Time-scale transformations: Effects on VaR models

Fabio Lamantia, Sergio Ortobelli, Svetlozar Rachev

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This paper investigates the effects of using temporal aggregation rules in the evaluation of the maximum portfolio loss1. In particular, we propose and compare different time aggregation rules for VaR models. We implement time-scale transformations for: (i) a EWMA model with Student's t conditional distributions, (ii) a stable sub-Gaussian model, (iii) a stable asymmetric model. All models are subjected to backtest on out-of-sample data in order to assess their forecasting power and to show how these aggregation rules perform in practice.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsMarian Bubak, Geert Dick van Albada, Peter M. A. Sloot, Jack J. Dongarra
PublisherSpringer-Verlag
Pages779-786
Number of pages8
ISBN (Print)3540221298
DOIs
StatePublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3039
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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  • Cite this

    Lamantia, F., Ortobelli, S., & Rachev, S. (2004). Time-scale transformations: Effects on VaR models. In M. Bubak, G. D. van Albada, P. M. A. Sloot, & J. J. Dongarra (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 779-786). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3039). Springer-Verlag. https://doi.org/10.1007/978-3-540-25944-2_101