A Class of Antipersistent Processes

We introduce a class of stationary processes characterized by the behaviour of their infinite moving average parameters. We establish the asymptotic behaviour of the covariance function and the behaviour around zero of the spectral density of these processes, showing their antipersistent character. Then, we discuss the existence of an infinite autoregressive representation for this family of processes, and we present some consequences for fractional autoregressive moving average models.

[1]  A. Inoue Regularly varying correlation functions and KMO-Langevin equations , 1997 .

[2]  Piotr Kokoszka,et al.  Fractional ARIMA with stable innovations , 1995 .

[3]  M Ausloos,et al.  Power-law correlations in the southern-oscillation-index fluctuations characterizing El Niño. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  F. Topsøe Banach algebra methods in prediction theory , 1977 .

[5]  C. Granger,et al.  Properties of Nonlinear Transformations of Fractionally Integrated Processes , 2000 .

[6]  Toshiaki Watanabe,et al.  Modeling and Forecasting the Volatility of the Nikkei 225 Realized Volatility Using the ARFIMA-GARCH Model , 2009 .

[7]  Clive W. J. Granger,et al.  Properties of Nonlinear Transformations of Fractionally Integrated Processes , 2000 .

[8]  Richard A. Davis,et al.  Time Series: Theory and Methods (2Nd Edn) , 1993 .

[9]  Christopher F. Baum,et al.  Long Term Dependence in Stock Returns , 1996 .

[10]  Clive W. J. Granger,et al.  An introduction to long-memory time series models and fractional differencing , 2001 .

[11]  Jan Beran,et al.  Maximum Likelihood Estimation of the Differencing Parameter for Invertible Short and Long Memory Autoregressive Integrated Moving Average Models , 1995 .

[12]  A. Ian McLeod,et al.  Hyperbolic Decay Time Series , 1998, 1611.00826.

[13]  E. J. Hannan,et al.  Multiple time series , 1970 .

[14]  Murad S. Taqqu,et al.  Theory and applications of long-range dependence , 2003 .

[15]  Larry S. Liebovitch,et al.  TRANSITION FROM PERSISTENT TO ANTIPERSISTENT CORRELATION IN BIOLOGICAL SYSTEMS , 1997 .

[16]  C. Granger,et al.  AN INTRODUCTION TO LONG‐MEMORY TIME SERIES MODELS AND FRACTIONAL DIFFERENCING , 1980 .

[17]  W. Rudin Principles of mathematical analysis , 1964 .

[18]  Richard A. Davis,et al.  Time Series: Theory and Methods , 2013 .

[19]  Asymptotics for the partial autocorrelation function of a stationary process , 2000 .

[20]  Jan Beran,et al.  Local Polynomial Fitting with Long-Memory, Short-Memory and Antipersistent Errors , 2002 .

[21]  N. Davies Multiple Time Series , 2005 .

[22]  Long-memory analysis of time series with missing values. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  Gabor Szegö,et al.  A problem in prediction theory , 1960 .

[24]  A Inoue Asymptotic behaviour for partial autocorrelation functions of fractional ARIMA processes , 2000 .

[25]  Richard A. Davis,et al.  Time Series: Theory and Methods (2nd ed.). , 1992 .

[26]  E. Parzen Foundations of Time Series Analysis and Prediction Theory , 2002 .

[27]  P. Robinson,et al.  The memory of stochastic volatility models , 2001 .

[28]  Oâ Lan T. Henry Long memory in stock returns: some international evidence , 2002 .

[29]  Partial autocorrelation functions of the fractional ARIMA processes with negative degree of differencing , 2000 .