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Neural networks Portfolio selection General optimization Portfolio optimization Heuristic optimization CAPM (Capital Asset Pricing Model) Neural optimization Va R (Value at Risk) Genetic algorithms Prospect Theory Simulated annealing Technical analysis Taboo search Data patterns Game theory Bond pricing Multi-agent simulations Option pricing Monte Carlo simulations Volatility estimations Time series analysis SWAP pricing Fuzzy logic Arbitrage trading Fractals and chaos Term structure [1] Feedforward and Recurrent Neural Networks and Genetic Programs for Stock Market and Time Series Forecasting, P. Mc Cluskey, 1993 [2] Clustering of the Self-Organizing Map , Juha Vesanto , 2000 [3] The Learning Vector Quantization Program Package, T. Moody, 1994 [6] Economic Forecasting: Challenges and Neural Network Solutions, J.

Kohonen, 1996 [4] A Neural Network Model for Gold Market, P. Moody, 1995 [7] Neural Networks in Economics: Background, Applications and New Developments, R.

This study proposes a procedure for using a generalized error distribution (GED) model in conjunction with historical simulation when computing Value-at-Risk (Va R).

We collected data from the New York S&P 500, the London Times FTSE 100 and the Frankfurt Index to evaluate the Va R approaches.

incorporating volatility updating into the historical simulation-59

Dorffner, 1996 [10] Stock Price Prediction Using Neural Networks, F. Riedmiller, 1994 [12] The Self-Organizing Map Program Package, T. Cheng , 1996 [16] Optimal Asset Allocation using Adaptive Dynamic Programming, R.

Using backtesting of historical daily return series of five...

[Show full abstract]We modify a two-step approach by Mc Neil and Frey (2000) for forecasting Value-at-Risk (Va R).

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