ARCH/GARCH Models in Applied Financial Econometrics
Published Online: 15 DEC 2012
Copyright © 2013 by Frank J. Fabozzi. All rights reserved.
Encyclopedia of Financial Models
How to Cite
Engle, R. F., Focardi, S. M. and Fabozzi, F. J. 2012. ARCH/GARCH Models in Applied Financial Econometrics. Encyclopedia of Financial Models. .
- Published Online: 15 DEC 2012
Volatility is a key parameter used in many financial applications, from derivatives valuation to asset management and risk management. Volatility measures the size of the errors made in modeling returns and other financial variables. It was discovered that, for vast classes of models, the average size of volatility is not constant but changes with time and is predictable. Autoregressive conditional heteroskedasticity (ARCH), generalized autoregressive conditional heteroskedasticity (GARCH) models, and stochastic volatility models are the main tools used to model and forecast volatility. Moving from single assets to portfolios made of multiple assets, not only are there idiosyncratic volatilities but also correlations and covariances between assets that are time varying and predictable. Multivariate ARCH/GARCH models and dynamic factor models, eventually in a Bayesian framework, are the basic tools used to forecast correlations and covariances.
- autoregressive conditional duration;
- autoregressive conditional heteroskedasticity (ARCH);
- autoregressive models;
- conditional autoregressive value at risk (CAViaR);
- dynamic factor models;
- generalized autoregressive conditional heteroskedasticity (GARCH);
- exponential GARCH (EGARCH);
- high-frequency data;
- integrated GARCH (IGARCH);
- threshold ARCH (TARCH);
- temporal aggregation;
- ultra-high-frequency data;
- value at risk (VaR);