Analysis of Causal Interactions and Predictive Modelling of Financial Markets Using Econometric Methods, Maximal Overlap Discrete Wavelet Transformation and Machine Learning: A Study in Asian Context

摘要

Proper understanding of dynamics of equity markets in long run and short run is extremely critical for investors, speculators and arbitrageurs. It is essential to delve into causal interrelationships among different financial markets in order to assess the impact of ongoing inter country trades and forecast future movements. In this paper, initially effort has been made to comprehend the nature of temporal movements and interactions among four Asian stock indices namely, Bombay Stock Exchange (BSE), Taiwan Stock Exchange (TWSE), Jakarta Stock Exchange (JSX) and Korea Composite Stock Price Exchange (KOSPI) through conventional Econometric and Statistical methods. Subsequently a granular forecasting model comprising Maximal Overlap Discrete Wavelet Transformation (MODWT) and Support Vector Regression (S VR) has been utilized to predict the future prices of the respective indices in univariate framework.