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Filipovic, D., Mayerhofer, E. and Schneider, P. (2013) Density Approximations for Multivariate Affine Jump Diffusion Processes. Journal of Econometrics, 176, 93-111.
https://doi.org/10.1016/j.jeconom.2012.12.003

has been cited by the following article:

• TITLE: Derivatives Pricing via Machine Learning

  AUTHORS: Tingting Ye, Liangliang Zhang

  KEYWORDS: Machine Learning, Regression Analysis, Jump-Diffusion, Derivatives Pricing, Hilbert Space, Orthogonal Projection

  JOURNAL NAME: Journal of Mathematical Finance, Vol.9 No.3, August 27, 2019

  ABSTRACT: In this paper, we combine the theory of stochastic process and techniques of machine learning with the regression analysis, first proposed by [1] to solve for American option prices, and apply the new methodologies on financial derivatives pricing. Rigorous convergence proofs are provided for some of the methods we propose. Numerical examples show good applicability of the algorithms. More applications in finance are discussed in the Appendices.