Structural changes in business fluctuations have been gathering attention in Europe and the US in recent years. It has become clear that business fluctuations in the US began to stabilize from the middle of the 1980s, and similar structural changes have been observed in Europe. On the other...

An attempt is made to estimate a state space model of investment and borrowing in a Bayesian framework and extract the unobservable agency cost of Japanese firms by firm size. Our estimates of the agency cost exhibited a declining trend in the late 80s and then switched to an increasing trend in...

Bivariate mixture models attribute the well-known positive correlation between return volatility and trading volume in financial markets to stochastic changes in a single latent variable representing the number of information arrivals. In this article, dynamic bivariate mixture models that allow...

The realized stochastic volatility model of Takahashi, Omori, and Watanabe (2009), which incorporates the asymmetric stochastic volatility model with the realized volatility, is extended by employing a wider class distribution, the generalized hyperbolic skew Student's t-distribution, for...

This article evaluates the predictive performance of variance risk premiums (VRPs) in Japan on the Nikkei 225 returns, credit spreads, and the composite index of coincident indicators. Different monthly VRPs, such as expected and ex-post VRPs, are measured by using model-free implied and...

　　 The realized stochastic volatility model of Takahashi, Omori, and Watanabe (2009), which incorporates the asymmetric stochastic volatility model with the realized volatility, is extended with more general form of bias correction in realized volatility and wider class...

This paper develops a new method for the analysis of stochastic volatility (SV) models. Since volatility is a latent variable in SV models, it is difficult to evaluate the exact likelihood. In this paper, a non-linear filter which yields the exact likelihood of SV models is employed. Solving a...