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Gradient boosting for quantitative finance
Davis, Jesse; Devos, Laurens; Reyners, Sofie; Schoutens, Wim - In: The journal of computational finance 24 (2021) 4, pp. 1-40
Persistent link: https://www.econbiz.de/10012544161
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Symbolic regression-based adaptive generation of implied volatility
Yen, Joseph; Qi, Yuan Yuan; Wong, Seng Fat; Zhou, Jiantao - In: International journal of financial engineering 9 (2022) 3, pp. 2250018-1-2250018-27
Persistent link: https://www.econbiz.de/10013367616
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American option pricing with machine learning : an extension of the Longstaff-Schwartz method
Lin, Jingying; Almeida, Caio - In: Revista Brasileira de Finanças : RBFin 19 (2021) 3, pp. 85-109
Persistent link: https://www.econbiz.de/10012804834
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Least square regression methods for Bermudan derivatives and systems of functions
Kusuoka, Shigeo; Morimoto, Yusuke - In: Advances in mathematical economics 19 (2015), pp. 57-89
Persistent link: https://www.econbiz.de/10010519532
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Non-parametric pricing of interest rates options
Mauad, Roberto Baltieri; Laurini, Márcio Poletti - In: Brazilian review of econometrics : BRE ; the review of … 32 (2012) 2, pp. 201-240
Persistent link: https://www.econbiz.de/10011538557
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Algorithmic counterparty credit exposure for multi-asset Bermudan options
Shen, Yanbin; Anderluh, J. H. M.; Weide, Hans van der - In: International journal of theoretical and applied finance 18 (2015) 1, pp. 1-35
Persistent link: https://www.econbiz.de/10011403163
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Deep learning-based least squares forward-backward stochastic differential equation solver for high-dimensional derivative pricing
Liang, Jian; Xu, Zhe; Li, Peter - In: Quantitative finance 21 (2021) 8, pp. 1309-1323
Persistent link: https://www.econbiz.de/10012608649
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Gaussian process regression for derivative portfolio modeling and application to credit valuation adjustment computations
Crépey, Stéphane; Dixon, Matthew F. - In: The journal of computational finance 24 (2020) 1, pp. 47-81
Persistent link: https://www.econbiz.de/10012421957
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Applied Linear Regression for Business Analytics with R : A Practical Guide to Data Science with Case Studies
McGibney, Daniel P. - 2023
1. Introduction -- 2. Basic Statistics and Functions using R -- 3. Regression Fundamentals -- 4. Simple Linear Regression -- 5. Multiple Regression -- 6. Estimation Intervals and Analysis of Variance -- 7. Predictor Variable Transformations -- 8. Model Diagnostics -- 9. Variable Selection.
Persistent link: https://www.econbiz.de/10014286486
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Modeling organizational performance with machine learning
Pap, Jozsef; Makó, Csaba; Illessy, Miklos; Kis, Norbert; … - In: Journal of open innovation : technology, market, and … 8 (2022) 4, pp. 1-19
Identifying the performance factors of organizations is of utmost importance for labor studies for both empirical and theoretical research. The present study investigates the essential intra- and extra-organizational factors in determining the performance of firms using the European Company...
Persistent link: https://www.econbiz.de/10013372863