STOCHASTIC VOLATILITY MODELS FOR FINANCIAL TIME SERIES ANALYSIS

This article highlights a comprehensive and approachable perspective to stochastic volatility models for financial time series analysis. Financial time series represent a distinctive category in the economic field, with highly dynamic characteristics, especially in times of financial crisis. Beyond its highly empirical behavior, modeling volatility of financial asset returns aims to improve forecast accuracy. The stochastic volatility models analyzed in this article include the autoregressive conditional heteroscedastic model (ARCH), the generalized autoregressive conditional heteroscedastic (GARCH) model and the exponential generalized autoregressive conditional heteroscedastic (EGARCH) model.