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Nonparametric sharpe ratio function estimation in heteroscedastic regression models via convex optimization
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Seung-Jean | - |
dc.contributor.author | Lim, Johan | - |
dc.contributor.author | Won, Joong-Ho | - |
dc.date.accessioned | 2023-10-30T01:51:01Z | - |
dc.date.available | 2023-10-30T01:51:01Z | - |
dc.date.created | 2023-08-25 | - |
dc.date.created | 2023-08-25 | - |
dc.date.issued | 2018-01 | - |
dc.identifier.citation | International Conference on Artificial Intelligence and Statistics, AISTATS 2018, Vol.84, pp.1495-1504 | - |
dc.identifier.issn | 2640-3498 | - |
dc.identifier.uri | https://hdl.handle.net/10371/195917 | - |
dc.description.abstract | Copyright 2018 by the author(s).We consider maximum likelihood estimation (MLE) of heteroscedastic regression models based on a new parametrization of the likelihood in terms of the Sharpe ratio function, or the ratio of the mean and volatility functions. While with a standard parametrization the MLE problem is not convex and hence hard to solve globally, our parametrization leads to a functional that is jointly convex in the Sharpe ratio and inverse volatility functions. The major difficulty with the resulting infinite-dimensional convex program is the shape constraint on the inverse volatility function. We propose to solve the problem by solving a sequence of finite-dimensional convex programs with increasing dimensions, which can be done globally and efficiently. We demonstrate that, when the goal is to estimate the Sharpe ratio function directly, the finite-sample performance of the proposed estimation method is superior to existing methods that estimate the mean and variance functions separately. When applied to a financial dataset, our method captures a well-known covariate-dependent effect on the Shape ratio. | - |
dc.language | 영어 | - |
dc.publisher | PMLR | - |
dc.title | Nonparametric sharpe ratio function estimation in heteroscedastic regression models via convex optimization | - |
dc.type | Article | - |
dc.citation.journaltitle | International Conference on Artificial Intelligence and Statistics, AISTATS 2018 | - |
dc.identifier.wosid | 000509385300156 | - |
dc.identifier.scopusid | 2-s2.0-85067784413 | - |
dc.citation.endpage | 1504 | - |
dc.citation.startpage | 1495 | - |
dc.citation.volume | 84 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Won, Joong-Ho | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.subject.keywordPlus | VARIANCE FUNCTION ESTIMATION | - |
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