Machine Learning Risk Models

We give an explicit algorithm and source code for constructing risk models based on machine learning techniques. The resultant covariance matrices are not factor models. Based on empirical backtests, we compare the performance of these machine learning risk models to other constructions, including statistical risk models, risk models based on fundamental industry classifications, and also those utilizing multilevel clustering based industry classifications

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Year of publication:	2019
Authors:	Kakushadze, Zura
Other Persons:	Yu, Willie (contributor)
Publisher:	[2019]: [S.l.] : SSRN
Subject:	Künstliche Intelligenz \| Artificial intelligence \| Risikomanagement \| Risk management \| Lernprozess \| Learning process \| Lernen \| Learning

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Extent:	1 Online-Ressource (26 p)
Type of publication:	Book / Working Paper
Language:	English
Notes:	In: Journal of Risk & Control 6(1) (2019) 37-64 Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments January 1, 2019 erstellt
Other identifiers:	10.2139/ssrn.3308964 [DOI]
Classification:	G00 - Financial Economics. General ; G10 - General Financial Markets. General ; G11 - Portfolio Choice ; G12 - Asset Pricing ; G23 - Pension Funds; Other Private Financial Institutions
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10012895821