Fetching the paper…
Reading the bibliography…
Techniques from deep learning play a more and more important role for the important task of calibration of financial models.
K. Levenberg. A Method for the Solution of Certain Non-Linear Problems in Least Squares. Quarterly of Applied Mathematics
1944
Earlier work this paper cites.
D. Marquardt. An Algorithm for Least-Squares Estimation of Nonlinear Parameters. SIAM Journal on Applied Mathematics
1963
Earlier work this paper cites.
K. Hornik, M. Stinchcombe, and H. White. Multilayer feedforward networks are universal approximators. Neural Networks
1989
Earlier work this paper cites.
K. Hornik. M. Stinchcombe and H. White. Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural Networks
1990
Earlier work this paper cites.
J. Hull and A. White. Pricing interest rate derivatives securities. The Review of Financial Studies
1990
Earlier work this paper cites.
S.L. Heston. A closed-form solution for options with stochastic volatility with applications to bond and currency options, The Review of Financial Studies
1993
Earlier work this paper cites.
J. M. Hutchinson, A. W. Lo and T. Poggio. A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks. The Journal of Finance
1994
Earlier work this paper cites.
M. Avellaneda, A. Carelli, F. Stella. Following the Bayes path to option pricing. Journal of Computational Intelligence in Finance
1999
Earlier work this paper cites.
J. Friedman, R. Tibshiran and T. Hastie. The Elements of Statistical Learning. Springer New York Inc
2001
Earlier work this paper cites.
P. Hagan, D. Kumar, A. Lesniewski, and D. Woodward. Managing smile risk. Wilmott Magazine
2002
Earlier work this paper cites.
E. Alòs, J. León and J. Vives. On the short-time behavior of the implied volatility for jump-diffusion models with stochastic volatility. Finance and Stochastics
2007
Earlier work this paper cites.
R. Cont. Model Calibration. Encyclopedia of Quantitative Finance
2010
Earlier work this paper cites.
B. Chen, C. W. Oosterlee and H. Van Der Weide. Efficient unbiased simulation scheme for the SABR stochastic volatility model, 2011
2011
Earlier work this paper cites.
M. Fukasawa. Asymptotic analysis for stochastic volatility: martingale expansion. Finance and Stochastics
2011
Earlier work this paper cites.
J. Gatheral. The volatility surface: a practitioner’s guide, Wiley
2011
Earlier work this paper cites.
L. Setayeshgar, and H. Wang. Large deviations for a feed-forward network, Advances in Applied Probability
2011
Earlier work this paper cites.
D. Foreman-Mackey, D. W. Hogg, D. Lang, J. Goodman. emcee: the MCMC hammer, Publications of the Astronomical Society of the Pacific , 125(925), 306, 2013
2013
Earlier work this paper cites.
A. Itkin. To sigmoid-based functional description of the volatility smile. Preprint
2014
Cited alongside, same era.
L. Bergomi. Stochastic Volatility Modeling. Chapman & Hall/CRC financial mathematical series. Chapman & Hall/CRC
2015
Cited alongside, same era.
A. Green. XVA: Credit, Funding and Capital Valuation Adjustments. Wiley
2015
Cited alongside, same era.
2015
Cited alongside, same era.
D.P. Kingman and J. Ba, Adam: A Method for Stochastic Optimization. Conference paper
2015
Cited alongside, same era.
G. Dimitroff, D. Röder and C. P. Fries. Volatility model calibration with convolutional neural networks. Preprint
2018
Later among the works it cites.
O. El Euch and M. Rosenbaum. Perfect hedging in rough Heston models, to appear in The Annals of Applied Probability
2018
Later among the works it cites.
R. Ferguson and A. D. Green. Deeply learning derivatives. Preprint arXiv:1809.02233 , 2018
2018
Later among the works it cites.
J. Gatheral, T. Jaisson and M. Rosenbaum. Volatility is rough. Quantitative Finance
2018
Later among the works it cites.
J. Han, A. Jentzen, E. Weinan. Overcoming the curse of dimensionality: Solving high-dimensional partial differential equations using deep learning. PNAS
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
C. Bayer, P. Friz and J. Gatheral. Pricing under rough volatility. Quantitative Finance
2016
Cited alongside, same era.
R. Eldan and O. Shamir. The power of depth for feedforward neural neworks. JMLR: Workshop and Conference Proceedings
2016
Cited alongside, same era.
D. Foreman-Mackey, corner.py: Scatterplot matrices in Python, The Journal of Open Source Software 24, http://dx.doi.org/10.5281/zenodo.45906 , 2016
2016
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
M. Bennedsen, A. Lunde and M.S. Pakkanen. Hybrid scheme for Brownian semistationary processes. Finance and Stochastics
2017
Cited alongside, same era.
R. Culkin and S. R. Das Machine Learning in Finance: The Case of Deep Learning for Option Pricing. Journal of Investment Management
2017
Cited alongside, same era.
B. Horvath, O. Reichmann. Dirichlet Forms and Finite Element Methods for the SABR Model. SIAM Journal on Financial Mathematics
2018
Later among the works it cites.
W. A. McGhee. An artificial neural network representation of the SABR stochastic volatility model. Preprint
2018
Later among the works it cites.
R. McCrickerd, M. Pakkanen, Turbocharging Monte Carlo pricing for the rough Bergomi model, Quantitative Finance
2018
Later among the works it cites.
2018
Later among the works it cites.
J. Sirignano and K. Spiliopoulos. DGM: A deep learning algorithm for solving partial differential equations. Journal of Computational Physics
2018
Later among the works it cites.
U. Shaham, A. Cloninger, and R. R. Coifman. Provable approximation properties for deep neural networks. Appl. Comput. Harmon. Anal
2018
Later among the works it cites.
H. Stone. Calibrating rough volatility models: a convolutional neural network approach. Preprint
2018
Later among the works it cites.
A. Antonov, M. Konikov, M. Spector. Modern SABR Analytics: Formulas and Insights for Quants, Former Physicists and Mathematicians. SpringerBriefs in Quantitative Finance
2019
Closest in time.
2019
Closest in time.
B. Horvath, A. Muguruza and T. Mehdi. Deep learning volatility. Available at SSRN 3322085, 2019
2019
Closest in time.