Výsledky vyhledávání - "Yamauchi, Yuta"

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General Bayesian quantile regression for counts via generative modeling

Autor: Yamauchi, Yuta, Kobayashi, Genya, Sugasawa, Shonosuke

Although quantile regression has emerged as a powerful tool for understanding various quantiles of a response variable conditioned on a set of covariates, the development of quantile regression for count responses has received far less attention. Thi

Externí odkaz: http://arxiv.org/abs/2410.23081

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

Autor: Kobayashi, Genya, Yamauchi, Yuta

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

Externí odkaz: http://arxiv.org/abs/2405.06335

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The total absolute curvature of closed curves with singularities

Autor: Honda, Atsufumi, Tanaka, Chisa, Yamauchi, Yuta

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 equ

Externí odkaz: http://arxiv.org/abs/2403.00487

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Realized Stochastic Volatility Model with Skew-t Distributions for Improved Volatility and Quantile Forecasting

Autor: Takahashi, Makoto, Yamauchi, Yuta, Watanabe, Toshiaki, Omori, Yasuhiro

Forecasting volatility and quantiles of financial returns is essential for accurately measuring financial tail risks, such as value-at-risk and expected shortfall. The critical elements in these forecasts involve understanding the distribution of fin

Externí odkaz: http://arxiv.org/abs/2401.13179

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Dynamic factor, leverage and realized covariances in multivariate stochastic volatility

Autor: Yamauchi, Yuta, Omori, Yasuhiro

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 re

Externí odkaz: http://arxiv.org/abs/2011.06909

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Bayesian approach to Lorenz curve using time series grouped data

Autor: Kobayashi, Genya, Yamauchi, Yuta, Kakamu, Kazuhiko, Kawakubo, Yuki, Sugasawa, Shonosuke

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 parame

Externí odkaz: http://arxiv.org/abs/1908.06772

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Multivariate Stochastic Volatility Model with Realized Volatilities and Pairwise Realized Correlations

Autor: Yamauchi, Yuta, Omori, Yasuhiro

Publikováno v: Journal of Business and Economic Statistics 38-4 (2020) 839-855

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 correl

Externí odkaz: http://arxiv.org/abs/1809.09928

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