Value-at-risk estimation by using probabilistic fuzzy systems

D. Xu, U. Kaymak

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

15 Citations (Scopus)
2 Downloads (Pure)


Value at Risk (VaR) measures the worst expected loss of a portfolio over a given horizon at a given confidence level. It summarises the financial risk a company faces into one single number. Recent methods of VaR estimation use parametric conditional models of portfolio volatility to adapt risk estimation to changing market conditions. However, more flexible methods that adapt to the underlying data distribution would be better suited for VaR estimation. In this paper, we consider VaR estimation by using probabilistic fuzzy systems, a semi-parametric method, which combines a linguistic description of the system behaviour with statistical properties of data. The performance of the proposed model is compared to the performance of a GARCH model for VaR estimation. It is found that statistical back testing always accepts PFS models after tuning, while GARCH models may be rejected.
Original languageEnglish
Title of host publicationIEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 1-6 June 2008, Hong Kong
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Print)978-1-4244-1818-3
Publication statusPublished - 2008
Event2008 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2008) - Hong Kong Convention and Exhibition Centre, Hong Kong, Hong Kong
Duration: 1 Jun 20086 Jun 2008


Conference2008 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2008)
Abbreviated titleFUZZ-IEEE 2008
Country/TerritoryHong Kong
CityHong Kong
OtherConference held at the 2008 IEEE World Congress on Computational Intelligence (WCCI 2008)


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