國立高雄大學圖資館 |

登入

回首頁

Gaussian process models for quantitative finance

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Gaussian process models for quantitative financeby Michael Ludkovski, Jimmy Risk.
作者:	Ludkovski, Michael.
其他作者:	Risk, Jimmy.
出版者:	Cham :Springer Nature Switzerland :2025.
面頁冊數:	xii, 138 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	FinanceMathematical models.
電子資源:	https://doi.org/10.1007/978-3-031-80874-6
ISBN:	9783031808746$q(electronic bk.)

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$q(electronic bk.)

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

183782
Finance
--Mathematical models.

LC Class. No.: HG106

Dewey Class. No.: 332.0151

Gaussian process models for quantitative finance
LDR:02427nmm a2200361 a 4500 001 680451
003 DE-He213
005 20250307115223.0
006 m d
007 cr nn 008maaau
008 251110s2025 sz s 0 eng d
020 $a 9783031808746$q(electronic bk.)
020 $a 9783031808739$q(paper)
024 7 $a 10.1007/978-3-031-80874-6 $2 doi
035 $a 978-3-031-80874-6
040 $a GP $c GP
041 0 $a eng
050 4 $a HG106
072 7 $a PBW $2 bicssc
072 7 $a K $2 bicssc
072 7 $a MAT003000 $2 bisacsh
072 7 $a PBW $2 thema
072 7 $a K $2 thema
082 0 4 $a 332.0151 $2 23
090 $a HG106 $b .L944 2025
100 1 $a Ludkovski, Michael. $3 237502
245 1 0 $a Gaussian process models for quantitative finance $h [electronic resource] / $c by Michael Ludkovski, Jimmy Risk.
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 183782
650 0 $a Gaussian processes. $3 211434
650 1 4 $a Mathematics in Business, Economics and Finance. $3 913174
650 2 4 $a Stochastic Processes. $3 743764
650 2 4 $a Machine Learning. $3 833608
650 2 4 $a Stochastic Systems and Control. $3 922350
700 1 $a Risk, Jimmy. $3 993184
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a SpringerBriefs in quantitative finance. $3 683267
856 4 0 $u https://doi.org/10.1007/978-3-031-80874-6
950 $a Mathematics and Statistics (SpringerNature-11649)