Realized volatility, GARCH models & chaos theory

• Main Author: Jayasuriya, Dulani
• Format: text
• Published: Animo Repository 2014
• Subjects: Stocks; Finance and Financial Management
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/6741
• Institution: De La Salle University

This study applies the BDS test to identify whether financial market data are driven by chaos theory and identified finacial time series for modelling that display non-random behavior. Subsequently, an empirical analysis of univariate and multivariate garch models are implemented for several financial time series. Finally, the expanding literature on realized volatility is reviewed. After presenting a general univariate framework for estimating realized volatilities, a simple discrete time model is presented. Cases with and without microstructure noise are considered, and it is shown that microstructure noise cause severe problems in terms of consistent estimation of the daily realized volatility. The most important methods for providing consistent estimators are presented, and a critical exposition of different techniques is given. The main empirical findings using univariate and multivariate methods are summarized. Our paper gives evidence for the presence of low complexity chaotic behavior in stock returns.