Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
The Ant Colony Metaphor for Searching Continuous Design Spaces
Selected Papers from AISB Workshop on Evolutionary Computing
Short-Term Variations and Long-Term Dynamics in Commodity Prices
Management Science
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Considering the stochastic exchange rate, a four-factor futures model with the underling asset, convenience yield, instantaneous risk-free interest rate and exchange rate, is established. These processes follow jump-diffusion processes (Weiner process and Poisson process). The corresponding partial differential equation (PDE) with terminal boundary condition of the model is drawn. The general solution with parameters of the above PDE is derived. The parameters are estimated by using the weight least squares approach with historical data for special cases. For the objective of risk assessment, downside risk has impacted on the practitioner's view of risk apparently. Variance is substituted by semi-variance. Moreover, one period portfolio selection is extended to multi-period. A class of multi-period semi-variance model is formulated. A hybrid genetic algorithm, which makes use of the position displacement strategy of the particle swarm optimiser as a mutation operation, is applied to solve the multi-period semi-variance model. Finally, in order to demonstrate the effectiveness of the theoretical models and numerical methods, fuel futures in the Shanghai exchange market is selected to be an example.