GARCH Models

23 Jan 2015

Article by: David Ruppert
From: Statistics and Data Analysis for Financial Engineering
Published by: Springer New York
Date: 2010

“…financial markets data often exhibit volatility clustering, where time series show periods of high volatility and periods of low volatility;…. In fact, with economic and financial data, time-varying volatility is more common than constant volatility, and accurate modeling of time-varying volatility is of great importance in financial engineering.

“…ARMA models are used to model the conditional expectation of a process given the past, but in an ARMA model the conditional variance given the past is constant. What does this mean for, say, modeling stock returns? Suppose we have noticed that recent daily returns have been unusually volatile. We might expect that tomorrow’s return is also more variable than usual. However, an ARMA model cannot capture this type of behavior because its conditional variance is constant. So we need better time series models if we want to model the nonconstant volatility. In this chapter we look at GARCH time series models that are becoming widely used in econometrics and finance because they have randomly varying volatility.”

Full article (PDF): Link

Comments Off

Posted in Realized volatility


Comments are closed.

© Copyright 2018 RealVol LLC. All rights reserved