NDLI: A multi-covariate semi-parametric conditional volatility model using probabilistic fuzzy systems

Content Provider	IEEE Xplore Digital Library
Author	Almeida, R.J. Basturk, N. Kaymak, U. Milea, V.
Copyright Year	2012
Description	Author affiliation: Erasmus University Rotterdam, Erasmus School of Economics, Department of Econometrics, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands (Almeida, R.J.; Basturk, N.; Milea, V.) \|\| School of Industrial Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands (Kaymak, U.)
Abstract	Value at Risk (VaR) has been successfully estimated using single covariate probabilistic fuzzy systems (PFS), a method which combines a linguistic description of the system behaviour with statistical properties of data. In this paper, we consider VaR estimation based on a PFS model for density forecast of a continuous response variable conditional on a high-dimensional set of covariates. The PFS model parameters are estimated by a novel two-step process. The performance of the proposed model is compared to the performance of a GARCH model for VaR estimation of the S&P 500 index. Furthermore, the additional information and process understanding provided by the different interpretations of the PFS models are illustrated. Our findings show that the validity of GARCH models are sometimes rejected, while those of PFS models of VaR are never rejected. Additionally, the PFS model captures both instant and periods of high volatility, and leads to less conservative models.
Starting Page	1
Ending Page	8
File Size	288897
Page Count	8
File Format	PDF
ISBN	9781467318020
e-ISBN	9781467318037
e-ISBN	9781467318013
DOI	10.1109/CIFEr.2012.6327765
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-03-29
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Reactive power Histograms Estimation Probabilistic logic Cognition Portfolios Fuzzy systems
Content Type	Text
Resource Type	Article

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