Abstract
Value at Risk (V.a.R.) is used to measure the possible losses of a stock, a derivative, a portfolio, and so on. Different approaches were proposed to obtain such a value, based on the past history, or on stochastic simulation, or on estimation of the theoretical distributions. We propose a method that empirically reconstructs the conditional distribution of the analyzed financial returns, using two soft-computing techniques based, respectively, on fuzzy estimation and on a polynomial neural network. At first, a non-parametric density estimation is proposed, using a fuzzy similarity measure between k-patterns, that are sequence of k consecutive values sampled from the considered time series, extending the Nadaraya-Watson approach [7]. Subsequently, the Group Method of Data Handling (on following: G.M.D.H.) neural network is used to compute a polynomial approximation of the unknown relationship between the data. The two-phases algorithm was finally applied to a real financial time series, and the results are used to create a V.a.R. predictor.
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© 2002 Springer-Verlag London Limited
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Corazza, M., Giove, S. (2002). A Fuzzy-G.M.D.H. Approach to V.a.R.. In: Tagliaferri, R., Marinaro, M. (eds) Neural Nets WIRN Vietri-01. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0219-9_24
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DOI: https://doi.org/10.1007/978-1-4471-0219-9_24
Publisher Name: Springer, London
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