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Asymmetric heavy-tailed distribution...

McGill University (Canada).

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Asymmetric heavy-tailed distributions: Theory and applications to finance and risk management.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Asymmetric heavy-tailed distributions: Theory and applications to finance and risk management./
Author:	Zhu, Dongming.
Description:	174 p.
Notes:	Source: Dissertation Abstracts International, Volume: 68-10, Section: A, page: 4417.
Contained By:	Dissertation Abstracts International68-10A.
Subject:	Economics, Theory. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NR32338
ISBN:	9780494323380

Asymmetric heavy-tailed distributions: Theory and applications to finance and risk management.
Zhu, Dongming.

Asymmetric heavy-tailed distributions: Theory and applications to finance and risk management. - 174 p.

Source: Dissertation Abstracts International, Volume: 68-10, Section: A, page: 4417.

Thesis (Ph.D.)--McGill University (Canada), 2007.

This thesis focuses on construction, properties and estimation of asymmetric heavy-tailed distributions, as well as on their applications to financial modeling and risk measurement. First of all, we suggest a general procedure to construct a fully asymmetric distribution based on a symmetrically parametric distribution, and establish some natural relationships between the symmetric and asymmetric distributions. Then, three new classes of asymmetric distributions are proposed by using the procedure: the Asymmetric Exponential Power Distributions (AEPD), the Asymmetric Student-t Distributions (ASTD) and the Asymmetric Generalized t Distribution (AGTD). For the first two distributions, we give an interpretation of their parameters and explore basic properties of them, including moments, expected shortfall, characterization by the maximum entropy property, and the stochastic representation. Although neither distribution satisfies the regularity conditions under which the ML estimators have the usual asymptotics, due to a non-differentiable likelihood function, we nonetheless establish asymptotics for the full MLE of the parameters. A closed-form expression for the Fisher information matrix is derived, and Monte Carlo studies are provided. We also illustrate the usefulness of the GARCH-type models with the AEPD and ASTD innovations in the context of predicting downside market risk of financial assets and demonstrate their superiority over skew-normal and skew-Student's t GARCH models. Finally, two new classes of generalized extreme value distributions, which include Jenkinson's GEV (Generalized Extreme Value) distribution (Jenkinson, 1955) as special cases, are proposed by using the maximum entropy principle, and their properties are investigated in detail.

ISBN: 9780494323380Subjects--Topical Terms:

1017575
Economics, Theory.

Asymmetric heavy-tailed distributions: Theory and applications to finance and risk management.
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020 $a 9780494323380
035 $a (UMI)AAINR32338
035 $a AAINR32338
040 $a UMI $c UMI
100 1 $a Zhu, Dongming. $3 1026719
245 1 0 $a Asymmetric heavy-tailed distributions: Theory and applications to finance and risk management.
300 $a 174 p.
500 $a Source: Dissertation Abstracts International, Volume: 68-10, Section: A, page: 4417.
502 $a Thesis (Ph.D.)--McGill University (Canada), 2007.
520 $a This thesis focuses on construction, properties and estimation of asymmetric heavy-tailed distributions, as well as on their applications to financial modeling and risk measurement. First of all, we suggest a general procedure to construct a fully asymmetric distribution based on a symmetrically parametric distribution, and establish some natural relationships between the symmetric and asymmetric distributions. Then, three new classes of asymmetric distributions are proposed by using the procedure: the Asymmetric Exponential Power Distributions (AEPD), the Asymmetric Student-t Distributions (ASTD) and the Asymmetric Generalized t Distribution (AGTD). For the first two distributions, we give an interpretation of their parameters and explore basic properties of them, including moments, expected shortfall, characterization by the maximum entropy property, and the stochastic representation. Although neither distribution satisfies the regularity conditions under which the ML estimators have the usual asymptotics, due to a non-differentiable likelihood function, we nonetheless establish asymptotics for the full MLE of the parameters. A closed-form expression for the Fisher information matrix is derived, and Monte Carlo studies are provided. We also illustrate the usefulness of the GARCH-type models with the AEPD and ASTD innovations in the context of predicting downside market risk of financial assets and demonstrate their superiority over skew-normal and skew-Student's t GARCH models. Finally, two new classes of generalized extreme value distributions, which include Jenkinson's GEV (Generalized Extreme Value) distribution (Jenkinson, 1955) as special cases, are proposed by using the maximum entropy principle, and their properties are investigated in detail.
590 $a School code: 0781.
650 4 $a Economics, Theory. $3 1017575
690 $a 0511
710 2 $a McGill University (Canada). $3 1018122
773 0 $t Dissertation Abstracts International $g 68-10A.
790 $a 0781
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NR32338