A multivariate stochastic volatility model with dynamic equicorrelation and cross leverage effect is proposed and estimated. Using a Bayesian approach, an efficient Markov chain Monte Carlo algorithm is described where we use the multi-move sampler, which generates multiple latent variables simultaneously. Numerical examples are provided to show its sampling efficiency in comparison with the simple algorithm that generates one latent variable at a time given other latent variables. Furthermore, the proposed model is applied to the multivariate daily stock price index data. The model comparisons based on the portfolio performances and DIC show that our model overall outperforms competing models.

This paper proposes a new stochastic volatility model with time-varying expected return, which enables us to predict returns based on exponential moving averages of the past returns frequently used in practice. Particularly, exploiting a particle filter in a self-organizing state space framework, we demonstrate that a simple return predictionbased strategy is superior to well-known strategies such as equally-weighted, minimumvariance and risk parity portfolios, which do not depend on return prediction. In addition, we develop three types of anomaly detectors that are easily implemented in the algorithm of the particle filter and apply them to investment decision. As a result, our model robustly outperforms the exponential moving average. Our dataset is monthly total returns of global assets such as stocks, bonds and REITs, and investment performances are evaluated with various statistics such as compound returns, Sharpe ratios, Sortino ratios or drawdowns.

A smoothing spline is considered to propose a novel model for the time-varying quantile of the univariate time series using a state space approach. A correlation is further incorporated between the dependent variable and its one-step-ahead quantile. Using a Bayesian approach, an efficient Markov chain Monte Carlo algorithm is described where we use the multi-move sampler, which generates simultaneously latent time-varying quantiles. Numerical examples are provided to show its high sampling efficiency in comparison with the simple algorithm that generates one latent quantile at a time given other latent quantiles. Furthermore, using Japanese inflation rate data, an empirical analysis is provided with the model comparison.

This study presents contemporaneous modeling of asset return and price range within the framework of stochastic volatility with leverage. A new representation of the probability density function for the price range is provided, and its accurate sampling algorithm is developed. A Bayesian estimation using Markov chain Monte Carlo (MCMC) method is provided for the model parameters and unobserved variables. MCMC samples can be generated rigorously, despite the estimation procedure requiring sampling from a density function with the sum of an infinite series. The empirical results obtained using data from the U.S. market indices are consistent with the stylized facts in the financial market, such as the existence of the leverage effect. In addition, to explore the model's predictive ability, a model comparison based on the volatility forecast performance is conducted.