scispace - formally typeset
T

Toshiaki Watanabe

Researcher at Hitotsubashi University

Publications -  57
Citations -  1314

Toshiaki Watanabe is an academic researcher from Hitotsubashi University. The author has contributed to research in topics: Volatility (finance) & Realized variance. The author has an hindex of 18, co-authored 56 publications receiving 1185 citations. Previous affiliations of Toshiaki Watanabe include Tokyo Metropolitan University & Bank of Japan.

Papers
More filters
Journal ArticleDOI

Bayesian analysis of time-varying parameter vector autoregressive model for the Japanese economy and monetary policy

TL;DR: In this paper, the authors analyzed the time-varying parameter vector autoregressive (TVP-VAR) model for the Japanese economy and monetary policy and found that the model best fit the Japanese economic data.
Journal ArticleDOI

Estimating stochastic volatility models using daily returns and realized volatility simultaneously

TL;DR: In this paper, a Bayesian approach is taken and an efficient sampling algorithm is proposed to implement the Markov chain Monte Carlo method for the simultaneous model, which provides an estimate of the entire conditional predictive distribution of returns under consideration of the uncertainty in the estimation of both biases and parameters.
Posted Content

Estimating Stochastic Volatility Models Using Daily Returns and Realized Volatility Simultaneously

TL;DR: The result of the model comparison between the simultaneous models using both naive and scaled realized volatilities indicates that the effect of non-trading hours is more essential than that of microstructure noise and that asymmetry is crucial in stochastic volatility models.
Journal ArticleDOI

Block sampler and posterior mode estimation for asymmetric stochastic volatility models

TL;DR: A new efficient simulation smoother and disturbance smoother for asymmetric stochastic volatility models where there exists a correlation between today's return and tomorrow's volatility.
Posted Content

Block Sampler and Posterior Mode Estimation for Asymmetric Stochastic Volatility Models (Published in "Computational Statistics and Data Analysis", 52-6, 2892-2910. February 2008. )

TL;DR: In this article, a new efficient simulation smoother and disturbance smoother for asymmetric stochastic volatility models where there exists a correlation between today's return and tomorrow's volatility is introduced, where the state vector is divided into several blocks where each block consists of many state variables.