Efficient Estimation of First Passage Time Density Function for Jump-Diffusion Processes
SIAM Journal on Scientific Computing
On learning strategies for evolutionary Monte Carlo
Statistics and Computing
Gelfand-Yaglom-Perez theorem for generalized relative entropy functionals
Information Sciences: an International Journal
Coupling control and human factors in mathematical models of complex systems
Engineering Applications of Artificial Intelligence
Parallel and interacting Markov chain Monte Carlo algorithm
Mathematics and Computers in Simulation
Evolutionary optimization of dynamics models in sequential Monte Carlo target tracking
IEEE Transactions on Evolutionary Computation
Applied Stochastic Models in Business and Industry
Hi-index | 0.00 |
Evolutionary computation techniques are closely connected with Monte Carlo simulations via statistical mechanics. Most practical realizations of such a connection are based on Markov chain Monte Carlo procedures and Markov chain approximation methodologies. However, such realizations face challenges when we have to deal with multivariate situations. In this contribution, we consider the development of evolutionary type Monte Carlo based algorithms for dealing with jump-diffusion stochastic processes. In particular, we focus on the first passage time problems for multivariate correlated jump-diffusion processes in the context of credit risk and the analysis of default correlations. The developed technique can be useful in option pricing as well as in other areas of complex systems analysis.