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...

Using inflation and return time series, we first evaluate the forecasting performance of two classes of conditional heteroscedastic models: the random coefficient autoregressive (RCAR) models and the conditional heteroscedastic autoregressive moving average (CHARMA) models. Markov Chain Monte...

Using high-frequency transaction data, we evaluate the forecasting performance of several dynamic ordinal-response time series models with generalized autoregressive conditional heteroscedasticity. The specifications account for three components; leverage effects, in-mean effects and moving...