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Student processes

Part of: Inference from stochastic processes Stochastic processes Distribution theory - Probability

Published online by Cambridge University Press:  01 July 2016

C. C. Heyde  and
N. N. Leonenko
C. C. Heyde*
Affiliation:
Australian National University and Columbia University
N. N. Leonenko*
Affiliation:
Cardiff University
*
∗ Postal address: Mathematical Sciences Institute, Australian National University, Canberra, ACT 0200, Australia. Email address: [email protected]
∗∗ Postal address: Cardiff School of Mathematics, Cardiff University, Senghennydd Road, Cardiff CF24 4AG, UK. Email address: [email protected]
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Abstract

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Stochastic processes with Student marginals and various types of dependence structure, allowing for both short- and long-range dependence, are discussed in this paper. A particular motivation is the modelling of risky asset time series.

Keywords

Student distributionOrnstein-Uhlenbeck-type processself-decomposabilitylong-range dependence

MSC classification

Primary: 60E99: None of the above, but in this section 60G10: Stationary processes 60G35: Signal detection and filtering 62M10: Time series, auto-correlation, regression, etc.
Type
General Applied Probability
Information
Advances in Applied Probability , Volume 37 , Issue 2 , June 2005 , pp. 342 - 365
Copyright
Copyright © Applied Probability Trust 2005 

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