Abstract
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 language | English |
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| Title of host publication | IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 1-6 June 2008, Hong Kong |
| Place of Publication | Piscataway |
| Publisher | Institute of Electrical and Electronics Engineers |
| Pages | 2109-2116 |
| ISBN (Print) | 978-1-4244-1818-3 |
| DOIs | |
| Publication status | Published - 2008 |
| Event | 2008 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2008) - Hong Kong Convention and Exhibition Centre, Hong Kong, Hong Kong Duration: 1 Jun 2008 → 6 Jun 2008 |
Conference
| Conference | 2008 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2008) |
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| Abbreviated title | FUZZ-IEEE 2008 |
| Country/Territory | Hong Kong |
| City | Hong Kong |
| Period | 1/06/08 → 6/06/08 |
| Other | Conference held at the 2008 IEEE World Congress on Computational Intelligence (WCCI 2008) |