Hypothesis Testing Fusion for Nonlinearity Detection in Hedge Fund Price Returns

Jean-Marc Le Caillec (jm.lecaillec@imt-atlantique.fr)
Additional contact information
Jean-Marc Le Caillec: IMT Atlantique - ITI - Département lmage et Traitement Information - IMT Atlantique - IMT Atlantique - IMT - Institut Mines-Télécom [Paris]

Post-Print from HAL

Abstract: In this paper, we present the results of nonlinearity detection in Hedge Fund price returns. The main challenge is induced by the small length of the time series, since the return of this kind of asset is updated once a month. As usual, the nonlinearity of the return time series is a key point to accurately assess the risk of an asset, since the normality assumption is barely encountered in financial data. The basic idea to overcome the hypothesis testing lack of robustness on small time series is to merge several hypothesis tests to improve the final decision (i.e., the return time series is linear or not). Several aspects on the index/decision fusion, such as the fusion topology, as well as the shared information by several hypothesis tests, have to be carefully investigated to design a robust decision process. This designed decision rule is applied to two databases of Hedge Fund price return (TASS and SP). In particular, the linearity assumption is generally accepted for the factorial model. However, funds having detected nonlinearity in their returns are generally correlated with exchange rates. Since exchange rates nonlinearly evolve, the nonlinearity is explained by this risk factor and not by a nonlinear dependence on the risk factors.

Keywords: nonlinearity detection; decision fusion; hedge funds; price return model (search for similar items in EconPapers)
Date: 2022
New Economics Papers: this item is included in nep-rmg
Note: View the original document on HAL open archive server: https://hal.science/hal-03739132v1
References: View references in EconPapers View complete reference list from CitEc
Citations:

Published in Algorithms, 2022, 15 (8), pp.260. ⟨10.3390/a15080260⟩

Downloads: (external link)
https://hal.science/hal-03739132v1/document (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03739132

DOI: 10.3390/a15080260

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD (hal@ccsd.cnrs.fr).