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Gaussian process models for quantita...

Ludkovski, Michael.

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Gaussian process models for quantitative finance

Record Type:	Electronic resources : Monograph/item
Title/Author:	Gaussian process models for quantitative finance/ by Michael Ludkovski, Jimmy Risk.
Author:	Ludkovski, Michael.
other author:	Risk, Jimmy.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xii, 138 p. :ill. (chiefly color), digital ;24 cm.
[NT 15003449]:	- 1. Gaussian Process Preliminaries -- 2. Covariance Kernels -- 3. Advanced GP Modeling Topics -- 4. Option Pricing and Sensitivities -- 5. Optimal Stopping -- 6. Non-Parametric Modeling of Financial Structures -- 7. Stochastic Control.
Contained By:	Springer Nature eBook
Subject:	Finance - Mathematical models. -
Online resource:	https://doi.org/10.1007/978-3-031-80874-6
ISBN:	9783031808746

Gaussian process models for quantitative finance
Ludkovski, Michael.

Gaussian process models for quantitative finance[electronic resource] /by Michael Ludkovski, Jimmy Risk. - Cham :Springer Nature Switzerland :2025. - xii, 138 p. :ill. (chiefly color), digital ;24 cm. - SpringerBriefs in quantitative finance,2192-7014. - SpringerBriefs in quantitative finance..

- 1. Gaussian Process Preliminaries -- 2. Covariance Kernels -- 3. Advanced GP Modeling Topics -- 4. Option Pricing and Sensitivities -- 5. Optimal Stopping -- 6. Non-Parametric Modeling of Financial Structures -- 7. Stochastic Control.

This book describes the diverse applications of Gaussian Process (GP) models in mathematical finance. Spurred by the transformative influence of machine learning frameworks, the text aims to integrate GP modeling into the fabric of quantitative finance. The first half of the book provides an entry point for graduate students, established researchers and quant practitioners to get acquainted with GP methodology. A systematic and rigorous introduction to both GP fundamentals and most relevant advanced techniques is given, such as kernel choice, shape-constrained GPs, and GP gradients. The second half surveys the broad spectrum of GP applications that demonstrate their versatility and relevance in quantitative finance, including parametric option pricing, GP surrogates for optimal stopping, and GPs for yield and forward curve modeling. The book includes online supplementary materials in the form of half a dozen computational Python and R notebooks that provide the reader direct illustrations of the covered material and are available via a public GitHub repository.

ISBN: 9783031808746

Standard No.: 10.1007/978-3-031-80874-6doiSubjects--Topical Terms:

578740
Finance
--Mathematical models.

LC Class. No.: HG106

Dewey Class. No.: 332.0151

Gaussian process models for quantitative finance
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260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a xii, 138 p. : $b ill. (chiefly color), digital ; $c 24 cm.
490 1 $a SpringerBriefs in quantitative finance, $x 2192-7014
505 0 $a - 1. Gaussian Process Preliminaries -- 2. Covariance Kernels -- 3. Advanced GP Modeling Topics -- 4. Option Pricing and Sensitivities -- 5. Optimal Stopping -- 6. Non-Parametric Modeling of Financial Structures -- 7. Stochastic Control.
520 $a This book describes the diverse applications of Gaussian Process (GP) models in mathematical finance. Spurred by the transformative influence of machine learning frameworks, the text aims to integrate GP modeling into the fabric of quantitative finance. The first half of the book provides an entry point for graduate students, established researchers and quant practitioners to get acquainted with GP methodology. A systematic and rigorous introduction to both GP fundamentals and most relevant advanced techniques is given, such as kernel choice, shape-constrained GPs, and GP gradients. The second half surveys the broad spectrum of GP applications that demonstrate their versatility and relevance in quantitative finance, including parametric option pricing, GP surrogates for optimal stopping, and GPs for yield and forward curve modeling. The book includes online supplementary materials in the form of half a dozen computational Python and R notebooks that provide the reader direct illustrations of the covered material and are available via a public GitHub repository.
650 0 $a Finance $x Mathematical models. $3 578740
650 0 $a Gaussian processes. $3 672951
650 1 4 $a Mathematics in Business, Economics and Finance. $3 3538573
650 2 4 $a Stochastic Processes. $3 906873
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Stochastic Systems and Control. $3 3596108
700 1 $a Risk, Jimmy. $3 3782709
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a SpringerBriefs in quantitative finance. $3 2072008
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