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The sample autocorrelation function and the detection of long-memory processes

Hassani, Hossein; Leonenko, Nikolai; Patterson, Kerry - In: Physica A: Statistical Mechanics and its Applications 391 (2012) 24, pp. 6367-6379

The detection of long-range dependence in time series analysis is an important task to which this paper contributes by showing that whilst the theoretical definition of a long-memory (or long-range dependent) process is based on the autocorrelation function, it is not possible for long memory to...

Persistent link: https://www.econbiz.de/10011059967

A note on the sum of the sample autocorrelation function

Hassani, Hossein - In: Physica A: Statistical Mechanics and its Applications 389 (2010) 8, pp. 1601-1606

It is shown that the sum of the sample autocorrelation function at lag h≥1 is always −12 for any stationary time series with arbitrary length T≥2 (Hassani, 2009 [1]). In this paper, the distribution of a set of the sample autocorrelation function using the properties of this quantity is...

Persistent link: https://www.econbiz.de/10011063467

Long range dependence in the high frequency USD/INR exchange rate

Kumar, Dilip - In: Physica A: Statistical Mechanics and its Applications 396 (2014) C, pp. 134-148

Using high frequency data, this paper examines the long memory property in the unconditional and conditional volatility of the USD/INR exchange rate at different time scales using the Local Whittle (LW), the Exact Local Whittle (ELW) and the FIAPARCH models. Results indicate that the long memory...

Persistent link: https://www.econbiz.de/10010730347

Blocks adjustment—reduction of bias and variance of detrended fluctuation analysis using Monte Carlo simulation

Michalski, Sebastian - In: Physica A: Statistical Mechanics and its Applications 387 (2008) 1, pp. 217-242

examined using ARFIMA(p,d,q) generator. Due to the bias of the estimator for anti-persistent processes, we narrowed down the …

Persistent link: https://www.econbiz.de/10011062109

Empirical study of ARFIMA model based on fractional differencing

Xiu, Jin; Jin, Yao - In: Physica A: Statistical Mechanics and its Applications 377 (2007) 1, pp. 138-154

In this paper, we studied the long-term memory of Hong Kong Hang Sheng index using MRS analysis, established ARFIMA … model for it, and detailed the procedure of fractional differencing. Furthermore, we compared the ARFIMA model built by this … information of time series would be lost. The forecast formula of ARFIMA model was corrected according to the method of fractional …

Persistent link: https://www.econbiz.de/10010588698