arXiv subjects

Yasuhiro Omori

Publications and source records attributed to Yasuhiro Omori.

7 recordsLinked to original sources

arXiv2026

Unified Mixture Sampler for State-Space Models: Application to Stochastic Conditional Duration Models

We propose a unified mixture sampler (UMS) that provides a universal estimation framework for nonlinear state-space models with "exp-exp" likelihood kernels. Unlike existing methods that require deriving new mixture approximations for each specific distribution, our approach dynamically adapts the standard ten-component mixture from Omori et al. (2007) through a deterministic re-centering and rescaling algorithm. Applying this to the stochastic conditional duration (SCD) model, we demonstrate that the proposed sampler can efficiently handle unknown shape parameters - such as those in Weibull or Gamma distributions - by updating mixture components near-instantaneously during MCMC iterations. The UMS not only simplifies implementation but also ensures exact inference via a lightweight Metropolis-Hastings step. Numerical examples show that our method substantially outperforms the conventional slice sampling approach, significantly reducing autocorrelation in MCMC samples while maintaining high computational efficiency. This unified framework encompasses a wide range of applications, including logit, Poisson, and various SCD model specifications, providing a highly efficient alternative to model-specific samplers.

stat.ME↗

arXiv2026

Dynamic Factor Stochastic Volatility-in-Mean VAR for Large Macroeconomic Panels

We develop a dynamic factor stochastic volatility-in-mean (SVM) specification for vector autoregressions (VARs) that embeds an SVM component within a dynamic factor stochastic volatility structure. A small number of latent volatility factors capture common movements in conditional variances, while volatility enters the conditional mean of the VAR. This specification allows time-varying uncertainty to influence macroeconomic dynamics through both second moments and expected outcomes while preserving tractability in large panels. We construct an efficient Markov chain Monte Carlo algorithm for estimation in this high-dimensional, non-Gaussian setting. Using quarterly data on twenty variables from the FRED-QD database, we compare predictive performance with the benchmark stochastic volatility VAR model. The dynamic factor SVM specification delivers superior forecasts for more variables during major macroeconomic disruptions such as the 2008 global financial crisis. The results indicate that allowing volatility to enter the mean captures an important transmission channel in macroeconomic dynamics.

stat.ME↗

arXiv2024

Stochastic Volatility in Mean: Efficient Analysis by a Generalized Mixture Sampler

In this paper we consider the simulation-based Bayesian analysis of stochastic volatility in mean (SVM) models. Extending the highly efficient Markov chain Monte Carlo mixture sampler for the SV model proposed in Kim et al. (1998) and Omori et al. (2007), we develop an accurate approximation of the non-central chi-squared distribution as a mixture of thirty normal distributions. Under this mixture representation, we sample the parameters and latent volatilities in one block. We also detail a correction of the small approximation error by using additional Metropolis-Hastings steps. The proposed method is extended to the SVM model with leverage. The methodology and models are applied to excess holding yields and S&P500 returns in empirical studies, and the SVM models are shown to outperform other volatility models based on marginal likelihoods.

econ.EM↗

arXiv2024

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↗

arXiv2021

Particle rolling MCMC with double-block sampling

An efficient simulation-based methodology is proposed for the rolling window estimation of state space models, called particle rolling Markov chain Monte Carlo (MCMC) with double block sampling. In our method, which is based on Sequential Monte Carlo (SMC), particles are sequentially updated to approximate the posterior distribution for each window by learning new information and discarding old information from observations. Th particles are refreshed with an MCMC algorithm when the importance weights degenerate. To avoid degeneracy, which is crucial for reducing the computation time, we introduce a block sampling scheme and generate multiple candidates by the algorithm based on the conditional SMC. The theoretical discussion shows that the proposed methodology with a nested structure is expressed as SMC sampling for the augmented space to provide the justification. The computational performance is evaluated in illustrative examples, showing that the posterior distributions of the model parameters are accurately estimated. The proofs and additional discussions (algorithms and experimental results) are provided in the Supplementary Material.

stat.CO↗

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

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↗