Delta Force: Option Pricing with Differential Machine Learning

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Standard

Delta Force : Option Pricing with Differential Machine Learning. / Frandsen, Magnus Grønnegaard; Pedersen, Tobias Cramer; Poulsen, Rolf.

I: Digital Finance, Bind 4, 2022, s. 1-15.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Frandsen, MG, Pedersen, TC & Poulsen, R 2022, 'Delta Force: Option Pricing with Differential Machine Learning', Digital Finance, bind 4, s. 1-15. https://doi.org/10.1007/s42521-021-00041-7

APA

Frandsen, M. G., Pedersen, T. C., & Poulsen, R. (2022). Delta Force: Option Pricing with Differential Machine Learning. Digital Finance, 4, 1-15. https://doi.org/10.1007/s42521-021-00041-7

Vancouver

Frandsen MG, Pedersen TC, Poulsen R. Delta Force: Option Pricing with Differential Machine Learning. Digital Finance. 2022;4:1-15. https://doi.org/10.1007/s42521-021-00041-7

Author

Frandsen, Magnus Grønnegaard ; Pedersen, Tobias Cramer ; Poulsen, Rolf. / Delta Force : Option Pricing with Differential Machine Learning. I: Digital Finance. 2022 ; Bind 4. s. 1-15.

Bibtex

@article{b9c030e8694a4dd8bafc4cb412e09bd8,
title = "Delta Force: Option Pricing with Differential Machine Learning",
abstract = "We show how and why to use a financially meaningful differential regularization method when pricing options by Monte Carlo simulation, be that in polynomial regression or neural network context.",
author = "Frandsen, {Magnus Gr{\o}nnegaard} and Pedersen, {Tobias Cramer} and Rolf Poulsen",
year = "2022",
doi = "10.1007/s42521-021-00041-7",
language = "English",
volume = "4",
pages = "1--15",
journal = "Digital Finance",
issn = "2524-6186",
publisher = "Springer",

}

RIS

TY - JOUR

T1 - Delta Force

T2 - Option Pricing with Differential Machine Learning

AU - Frandsen, Magnus Grønnegaard

AU - Pedersen, Tobias Cramer

AU - Poulsen, Rolf

PY - 2022

Y1 - 2022

N2 - We show how and why to use a financially meaningful differential regularization method when pricing options by Monte Carlo simulation, be that in polynomial regression or neural network context.

AB - We show how and why to use a financially meaningful differential regularization method when pricing options by Monte Carlo simulation, be that in polynomial regression or neural network context.

U2 - 10.1007/s42521-021-00041-7

DO - 10.1007/s42521-021-00041-7

M3 - Journal article

VL - 4

SP - 1

EP - 15

JO - Digital Finance

JF - Digital Finance

SN - 2524-6186

ER -

ID: 274850136