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Joint exceedances of the ARCH process

Published online by Cambridge University Press:  14 July 2016

M. Ivette Gomes*
Affiliation:
Universidade de Lisboa
Laurens de Haan*
Affiliation:
Erasmus University of Rotterdam
Dinis Pestana*
Affiliation:
Universidade de Lisboa
*
Postal address: CEAUL and DEIO (FUCL), Universidade de Lisboa, Lisboa 1749-016, Portugal
∗∗ Postal address: Econometric Institute, Erasmus University of Rotterdam, PO Box 1738, NL 3000 DR Rotterdam, The Netherlands. Email address: ldehaan@few.eur.nl
Postal address: CEAUL and DEIO (FUCL), Universidade de Lisboa, Lisboa 1749-016, Portugal

Abstract

We examine the joint finite structure of extremes of the ARCH process and find an unexpected phenomenon: when assessing probabilities of failure during some finite time interval in the future, the extremal index seems not to be the object to look at. Two possible ramifications of this phenomenon are put forward.

Type
Short Communications
Copyright
Copyright © Applied Probability Trust 2004 

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References

Alpuim, M. T. (1988). An extremal Markovian sequence. J. Appl. Prob. 26, 219232.10.2307/3214030Google Scholar
Barão, M. I., de Haan, L., and Li, D. (2003). Comparison of estimators in multivariate extreme value theory. Submitted.Google Scholar
Basrak, B., Davis, R., and Mikosch, T. (2002). Regular variation of GARCH processes. Stoch. Process. Appl. 99, 95115.10.1016/S0304-4149(01)00156-9Google Scholar
Breiman, L. (1965). On some limit theorems similar to the arc-sin law. Theory Prob. Appl. 10, 323331.10.1137/1110037Google Scholar
De Haan, L., and Themido Pereira, T. (2002). Spatial extremes: the stationary case. Submitted.Google Scholar
De Haan, L., Resnick, S. I., Rootzén, H., and de Vries, C. G. (1989). Extremal behaviour of solutions to a stochastic difference equation with applications to ARCH processes. Stoch. Process. Appl. 32, 213224.10.1016/0304-4149(89)90076-8Google Scholar
Einmahl, J. H. J., and Lin, T. (2003). Asymptotic normality of extreme value estimation on C[0,1]. Submitted.Google Scholar
Embrechts, P., Goldie, C., and Veraverbeke, N. (1979). Subexponentiality and infinite divisibility. Z. Wahrscheinlichkeitsth. 49, 335347.10.1007/BF00535504Google Scholar
Engle, R. F. (1982). Autoregressive conditional heteroscedastic models with estimates of the variance of United Kingdom inflation. Econometrica 50, 9871007.10.2307/1912773Google Scholar
Feller, W. (1971). An Introduction to Probability Theory and Its Applications, Vol. 2, 2nd edn. John Wiley, New York.Google Scholar
Goldie, C. M. (1991). Implicit renewal theory and tails of solutions of random equations. Ann. Appl. Prob. 1, 126166.10.1214/aoap/1177005985Google Scholar
Kesten, H. (1973). Random difference equations and renewal theory for products of random matrices. Acta Math. 131, 207248.10.1007/BF02392040Google Scholar
Leadbetter, M. R., Lindgren, G. and Rootzén, H. (1983). Extremes and Related Properties of Random Sequences and Processes. Springer, Berlin.10.1007/978-1-4612-5449-2Google Scholar
Vervaat, W. (1979). On a stochastic differential equation and a representation of non-negative infinite divisible random variables. Adv. Appl. Prob. 11, 750783.10.2307/1426858Google Scholar