Article2014

ECONOMETRIC ANALYSIS OF VOLATILITY COMPONENT MODELS

65 citations0/0 files storedEconometric Theory

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Financial Risk and Volatility Modeling Stochastic processes and financial applications Complex Systems and Time Series Analysis

Abstract

Volatility component models have received considerable attention recently, not only because of their ability to capture complex dynamics via a parsimonious parameter structure, but also because it is believed that they can handle well structural breaks or nonstationarities in asset price volatility. This paper revisits component volatility models from a statistical perspective and attempts to explore the stationarity of the underlying processes. There is a clear need for such an analysis, since any discussion about nonstationarity presumes we know when component models are stationary. As it turns out, this is not the case and the purpose of the paper is to rectify this. We also look into the sampling behavior of the maximum likelihood estimates of recently proposed volatility component models and establish their consistency and asymptotic normality.

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References

1986Generalized autoregressive conditional heteroskedasticity 1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation 1989Why Does Stock Market Volatility Change Over Time?1992Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances 1980On estimating the expected return on the market 2002Range‐Based Estimation of Stochastic Volatility Models 1994Use of Cumulative Sums of Squares for Retrospective Detection of Changes of Variance 2012Stock Market Volatility and Macroeconomic Fundamentals

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