We develop forecasting methodology for the fractional exponential (FEXP) model. First, we devise algorithms for fast exact computation of the coefficients in the infinite order autoregressive and moving average representations of a FEXP process. We also describe an algorithm to accurately approximate the autocovariances and to simulate realizations of the process. Next, we present a fast freque...

This review offers a guided tour to PcGive 10 modules for econometrics analysis of time series (PcGive), limited dependent variable (LogitJD) and static and dynamic panel data analyses (DPD), financial econometric (GARCH) and time series (ARFIMA) modelling. Several empirical applications are reported to illustrate the package.

We consider the fractionally integrated ARFIMA Processes with seasonality s, denoted by SARFIMA(0, D, 0)s. This work presents a closed formula for the Durbin-Levinson’s algorithm relating the partial autocorrelation and the autocorrelation functions of these processes. In order to obtain the closed formula we show a hypergeometric identity, namely

In this article, we develop one- and two-component Markov regime-switching conditional volatility models based on the intraday range evaluate their performance in forecasting daily of S&P 500 Index. We compare with that several well-established return- range-based models, namely EWMA, GARCH, FIGARCH GARCH model, hybrid EWMA CARR model. in-sample goodness fit out-of-sample forecast using a compr...

This paper develops a new multivariate Markov regime switching model that incorporates long memory in the volatility process. The research extends the Generalized Regime Switching (GRS) framework developed by Gray (1996) to the Bivariate case using a Fractionally Integrated GARCH process with constant correlation (B-RS_FIGARCH). The model is applied to estimate dynamic minimum variance hedge ra...