124,630 research outputs found
A neural network-based framework for financial model calibration
A data-driven approach called CaNN (Calibration Neural Network) is proposed
to calibrate financial asset price models using an Artificial Neural Network
(ANN). Determining optimal values of the model parameters is formulated as
training hidden neurons within a machine learning framework, based on available
financial option prices. The framework consists of two parts: a forward pass in
which we train the weights of the ANN off-line, valuing options under many
different asset model parameter settings; and a backward pass, in which we
evaluate the trained ANN-solver on-line, aiming to find the weights of the
neurons in the input layer. The rapid on-line learning of implied volatility by
ANNs, in combination with the use of an adapted parallel global optimization
method, tackles the computation bottleneck and provides a fast and reliable
technique for calibrating model parameters while avoiding, as much as possible,
getting stuck in local minima. Numerical experiments confirm that this
machine-learning framework can be employed to calibrate parameters of
high-dimensional stochastic volatility models efficiently and accurately.Comment: 34 pages, 9 figures, 11 table
Self-Adaptive Surrogate-Assisted Covariance Matrix Adaptation Evolution Strategy
This paper presents a novel mechanism to adapt surrogate-assisted
population-based algorithms. This mechanism is applied to ACM-ES, a recently
proposed surrogate-assisted variant of CMA-ES. The resulting algorithm,
saACM-ES, adjusts online the lifelength of the current surrogate model (the
number of CMA-ES generations before learning a new surrogate) and the surrogate
hyper-parameters. Both heuristics significantly improve the quality of the
surrogate model, yielding a significant speed-up of saACM-ES compared to the
ACM-ES and CMA-ES baselines. The empirical validation of saACM-ES on the
BBOB-2012 noiseless testbed demonstrates the efficiency and the scalability
w.r.t the problem dimension and the population size of the proposed approach,
that reaches new best results on some of the benchmark problems.Comment: Genetic and Evolutionary Computation Conference (GECCO 2012) (2012
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