期刊名称：International Journal of Data and Network Science
印刷版ISSN：2561-8148
电子版ISSN：2561-8156
出版年度：2019
卷号：3
期号：1
页码：23-36
DOI：10.5267/j.ijdns.2018.11.002
出版社：Growing Science
摘要：This paper presents an integrated granular framework of wavelet decomposition, DCC-GARCH, ADCC-GARCH, Diks-Panchenko nonlinear Granger’s causality and Diebold-Yilmaz spillover assessment techniques to understand temporal correlation, causal interplay and spillovers among volatile financial time series data exhibiting nonparametric behavior. The exercise has been carried out on daily closing observations of eight financial time series. Wavelet decomposition has been used to generate time varying components in which the other research models are applied to extract the interactive pattern of interaction to ascertain short and long run nexus. The findings rationalize the effectiveness of the presented research framework.
关键词：Dynamic Association; Causality; Spillover; Wavelet Decomposition; Diks-Panchenko Test; Diebold-Yilmaz Test
