40,169 research outputs found
Evolutionary estimation of a Coupled Markov Chain credit risk model
There exists a range of different models for estimating and simulating credit
risk transitions to optimally manage credit risk portfolios and products. In
this chapter we present a Coupled Markov Chain approach to model rating
transitions and thereby default probabilities of companies. As the likelihood
of the model turns out to be a non-convex function of the parameters to be
estimated, we apply heuristics to find the ML estimators. To this extent, we
outline the model and its likelihood function, and present both a Particle
Swarm Optimization algorithm, as well as an Evolutionary Optimization algorithm
to maximize the likelihood function. Numerical results are shown which suggest
a further application of evolutionary optimization techniques for credit risk
management
Evolutionary multi-stage financial scenario tree generation
Multi-stage financial decision optimization under uncertainty depends on a
careful numerical approximation of the underlying stochastic process, which
describes the future returns of the selected assets or asset categories.
Various approaches towards an optimal generation of discrete-time,
discrete-state approximations (represented as scenario trees) have been
suggested in the literature. In this paper, a new evolutionary algorithm to
create scenario trees for multi-stage financial optimization models will be
presented. Numerical results and implementation details conclude the paper
What is Computational Intelligence and where is it going?
What is Computational Intelligence (CI) and what are its relations with Artificial Intelligence (AI)? A brief survey of the scope of CI journals and books with ``computational intelligence'' in their title shows that at present it is an umbrella for three core technologies (neural, fuzzy and evolutionary), their applications, and selected fashionable pattern recognition methods. At present CI has no comprehensive foundations and is more a bag of tricks than a solid branch of science. The change of focus from methods to challenging problems is advocated, with CI defined as a part of computer and engineering sciences devoted to solution of non-algoritmizable problems. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions. Grand challenges on both sides of this spectrum are addressed
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