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Modeling and forecasting time series of precious metals:a new approach to multifractal data

Financial Innovation 2019年第1期5卷 407-434页

作者： Emrah Oral Gazanfer Unal Faculty of Economy and Administrative Sciences Istanbul Aydin UniversityIstanbulTurkey Faculty of Economics Administrative and Social SciencesBahcesehir UniversityIstanbulTurkey

We introduce a novel approach to multifractal data in order to achieve transcended modeling and forecasting performances by extracting time series out of local Hurst exponent calculations at a specified ***,the long range and co-movement dependencies of the time series are scrutinized on time-frequency space using multiple wavelet coherence ***,the multifractal behaviors of the series are verified by multifractal de-trended fluctuation analysis and its local Hurst exponents are ***,root mean squares of residuals at the specified scale are procured from an intermediate step during local Hurst exponent *** internally calculated series have been used to estimate the process with vector autoregressive fractionally integrated moving average(VARFIMA)model and forecasted *** our study,the daily prices of gold,silver and platinum are used for *** results have shown that all metals do behave in phase movement on long term periods and possess multifractal ***,the intermediate time series obtained during local Hurst exponent calculations still appertain the co-movement as well as multifractal characteristics of the raw data and may be successfully re-scaled,modeled and forecasted by using VARFIMA ***,VARFIMA model have notably surpassed its univariate counterpart(ARFIMA)in all efficacious trials while re-emphasizing the importance of comovement procurement in *** study’s novelty lies in using a multifractal de-trended fluctuation analysis,along with multiple wavelet coherence analysis,for forecasting purposes to an extent not seen *** results will be of particular significance to finance researchers and practitioners.

关键词： Continuous wavelet transform Multiple wavelet coherence Multifractal de-trended fluctuation analysis vector autoregressive fractionally integrated moving average Forecast

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