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Applications of nonlinear stochastic discount factors in performance analysis and tail risk

Submitted by Kym Marcel Martins Ardison (kymmarcel@gmail.com) on 2018-09-16T17:36:08Z
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Previous issue date: 2018-04-12 / We propose a new class of performance measures for Hedge Fund (HF) returns based on a family of empirically identi able stochastic discount factors (SDFs). These SDF-based measures incorporate no-arbitrage pricing restrictions and naturally embed information about higher-order mixed moments between HF and benchmark
factors returns. We provide full asymptotic theory for our SDF estimators that allows us to test for the statistical signi cance of each fund's performance and for the relevance of individual benchmark factors in identifying each proposed measure. Empirically, we apply our methodology to a large panel of individual hedge fund returns, revealing sizable di erences across performance measures implied by
di erent exposures to higher-order mixed moments. Moreover, when we compare SDF-based measures to the traditional linear regression approach (Jensen's alpha), our measures identify a signi cantly smaller fraction of funds in the cross-section of HFs with statistically signi cant performances

Identiferoai:union.ndltd.org:IBICT/oai:bibliotecadigital.fgv.br:10438/25676
Date12 April 2018
CreatorsArdison, Kym Marcel Martins
ContributorsCosta, Carlos Eugênio da, Vicente, José, Laurini, Márcio Poletti, Giovannetti, Bruno Cara, Escolas::EPGE, Almeida, Caio Ibsen Rodrigues de
Source SetsIBICT Brazilian ETDs
LanguageEnglish
Detected LanguageEnglish
Typeinfo:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/doctoralThesis
Sourcereponame:Repositório Institucional do FGV, instname:Fundação Getulio Vargas, instacron:FGV
Rightsinfo:eu-repo/semantics/openAccess

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