabilities of each data point falling in the groups. The combination of the data augmentation of underlying counts, the Pólya-Gamma augmentation to approximate the Poisson distribution, and parameter expansion for the factor components is used to facilitate posterior computing. The efficacy of the proposed factor model is demonstrated using the simulated data and real data on the involvement of youths in the nineteen illegal activities.

stat.ME↗

arXiv2024

The total absolute curvature of closed curves with singularities

In this paper, we give a generalization of Fenchel's theorem for closed curves as frontals in Euclidean space $\mathbb{R}^n$. We prove that, for a non-co-orientable closed frontal in $\mathbb{R}^n$, its total absolute curvature is greater than or equal to $π$. It is equal to $π$ if and only if the curve is a planar locally $L$-convex closed frontal whose rotation index is $1/2$ or $-1/2$. Furthermore, if the equality holds and if every singular point is a cusp, then the number $N$ of cusps is an odd integer greater than or equal to $3$, and $N=3$ holds if and only if the curve is simple.

math.DG↗

arXiv2021

Dynamic factor, leverage and realized covariances in multivariate stochastic volatility

In the stochastic volatility models for multivariate daily stock returns, it has been found that the estimates of parameters become unstable as the dimension of returns increases. To solve this problem, we focus on the factor structure of multiple returns and consider two additional sources of information: first, the realized stock index associated with the market factor, and second, the realized covariance matrix calculated from high frequency data. The proposed dynamic factor model with the leverage effect and realized measures is applied to ten of the top stocks composing the exchange traded fund linked with the investment return of the SP500 index and the model is shown to have a stable advantage in portfolio performance.

econ.EM↗

arXiv2019

Bayesian approach to Lorenz curve using time series grouped data

This study is concerned with estimating the inequality measures associated with the underlying hypothetical income distribution from the times series grouped data on the Lorenz curve. We adopt the Dirichlet pseudo likelihood approach where the parameters of the Dirichlet likelihood are set to the differences between the Lorenz curve of the hypothetical income distribution for the consecutive income classes and propose a state space model which combines the transformed parameters of the Lorenz curve through a time series structure. Furthermore, the information on the sample size in each survey is introduced into the originally nuisance Dirichlet precision parameter to take into account the variability from the sampling. From the simulated data and real data on the Japanese monthly income survey, it is confirmed that the proposed model produces more efficient estimates on the inequality measures than the existing models without time series structures.

stat.ME↗

arXiv2019

Multivariate Stochastic Volatility Model with Realized Volatilities and Pairwise Realized Correlations

Although stochastic volatility and GARCH (generalized autoregressive conditional heteroscedasticity) models have successfully described the volatility dynamics of univariate asset returns, extending them to the multivariate models with dynamic correlations has been difficult due to several major problems. First, there are too many parameters to estimate if available data are only daily returns, which results in unstable estimates. One solution to this problem is to incorporate additional observations based on intraday asset returns, such as realized covariances. Second, since multivariate asset returns are not synchronously traded, we have to use the largest time intervals such that all asset returns are observed in order to compute the realized covariance matrices. However, in this study, we fail to make full use of the available intraday informations when there are less frequently traded assets. Third, it is not straightforward to guarantee that the estimated (and the realized) covariance matrices are positive definite. Our contributions are the following: (1) we obtain the stable parameter estimates for the dynamic correlation models using the realized measures, (2) we make full use of intraday informations by using pairwise realized correlations, (3) the covariance matrices are guaranteed to be positive definite, (4) we avoid the arbitrariness of the ordering of asset returns, (5) we propose the flexible correlation structure model (e.g., such as setting some correlations to be zero if necessary), and (6) the parsimonious specification for the leverage effect is proposed. Our proposed models are applied to the daily returns of nine U.S. stocks with their realized volatilities and pairwise realized correlations and are shown to outperform the existing models with respect to portfolio performances.

econ.EM↗