On conditional risk estimation considering model risk. Issue 8 (10th June 2016)

• Record Type: Journal Article
• Authors: Telmoudi, Fedya; EL Ghourabi, Mohamed; Limam, Mohamed
• Is Part Of: Journal of applied statistics. Volume 43:Issue 8(2016)
• Page Start: 1386
• Page End: 1399
• Publication Date: 2016-06-10
• Subjects: artificial intelligence models -- conditional value at risk -- LS-SVR -- GARCH models -- modelrisk -- spareness; C10 -- C14 -- C41 -- C45 -- C53; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2015.1100595
• Languages: English
• ISSNs: 0266-4763

Abstract

Abstract : Usually, parametric procedures used for conditional variance modelling are associated with model risk. Model risk may affect the volatility and conditional value at risk estimation process either due to estimation or misspecification risks. Hence, non-parametric artificial intelligence models can be considered as alternative models given that they do not rely on an explicit form of the volatility. In this paper, we consider the least-squares support vector regression (LS-SVR), weighted LS-SVR and Fixed size LS-SVR models in order to handle the problem of conditional risk estimation taking into account issues of model risk. A simulation study and a real application show the performance of proposed volatility and VaR models.