(Series editor: TAYLOR J.G.)

Value at Risk (V.a.R.) is used to measure the possible losses of a stock, of a derivative, of a portfolio and so on. Different approaches were proposed to determine 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. Subsequently, the Group Method of Data Handling 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.

A fuzzy-G.M.D.H. approach to V.a.R.

CORAZZA, Marco
;
GIOVE, Silvio
2002-01-01

Abstract

Value at Risk (V.a.R.) is used to measure the possible losses of a stock, of a derivative, of a portfolio and so on. Different approaches were proposed to determine 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. Subsequently, the Group Method of Data Handling 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.
2002
Neural Nets [Series: Perspectives in Neural Computing]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/24540
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