Detecting Volatility Persistence in GARCH Models in the Presence of Leverage Effect

Most asset prices are subject to significant volatility. Arrival of new information is viewed as the main source of volatility. As new information is continually released, financial asset prices exhibit volatility persistence, which affects financial risk analysis and risk management strategies. This paper proposes a nonlinear regime switching threshold generalized autoregressive conditional heteroskedasticity (RS-TGARCH) model which can be used to analyze financial data. The empirical results based on quasi maximum likelihood estimation presented in this paper suggest that the proposed model is capable of extracting information about the sources of volatility persistence in the presence of leverage effect