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Financial Risk Management and Portfolio Optimization Using Artificial Neural Networks and Extreme Value Theory
Company: University of Kaiserslautern
Year Of Publication: 2002
Month Of Publication: October
Pages: 141
Download Count: 5
View Count: 1438
Comment Num: 0
Language: English
Source: thesis
Who Can Read: Free
Date: 6-10-2011
Publisher: Administrator
Summary
To measure the accuracy of the estimation procedures, the consistency of the artificial neural network estimates of the conditional expectations will be analysed. Within such models, to estimate the VaR or the Expected Shortfall(ES5; See Delbaen [1998]), one has to combine ANN functions and EVT (see Embrechts [1997], Mc-Neil [1999-2000]). EVT will be used for estimating the tails and the quantiles of the unexpected returns, which is needed in the nal VaR estimation formula. For the assessment of the quantile of the unexpected returns, the use of some results based on Extreme Value Distributions(EVD) and Generalised Pareto Distributions (GPD) will provide the accurate estimates for the quantiles of heavy tailed stochastic processes (See Smith [1987], Embrechts [1997] or Alexander Mc Neil [1999]). The last section of the rst chapter deals with numerical simulations using real financial data in the aim to illustrate the accuracy of the VaR methodology via the computation of the daily Value-at-Risk.
Author(s)
Diagne, Mabouba Sign in to follow this author
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