Gaussian process models for quantitative finance

By:

Ludkovski, Michael

Contributor(s):

Risk, Jimmy

Material type: Text

TextSeries: SpringerBriefs in Quantitative FinancePublication details: Springer Switzerland 2025Description: xii, 138 pISBN:

9783031808739

Subject(s):

DDC classification:

332.015195 LUD

Summary: 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. (https://link.springer.com/book/10.1007/978-3-031-80874-6)

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Finance & Accounting	332.015195 LUD (Browse shelf(Opens below))	1	Available		008735

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Previous	332.015195 AHL Statistical quantitative methods in finance: from theory to quantitative portfolio management	332.015195 LIM Financial valuation and econometrics	332.015195 LIN Financial econometrics:	332.015195 LUD Gaussian process models for quantitative finance	332.015195 MAR Quantitative finance	332.015195 MIL Mathematics and statistics for financial risk management	332.015195 REI Foundations of quantitative finance book IV: distribution functions and expectations	Next

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.

(https://link.springer.com/book/10.1007/978-3-031-80874-6)

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