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A data-driven approach called CaNN (Calibration Neural Network) is proposed to calibrate financial asset price models using an Artificial Neural Network (ANN).
Horvath, B., Muguruza, A., and Tomas, M. (2019) · 1901
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A stochastic approximation method
Robbins, H. and Monro, S. (1951) · 1951
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Algorithms for minimization without derivatives
Brent, R. P. (1973) · 1973
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A theory of the term structure of interest rates
Cox, J. C., Ingersoll, J. E., and Ross, S. A. (1985) · 1985
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A closed-form solution for options with stochastic volatility with applications to bond and currency options
Heston, S. L. (1993) · 1993
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Jumps and stochastic volatility: Exchange rate processes implicit in Deutsche mark options
Bates, D. S. (1996) · 1996
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Neural networks for optimal approximation of smooth and analytic functions
Mhaskar, H. N. (1996) · 1996
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The inverse problem of option pricing
Bouchouev, I. and Isakov, V. (1997) · 1997
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Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces
Storn, R. and Price, K. (1997) · 1997
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Lower bounds for approximation by mlp neural networks
Maiorov, V. and Pinkus, A. (1999) · 1999
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Bayesian calibration of computer models
Kennedy, M. C. and O’Hagan, A. (2001) · 2001
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An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
Daubechies, I., Defrise, M., and De Mol, C. (2004) · 2004
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An inverse problem of determining the implied volatility in option pricing
Deng, Z.-C., Yu, J.-N., and Yang, L. (2008) · 2008
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Training of artificial neural networks using differential evolution algorithm
Slowik, A. and Bialko, M. (2008) · 2008
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A novel pricing method for European options based on Fourier-Cosine series expansions
Fang, F. and Oosterlee, C. W. (2009) · 2009
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Fitting the smile, smart parameters for SABR and Heston
Gauthier, P. and Rivaille, P.-Y. H. (2009) · 2009
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Calibration of interest rate and option models using differential evolution
Model calibration with neural networks
Hernandez, A. (2016) · 2016
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Liang, S. and Srikant, R. (2016) · 2016
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Full and fast calibration of the Heston stochastic volatility model
Cui, Y., del Baño Rollin, S., and Germano, G. (2017) · 2017
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Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review
Poggio, T., Mhaskar, H., Rosasco, L., Miranda, B., and Liao, Q. (2017) · 2017
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Error bounds for approximations with deep relu networks
Yarotsky, D. (2017) · 2017
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Vollrath, I. and Wendland, J. (2009) · 2009
Cited alongside, same era.
Asymptotic formulae for implied volatility in the Heston model
Forde, M., Jacquier, A., and Mijatović, A. (2010) · 2010
Cited alongside, same era.
Implied volatility surface: Construction methodologies and characteristics
Homescu, C. (2011) · 2011
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Calibrating option pricing models with heuristics
Gilli, M. and Schumann, E. (2012) · 2012
Cited alongside, same era.
Calibration risk: Illustrating the impact of calibration risk under the Heston model
Guillaume, F. and Schoutens, W. (2012) · 2012
Cited alongside, same era.
Deep Learning
Goodfellow, I., Bengio, Y., and Courville, A. (2016) · 2016
Cited alongside, same era.
Dimitroff, G., Röder, D., and Fries, C. P. (2018) · 2018
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Averaging weights leads to wider optima and better generalization
Izmailov, P., Podoprikhin, D., Garipov, T., Vetrov, D., and Wilson, A. G. (2018) · 2018
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Resnet with one-neuron hidden layers is a universal approximator
Lin, H. and Jegelka, S. (2018) · 2018
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Machine learning for quantitative finance: fast derivative pricing, hedging and fitting
Spiegeleer, J. D., Madan, D. B., Reyners, S., and Schoutens, W. (2018) · 2018
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Inverse problems in option pricing: a statistical approach using minimal entropy random mixtures
Cont, R. (Accessed on 17/03/2019) · 2019
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Pricing options and computing implied volatilities using neural networks
Liu, S., Oosterlee, C. W., and Bohte, S. M. (2019) · 2019
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