Wavelet Method for Locally Stationary Seasonal Long Memory Processes
AbstractLong memory processes have been extensively studied over the past decades. When dealing with the financial and economic data, seasonality and time-varying long-range dependence can often be observed and thus some kind of non-stationarity can exist inside financial data sets. To take into account this kind of phenomena, we propose a new class of stochastic process : the locally stationary k-factor Gegenbauer process. We describe a procedure of estimating consistently the time-varying parameters by applying the discrete wavelet packet transform (DWPT). The robustness of the algorithm is investigated through simulation study. An application based on the error correction term of fractional cointegration analysis of the Nikkei Stock Average 225 index is proposed.
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Date of creation: Mar 2009
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Discrete wavelet packet transform ; Gegenbauer process ; Nikkei Stock Average 225 index ; non-stationarity ; ordinary least square estimation;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-04-25 (All new papers)
- NEP-ECM-2009-04-25 (Econometrics)
- NEP-ETS-2009-04-25 (Econometric Time Series)
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