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Understanding temporal aggregation effects on kurtosis in financial indices

Understanding temporal aggregation effects on kurtosis in financial indices
Understanding temporal aggregation effects on kurtosis in financial indices

Indices of financial returns typically display sample kurtosis that declines towards the Gaussian value 3 as the sampling interval increases. This paper uses stochastic unit root (STUR) and continuous time analysis to explain the phenomenon. Limit theory for the sample kurtosis reveals that STUR specifications provide two sources of excess kurtosis, both of which decline with the sampling interval. Limiting kurtosis is shown to be random and is a functional of the limiting price process. Using a continuous time version of the model under no-drift, local drift, and drift inclusions, we suggest a new continuous time kurtosis measure for financial returns that assists in reconciling these models with the empirical kurtosis characteristics of returns. Simulations are reported and applications to several financial indices demonstrate the usefulness of this approach.

Autoregression, Diffusion, Kurtosis, Stochastic unit root, Time-varying coefficients
0304-4076
Lieberman, Offer
dc3ceb23-c100-41be-8d88-7c80625fd3fb
Phillips, Peter Charles Bonest
f67573a4-fc30-484c-ad74-4bbc797d7243
Lieberman, Offer
dc3ceb23-c100-41be-8d88-7c80625fd3fb
Phillips, Peter Charles Bonest
f67573a4-fc30-484c-ad74-4bbc797d7243

Lieberman, Offer and Phillips, Peter Charles Bonest (2020) Understanding temporal aggregation effects on kurtosis in financial indices. Journal of Econometrics. (doi:10.1016/j.jeconom.2020.07.035).

Record type: Article

Abstract

Indices of financial returns typically display sample kurtosis that declines towards the Gaussian value 3 as the sampling interval increases. This paper uses stochastic unit root (STUR) and continuous time analysis to explain the phenomenon. Limit theory for the sample kurtosis reveals that STUR specifications provide two sources of excess kurtosis, both of which decline with the sampling interval. Limiting kurtosis is shown to be random and is a functional of the limiting price process. Using a continuous time version of the model under no-drift, local drift, and drift inclusions, we suggest a new continuous time kurtosis measure for financial returns that assists in reconciling these models with the empirical kurtosis characteristics of returns. Simulations are reported and applications to several financial indices demonstrate the usefulness of this approach.

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OLPCB-Kurt-Rev2-1-with-figures - Accepted Manuscript
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Accepted/In Press date: 19 July 2020
e-pub ahead of print date: 13 August 2020
Additional Information: Funding Information: Support from Israel Science Foundation grant No. 1182-17 is gratefully acknowledged.Research support is acknowledged from the Kelly Fund at the University of Auckland and the National Science Foundation under Grant No. SES 18-50860. Publisher Copyright: © 2020 Elsevier B.V. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
Keywords: Autoregression, Diffusion, Kurtosis, Stochastic unit root, Time-varying coefficients

Identifiers

Local EPrints ID: 444578
URI: http://eprints.soton.ac.uk/id/eprint/444578
ISSN: 0304-4076
PURE UUID: 52c82fcd-3a9e-4985-a8f4-b12c84ab6619
ORCID for Peter Charles Bonest Phillips: ORCID iD orcid.org/0000-0003-2341-0451

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Date deposited: 26 Oct 2020 17:31
Last modified: 17 Mar 2024 05:59

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Author: Offer Lieberman

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