LEADER 03665nam 22007455 450 001 9910986132803321 005 20251030151323.0 010 $a9783031808746 010 $a3031808746 024 7 $a10.1007/978-3-031-80874-6 035 $a(MiAaPQ)EBC31947409 035 $a(Au-PeEL)EBL31947409 035 $a(CKB)37788182700041 035 $a(OCoLC)1511106017 035 $a(DE-He213)978-3-031-80874-6 035 $a(EXLCZ)9937788182700041 100 $a20250307d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aGaussian Process Models for Quantitative Finance /$fby Michael Ludkovski, Jimmy Risk 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (323 pages) 225 1 $aSpringerBriefs in Quantitative Finance,$x2192-7014 311 08$a9783031808739 311 08$a3031808738 327 $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. 330 $aThis book describes the diverse applications of Gaussian Process (GP) models in mathematical finance. Spurred by the transformative influence of machine learning frameworks, the text aims to integrate GP modeling into the fabric of quantitative finance. The first half of the book provides an entry point for graduate students, established researchers and quant practitioners to get acquainted with GP methodology. A systematic and rigorous introduction to both GP fundamentals and most relevant advanced techniques is given, such as kernel choice, shape-constrained GPs, and GP gradients. The second half surveys the broad spectrum of GP applications that demonstrate their versatility and relevance in quantitative finance, including parametric option pricing, GP surrogates for optimal stopping, and GPs for yield and forward curve modeling. The book includes online supplementary materials in the form of half a dozen computational Python and R notebooks that provide the reader direct illustrations of the covered material and are available via a public GitHub repository. 410 0$aSpringerBriefs in Quantitative Finance,$x2192-7014 606 $aSocial sciences$xMathematics 606 $aStochastic processes 606 $aMachine learning 606 $aMathematics in Business, Economics and Finance 606 $aStochastic Processes 606 $aMachine Learning 606 $aStochastic Systems and Control 606 $aCičncies socials$2thub 606 $aMatemātica$2thub 606 $aProcessos estocāstics$2thub 606 $aAprenentatge automātic$2thub 608 $aLlibres electrōnics$2thub 615 0$aSocial sciences$xMathematics. 615 0$aStochastic processes. 615 0$aMachine learning. 615 14$aMathematics in Business, Economics and Finance. 615 24$aStochastic Processes. 615 24$aMachine Learning. 615 24$aStochastic Systems and Control. 615 7$aCičncies socials 615 7$aMatemātica 615 7$aProcessos estocāstics 615 7$aAprenentatge automātic 676 $a519 700 $aLudkovski$b Michael$01790682 701 $aRisk$b Jimmy$01790683 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910986132803321 996 $aGaussian Process Models for Quantitative Finance$94327413 997 $aUNINA