Modelling commodity value at risk with Psi Sigma neural networks using open–high–low–close data

The motivation for this paper is to investigate the use of a promising class of neural network models, Psi Sigma (PSI), when applied to the task of forecasting the one-day ahead value at risk (VaR) of the oil Brent and gold bullion series using open–high–low–close data. In order to benchmark our results, we also consider VaR forecasts from two different neural network designs, the multilayer perceptron and the recurrent neural network, a genetic programming algorithm, an extreme value theory model along with some traditional techniques such as an ARMA-Glosten, Jagannathan, and Runkle (1,1) model and the RiskMetrics volatility. The forecasting performance of all models for computing the VaR of the Brent oil and the gold bullion is examined over the period September 2001–August 2010 using the last year and half of data for out-of-sample testing. The evaluation of our models is done by using a series of backtesting algorithms such as the Christoffersen tests, the violation ratio and our proposed loss function that considers not only the number of violations but also their magnitude. Our results show that the PSI outperforms all other models in forecasting the VaR of gold and oil at both the 5% and 1% confidence levels, providing an accurate number of independent violations with small magnitude

Forecasting and trading the EUR/USD exchange rate with Gene Expression and Psi Sigma Neural Networks

The motivation for this paper is to investigate the use of two promising classes of artificial intelligence models, the PsiSigmaNeuralNetwork (PSI) and the GeneExpression algorithm (GEP), when applied to the task of forecasting and trading the EUR/USDexchangerate. This is done by benchmarking their results with a Multi-Layer Perceptron (MLP), a Recurrent NeuralNetwork (RNN), a genetic programming algorithm (GP), an autoregressive moving average model (ARMA) plus a naïve strategy. We also examine if the introduction of a time-varying leverage strategy can improve the trading performance of our models

A hybrid genetic algorithm-support vector machine approach in the task of forecasting and trading

The motivation of this article is to introduce a novel hybrid Genetic algorithm-Support Vector Machines method when applied to the task of forecasting and trading the daily and weekly returns of the FTSE 100 and ASE 20 indices. This is done by benchmarking its results with a Higher-Order Neural Network, a Naïve Bayesian Classifier, an autoregressive moving average model, a moving average convergence/divergence model, plus a naïve and a buy and hold strategy. More specifically, the trading performance of all models is investigated in forecast and trading simulations on the FTSE 100 and ASE 20 time series over the period January 2001-May 2010, using the last 18 months for out-of-sample testing. As it turns out, the proposed hybrid model does remarkably well and outperforms its benchmarks in terms of correct directional change and trading performance. © 2013 Macmillan Publishers Ltd

Portfolio Effects and Valuation of Weather Derivatives

In a mean-variance framework, the indifference pricing approach is adopted to value weather derivatives, taking account of portfolio effects. Our analysis shows how the magnitude of portfolio effects is related to the correlation between weather indexes and other risky assets, the correlation between weather indexes, and the payoff structures of the existing weather derivatives in an investor's asset portfolio. We also conduct some preliminary empirical analysis. This study contributes to the weather derivative pricing literature by incorporating both the hedgeable and unhedgeable parts of weather risks in illustrating the portfolio effects on the indifference prices of weather derivatives. Copyright 2006 by the Eastern Finance Association.

Optimal Prediction with Conditionally Heteroskedastic Factor Analysed Hidden Markov Models

Latent factor models, EM algorithm, Conditional heteroskedasticity, HMM, Time series segmentation, Forecasting, 62H25, 62M05, 62M10, 62P20,