Estimation of Time-Varying Long Memory Parameter Using Wavelet Method |
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Authors: | Zhiping Lu Dominique Guegan |
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Affiliation: | 1. Research Center of International Finance and Risk Management, School of Finance and Statistics , East China Normal University , Shanghai, P.R. China zplu@sfs.ecnu.edu.cn;3. Paris School of Economics, CES-MSE , Uiversité Pari's 1 Pantheon-Sorbonne , Paris, France |
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Abstract: | Stationary long memory processes have been extensively studied over the past decades. When we deal with financial, economic, or environmental data, seasonality and time-varying long-range dependence can often be observed and thus some kind of non-stationarity exists. To take into account this phenomenon, we propose a new class of stochastic processes: locally stationary k-factor Gegenbauer process. We present a procedure to estimate consistently the time-varying parameters by applying discrete wavelet packet transform. The robustness of the algorithm is investigated through a simulation study. And we apply our methods on Nikkei Stock Average 225 (NSA 225) index series. |
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Keywords: | Discrete wavelet packet transform Gegenbauer process Non stationarity Ordinary least square estimation |
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