Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic Algorithms and Machine Learning
Machine Learning
Sequential Monte Carlo Methods to Train Neural Network Models
Neural Computation
The crowding approach to niching in genetic algorithms
Evolutionary Computation
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 01
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Computers & Mathematics with Applications
Hybrid metaheuristic particle filters for stochastic volatility estimation
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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Stochastic volatility estimation is an important task for correctly pricing derivatives in mathematical finance. Such derivatives are used by varying types of market participant as either hedging tools or for bespoke market exposure. We evaluate our adaptive path particle filter, a recombinatory evolutionary algorithm based on the generation gap concept from evolutionary computation, for stochastic volatility estimation of three real financial asset time series. We calibrate the Heston stochastic volatility model employing a Markov-chain Monte Carlo, enabling us to understand the latent stochastic volatility process and parameters. In our experiments we find the adaptive path particle filter to be superior to the standard sequential importance resampling particle filter, the Markov-chain Monte Carlo particle filter and the particle learning particle filter. We present a detailed analysis of the results and suggest directions for future research.