18 citations · 18 across the 7 of their papers we have counts for
7 papers
Dynamic Factor Stochastic Volatility-in-Mean VAR for Large Macroeconomic Panels
Daichi Hiraki, Siddhartha Chib, Yasuhiro Omori
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 vola…
Unified Mixture Sampler for State-Space Models: Application to Stochastic Conditional Duration Models
Daichi Hiraki, Yasuhiro Omori
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 method…
Stochastic Volatility in Mean: Efficient Analysis by a Generalized Mixture Sampler
Daichi Hiraki, Siddhartha Chib, Yasuhiro Omori
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 samp…
Realized Stochastic Volatility Models with Skew-t Distributions for Volatility and Tail Risk Forecasting
Makoto Takahashi, Yuta Yamauchi, Toshiaki Watanabe +1
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).…
Dynamic factor, leverage and realized covariances in multivariate stochastic volatility
Yuta Yamauchi, Yasuhiro Omori
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.…
Multivariate Stochastic Volatility Model with Realized Volatilities and Pairwise Realized Correlations
Yuta Yamauchi, Yasuhiro Omori
Although stochastic volatility and GARCH (generalized autoregressive conditional heteroscedasticity) models have successfully described the volatility dynamics of univariate asset…
Particle rolling MCMC with double-block sampling
Naoki Awaya, Yasuhiro Omori
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 b…