An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series

169 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Long memory and structural breaks in modeling the return and volatility dynamics of precious metals (2012, background): “For instance, Bhardwaj and Swanson (2006) find that the LM models give better out-of-sample forecasts than ARMA, standard GARCH and related models.”
Predicting Bid–Ask Spreads Using Long‐Memory Autoregressive Conditional Poisson Models (2011, unknown): “Empirical studies confirm that limit and market order submission strategies indeed depend strongly on quoted spreads, see Biais et al. (1995), Harris and Hasbrouck (1996), Ranaldo (2004), Anand et al.”
Simple (but effective) tests of long memory versus structural breaks (2006, background): “Therefore, (14) and (15) follow, and we establish (10).”
Modeling and predicting the CBOE market volatility index (2014, methodology): “…form of long memory that depends exclusively on a single parameter, i.e., the fractional integration order (see, e.g., Abadir and Talmain, 2002; Bhardwaj and Swanson, 2006). nature of the model derives from the fact that, at each time scale, the partial volatility relies on different…”