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Ouvert le : Thursday 15 May 2025, 00:00À rendre : Monday 9 June 2025, 18:00
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The Original paper of the introduction of the ARCH model in 1982, by Robert F. Engle. This paper is published in the prestigious peer review journal - Econometrica -
Abstract
Traditional econometric models assume a constant one-period forecast variance. To generalize this implausible assumption, a new class of stochastic processes called autoregressive conditional heteroscedastic (ARCH) processes are introduced in this paper. These are mean zero, serially uncorrelated processes with nonconstant variances conditional on the past, but constant unconditional variances. For such processes, the recent past gives information about the one-period forecast variance. A regression model is then introduced with disturbances following an ARCH process. Maximum likelihood estimators are described and a simple scoring iteration formulated. Ordinary least squares maintains its optimality properties in this set-up, but maximum likelihood is more efficient. The relative efficiency is calculated and can be infinite. To test whether the disturbances follow an ARCH process, the Lagrange multiplier procedure is employed. The test is based simply on the autocorrelation of the squared OLS residuals. This model is used to estimate the means and variances of inflation in the U.K. The ARCH effect is found to be significant and the estimated variances increase substantially during the chaotic seventies. -
This is the original paper of the introduction of the GARCH model in 1986, by Tim Bollerslev. This paper is published by the MIT (Massachusetts Institute of Technology, the best University in the world) Press, in the peer review journal - The Review of Economics and Statistics -
Abstract
The distribution of speculative price changes and rates of return data tend to be uncorrelated over time but characterized by volatile and tranquil periods. A simple time series model designed to capture this dependence is presented. The model is an extension of the Autoregressive Conditional Heteroskedastic (ARCH) and Generalized ARCH (GARCH) models obtained by allowing for conditionally t-distributed errors. The model can be derived as a simple subordinate stochastic process by including an additive unobservable error term in the conditional variance equation. The descriptive validity of the model is illustrated for a set of foreign exchange rates and stock price indices. -
Nobel Laureate Professor Robert Engle, who was awarded the 2003 Nobel Prize in Economics for his research on the concept of autoregressive conditional heteroskedasticity (ARCH), joined UBS for a video series exploring the use of the ARCH model and its ability to forecast financial trends. Professor Engle developed his method for statistical modeling of time-varying volatility and demonstrated that the techniques accurately capture the properties of many time series. The seven-part UBS video series, featuring Professor Engle and Volatility Lab (V-Lab) Director Rob Capellini, can be seen here.
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Tim Bollerslev from Duke University receives the prize for his highly recognised research within finance and time-series econometrics. He is particularly recognised for his knowledge in the areas of financial econometrics and empirical finance, in which fields, he belongs to the absolute world elite.
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Auteurs : Régis Bourbonnais, Michel Terraza
Cet ouvrage développe les méthodes d'analyse des séries temporelles : méthodes standard de traitement (régression, désaisonnalisation, lissage exponentiel) et techniques plus modernes (analyse spectrale, étude de station narisation, modèles ARIMA, modèles ARCH). Ces techniques s'appliquent à diverses disciplines, telles la prévision macroéconomique, la finance, le marketing... L'alternance systématique du cours et des exercices permet une mise en pratique rapide des connaissances avec l'utilisation de logiciels. Un site internet compagnon permet au lecteur de télécharger les séries statistiques utilisées et les programmes de traitement. Cette troisième édition propose de nouveaux exercices ainsi que les développements les plus récents de la discipline.
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A comprehensive guide to financial econometrics
Financial econometrics is a quest for models that describe financial time series such as prices, returns, interest rates, and exchange rates. In Financial Econometrics, readers will be introduced to this growing discipline and the concepts and theories associated with it, including background material on probability theory and statistics. The experienced author team uses real-world data where possible and brings in the results of published research provided by investment banking firms and journals. Financial Econometrics clearly explains the techniques presented and provides illustrative examples for the topics discussed.
Svetlozar T. Rachev, PhD (Karlsruhe, Germany) is currently Chair-Professor at the University of Karlsruhe. Stefan Mittnik, PhD (Munich, Germany) is Professor of Financial Econometrics at the University of Munich. Frank J. Fabozzi, PhD, CFA, CFP (New Hope, PA) is an adjunct professor of Finance at Yale University’s School of Management. Sergio M. Focardi (Paris, France) is a founding partner of the Paris-based consulting firm The Intertek Group. Teo Jasic, PhD, (Frankfurt, Germany) is a senior manager with a leading international management consultancy firm in Frankfurt.
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