Abstract
In this paper, we analyze the inherent evolutionary dynamics of financial and energy markets, study their interrelationships and carrying out predictive analysis tasks in an integrated nonparametric framework. We consider the daily closing prices of Brent Crude Oil, DJIA Index, Shenzhen Component Index (SZSE) from January 2012 to January 2017 from the ‘Wind’ database for financial markets. First, we investigate the empirical characteristics of the underlying temporal dynamics of the financial time series through technique of nonlinear dynamics to extract the key insights. Results suggest the existence of strong trend component and long-range dependence as the underlying pattern. Then we apply the RP and RQA to investigate the co-movements of the considered assets. Long and medium range co-movements among the heterogeneous assets are discovered. The findings of dynamic time varying association reveal interesting insights that may assist portfolio managers in mitigating risk, which can effectively be used for trading purposes.
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