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Subordinated exchange rate models: evidence for heavy tailed distributions and long-range dependence

Marinelli, C. and Rachev, S. T. and Roll, R. (2001) Subordinated exchange rate models: evidence for heavy tailed distributions and long-range dependence. Mathematical and Computer Modelling, 34 (9-11). pp. 955-1001. ISSN 0895-7177. https://resolver.caltech.edu/CaltechAUTHORS:20190502-141143200

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Abstract

We investigate the main properties of high-frequency exchange rate data in the setting of stochastic subordination and stable modeling, focusing on heavy-tailedness and long memory, together with their dependence on the sampling period. We show that the intrinsic time process exhibits strong long-range dependence and has increments well described by a Weibull law, while the return series in intrinsic time has weak long memory and is well approximated by a stable Lévy motion. We also show that the stable domain of attraction offers a good fit to the returns in physical time, which leads us to consider as a realistic model for exchange rate data a process Z(t) subordinated to an α-stable Lévy motion S(t) (possibly fractional stable) by a long-memory intrinsic time process T(t) with Weibull-distributed increments.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/S0895-7177(01)00113-3DOIArticle
Additional Information:© 2001 Published by Elsevier Ltd. Available online 10 December 2001.
Issue or Number:9-11
Record Number:CaltechAUTHORS:20190502-141143200
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190502-141143200
Official Citation:C. Marinelli, S.T. Rachev, R. Roll, Subordinated exchange rate models: evidence for heavy tailed distributions and long-range dependence, Mathematical and Computer Modelling, Volume 34, Issues 9–11, 2001, Pages 955-1001, ISSN 0895-7177, https://doi.org/10.1016/S0895-7177(01)00113-3. (http://www.sciencedirect.com/science/article/pii/S0895717701001133)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:95188
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:03 May 2019 21:23
Last Modified:03 Oct 2019 21:11

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