Estimating financial risk using piecewise Gaussian processes
AbstractWe present a computational method for measuring financial risk by estimating the Value at Risk and Expected Shortfall from financial series. We have made two assumptions: First, that the predictive distributions of the values of an asset are conditioned by information on the way in which the variable evolves from similar conditions, and secondly, that the underlying random processes can be described using piecewise Gaussian processes. The performance of the method was evaluated by using it to estimate VaR and ES for a daily data series taken from the S&P500 index and applying a backtesting procedure recommended by the Basel Committee on Banking Supervision. The results indicated a satisfactory performance.
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Bibliographic InfoPaper provided by arXiv.org in its series Papers with number 1112.2889.
Date of creation: Dec 2011
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Web page: http://arxiv.org/
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-12-19 (All new papers)
- NEP-BAN-2011-12-19 (Banking)
- NEP-CMP-2011-12-19 (Computational Economics)
- NEP-ECM-2011-12-19 (Econometrics)
- NEP-RMG-2011-12-19 (Risk Management)
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- Kim, Hyoung-Moon & Mallick, Bani K. & Holmes, C.C., 2005. "Analyzing Nonstationary Spatial Data Using Piecewise Gaussian Processes," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 653-668, June.
- Brahim-Belhouari, Sofiane & Bermak, Amine, 2004. "Gaussian process for nonstationary time series prediction," Computational Statistics & Data Analysis, Elsevier, vol. 47(4), pages 705-712, November.
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