DeepOption: A novel option pricing framework based on deep learning with fused distilled data from multiple parametric methods

Black, 1973, The pricing of options and corporate liabilities, J. Polit. Econ., 81, 637, 10.1086/260062 Park, 2014, Parametric models and non-parametric machine learning models for predicting option prices: Empirical comparison study over KOSPI 200 Index options, Expert Syst. Appl., 41, 5227, 10.1016/j.eswa.2014.01.032 Heston, 1993, A closed-form solution for options with stochastic volatility with applications to bond and currency options, Rev. Financ. Stud., 6, 327, 10.1093/rfs/6.2.327 Merton, 1976, Option pricing when underlying stock returns are discontinuous, J. Financ. Econ., 125, 10.1016/0304-405X(76)90022-2 Carr, 2003, Stochastic volatility for Lévy processes, Math. Financ., 13, 345, 10.1111/1467-9965.00020 Madan, 1998, The variance gamma process and option pricing, Rev. Financ., 2, 79, 10.1023/A:1009703431535 Liang, 2009, Improving option price forecasts with neural networks and support vector regressions, Neurocomputing, 72, 3055, 10.1016/j.neucom.2009.03.015 Cox, 1979, Option pricing: a simplified approach, J. Financ. Econ., 7, 229, 10.1016/0304-405X(79)90015-1 Tian, 1999, A flexible binomial option pricing model, J. Futur. Mark., 19, 817, 10.1002/(SICI)1096-9934(199910)19:7<817::AID-FUT5>3.0.CO;2-D Boyle, 1977, Options: a Monte Carlo approach, J. Financ. Econ., 4, 323, 10.1016/0304-405X(77)90005-8 Niederreiter, 1978, Quasi-Monte Carlo methods and pseudo-random numbers, Bull. Am. Math. Soc., 84, 957, 10.1090/S0002-9904-1978-14532-7 Hammersley, 1956, A new Monte Carlo technique: antithetic variates, Math. Proc. Cambridge Philos. Soc., 52, 449, 10.1017/S0305004100031455 Rubinstein, 1985, Efficiency of multivariate control variates in monte carlo simulation, Oper. Res., 33, 661, 10.1287/opre.33.3.661 Siegmund, 1976, Importance sampling in the Monte Carlo study of sequential tests, Ann. Stat., 673, 10.1214/aos/1176343541 Schwartz, 1977, The valuation of warrants: implementing a new approach, J. Financ. Econ., 4, 79, 10.1016/0304-405X(77)90037-X Lecun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 Yang, 2017, Gated neural networks for option pricing: rationality by design, 2017 Gradojevic, 2009, Option pricing with modular neural networks, IEEE Trans. Neural Networks., 20, 626, 10.1109/TNN.2008.2011130 Hutchinson, 1994, A nonparametric approach to pricing and hedging derivative securities via learning networks, J. Finance., 49, 851, 10.1111/j.1540-6261.1994.tb00081.x Lajbcygier, 1997, Improved option pricing using artificial neural networks and bootstrap methods, Int. J. Neural Syst., 8, 457, 10.1142/S0129065797000446 Gençay, 2001, Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping, and bagging, IEEE Trans. Neural Networks., 12, 726, 10.1109/72.935086 Amilon, 2003, A neural network versus Black–Scholes: a comparison of pricing and hedging performances, J. Forecast., 22, 317, 10.1002/for.867 Das, 2017, A new hybrid parametric and machine learning model with homogeneity hint for European-style index option pricing, Neural Comput. Appl., 28, 4061, 10.1007/s00521-016-2303-y Sun, 2017, Revisiting unreasonable effectiveness of data in deep learning era, 843 E.P. Chan, Machine trading: deploying computer algorithms to conquer the markets, 2001. Baek, 2018, ModAugNet: a new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module, Expert Syst. Appl., 113, 457, 10.1016/j.eswa.2018.07.019 Jeong, 2019, Improving financial trading decisions using deep Q-learning: predicting the number of shares, action strategies, and transfer learning, Expert Syst. Appl., 117, 125, 10.1016/j.eswa.2018.09.036 Liu, 2019, Pricing options and computing implied volatilities using neural networks, , Risks, 7, 10.3390/risks7010016 Breen, 1991, The accelerated binomial option pricing model, J. Financ. Quant. Anal., 26, 153, 10.2307/2331262 Ian Goodfellow, 2016 Duda, 2012 Chollet, 2015, Keras documentation, Keras. Io. Tieleman, 2012 Mrázek, 2017, Calibration and simulation of Heston model, Open Math., 15, 679, 10.1515/math-2017-0058 Xu, 2006, Robust support vector machine training via convex outlier ablation, 2006 Alexander, 2012, Does model fit matter for hedging? Evidence from FTSE 100 options, J. Futur. Mark., 32, 609, 10.1002/fut.20537 Zhang, 2019, HOBA: a novel feature engineering methodology for credit card fraud detection with a deep learning architecture, Inf. Sci., 10.1016/j.ins.2019.05.023 González-Carrasco, 2019, Automatic detection of relationships between banking operations using machine learning, Inf. Sci., 485, 319, 10.1016/j.ins.2019.02.030 Rodrigues, 2019, Combining time-series and textual data for taxi demand prediction in event areas: A deep learning approach, Inf. Fusion., 49, 120, 10.1016/j.inffus.2018.07.007