Similar Search Results

A simultaneous perturbation weak derivative estimator for stochastic neural networks

Flynn, Thomas; Vázquez-Abad, Felisa - In: Computational Management Science : CMS 16 (2019) 4, pp. 715-738

Persistent link: https://www.econbiz.de/10012126685

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

Conditional nonparametric variable screening by neural factor regression

Fan, Jianqing; Wang, Weining; Zhao, Yue - 2024

more generally a set of variables. Our test statistics are based on the estimated partial derivative of the regression … derivative estimator is the convolution of a deep neural network regression estimator and a smoothing kernel. We demonstrate that … necessary to smooth the partial derivative of the neural network estimator to recover the desired convergence rate for the …

Persistent link: https://www.econbiz.de/10015053806

Efficient estimation of average derivatives in NPIV models : Simulation comparisons of neural network estimators

Chen, Jiafeng; Chen, Xiaohong; Tamer, Elie T. - In: Journal of econometrics 235 (2023) 2, pp. 1848-1875

Persistent link: https://www.econbiz.de/10014471433

A comparison of neural networks and Bayesian MCMC for the Heston model estimation (forget statistics – machine learning is sufficient!)

Witzany, Jiří; Fičura, Milan - 2023

Persistent link: https://www.econbiz.de/10014338462

Forecast accuracy of the linear and nonlinear autoregressive models in macroeconomic modeling

Taiebnia, Ali; Mohammadi, Shapour - In: Journal of forecasting 42 (2023) 8, pp. 2045-2062

Persistent link: https://www.econbiz.de/10014432847

Sustainable investing and the cross-section of returns and maximum drawdown

Goldberg, Lisa; Mouti, Saad - In: The Journal of finance and data science : JFDS 8 (2022), pp. 353-387

We use supervised learning to identify factors that predict the cross-section of returns and maximum drawdown for stocks in the US equity market. Our data run from January 1970 to December 2019 and our analysis includes ordinary least squares, penalized linear regressions, tree-based models, and...

Persistent link: https://www.econbiz.de/10014433739

An adversarial approach to structural estimation

Kaji, Tetsuya; Manresa, Elena; Pouliot, Guillaume - In: Econometrica : journal of the Econometric Society, an … 91 (2023) 6, pp. 2041-2063

Persistent link: https://www.econbiz.de/10014438258

Predicting stock return and volatility with machine learning and econometric models : a comparative case study of the Baltic stock market

Nõu, Anders; Lapitskaya, Darya; Eratalay, M. Hakan; … - In: International journal of computational economics and … 13 (2023) 4, pp. 446-489

Persistent link: https://www.econbiz.de/10014439728

Estimating parameters of structural models using neural networks

Wei, Yanhao; Jiang, Zhenling - In: Marketing science 44 (2025) 1, pp. 102-128

Persistent link: https://www.econbiz.de/10015197209