We propose a moving average stochastic volatility in mean model and a moving average stochastic volatility model with leverage. For parameter estimation, we develop efficient Markov chain Monte Carlo algorithms and illustrate our methods, using simulated data and a real data set. We compare the...

Autoregressive Conditional Heteroscedasticity (ARCH) models have successfully been applied in order to predict asset return volatility. Predicting volatility is of great importance in pricing financial derivatives, selecting portfolios, measuring and managing investment risk more accurately. In...

In the increasingly connected world, many systems are more or less coupled with each other in various ways. A typical example is the cross-market portfolio management, where the products of heterogeneous markets are selected and configured for investment. In such cross-market problems, one...