Spectral bandwidth selection for long memory

Abstract

Long-memory parameter estimation using log-periodogram regression relies largely on the frequency bandwidth and the order of estimation. Literature shows that a data-dependent plug-in method for the bandwidth significantly increases the MSE's. In a long memory time series with mild short range effect, a simple approach to determine the bandwidth size is suggested based on the spectral analysis. Monte Carlo simulation results and empirical applications show that the proposed bandwidth selection performs satisfactorily.

Modelling Financial Market Volatility Using Asymmetric-Skewed-ARFIMAX and -HARX Models

“…The combination of these dissimilar volatilities (due to reaction times) is believed to produce a slow decaying autocorrelation function or long memory dependence property in financial markets. The long memory trait is commonly analyzed via the autoregressive fractionally integrated moving average models, ARFIMA (Andersen et al, 2003;Barunik & Krehlik, 2016;Yap & Cheong, 2016). To give a comprehensive comparison, this study includes the discussion on the extension of ARFIMA to form the volatility model.…”