TY - JOUR
AU - Proietti, Tommaso
AB - AbstractExtracting and forecasting the volatility of financial markets is an important empirical problem. The article provides a time series characterization of the volatility components arising when the volatility process is fractionally integrated, through a generalization of the Beveridge–Nelson decomposition, and proposes a new integrated moving average (MA) model, formulated in terms of the fractional lag operator, the FLagIMA model, which allows the series to be decomposed as the sum of a fractional noise and a white noise component. We provide an assessment of the predictive performance of the FLagIMA model in comparison with other popular predictors and two other rival specifications, the fractionally integrated first-order MA model, and a fractional equal root integrated MA model. For statistical inference we show that, under mild regularity conditions, the Whittle pseudo-maximum likelihood estimator of the model parameters is consistent and asymptotically normal, also in the nonstationary case.
TI - Component-wise Representations of Long-memory Models and Volatility Prediction
JO - Journal of Financial Econometrics
DO - 10.1093/jjfinec/nbw004
DA - 2016-09-01
UR - https://www.deepdyve.com/lp/oxford-university-press/component-wise-representations-of-long-memory-models-and-volatility-lHQ8C2cAig
SP - 668
EP - 692
VL - 14
IS - 4
DP - DeepDyve
ER -