LEADER 00800nam0-22002891i-450- 001 990007499590403321 005 20061004122805.0 035 $a000749959 035 $aFED01000749959 035 $a(Aleph)000749959FED01 035 $a000749959 100 $a20030814d1948----km-y0itay50------ba 101 0 $afre 102 $aFR 200 1 $a<>grands explorateurs$fpar Marcel Griaule 210 $aParis$cPresses Universitaires de France$d1948 215 $a128 p.$d21 cm 225 1 $aQue sais-je? 610 0 $aViaggi e viaggiatori 700 1$aGriaule,$bMarcel$f<1898-1956>$0129558 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990007499590403321 952 $aC-03-111$bIst.4178$fILFGE 959 $aILFGE 996 $aGRANDS EXPLORATEURS$9449589 997 $aUNINA LEADER 04071nam 22007335 450 001 9910733712103321 005 20251113182104.0 010 $a981-19-2008-7 024 7 $a10.1007/978-981-19-2008-0 035 $a(CKB)26748005300041 035 $a(MiAaPQ)EBC7250384 035 $a(Au-PeEL)EBL7250384 035 $a(OCoLC)1379265692 035 $a(DE-He213)978-981-19-2008-0 035 $a(PPN)270616527 035 $a(EXLCZ)9926748005300041 100 $a20230516d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputational Finance with R /$fby Rituparna Sen, Sourish Das 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (352 pages) 225 1 $aIndian Statistical Institute Series,$x2523-3122 311 08$a9789811920073 320 $aIncludes bibliographical references and index. 327 $aPart I. Numerical Methods -- 1. Preliminaries -- 2. Solving a System of Linear Equations -- 3. Solving Non-Linear Equations -- 4. Numerical Integration -- 5. Numerical Differentiation -- 6. Numerical Methods for PDE -- 7. Optimization -- Part II. Simulation Methods -- 8. Monte-Carlo Methods -- 9. Lattice Models -- 10. Simulating Brownian Motion -- 11. Variance Reduction -- 12. Bayesian Computation with Stan -- 13. Resampling -- Part III. Statistical Methods -- 14. Descriptive Methods -- 15. Inferential Statistics -- 16. Statistical Risk Analysis -- 17. Multivariate Analysis -- 18. Univariate Time Series -- 19. Multivariate Time Series -- 20. High Frequency Data -- 21. Supervised Learning -- 22. Unsupervised Learning -- Appendix -- A. Basics of Mathematical Finance -- B. Introduction to R -- C. Extreme Value Theory in Finance -- Bibliography. . 330 $aThis book prepares students to execute the quantitative and computational needs of the finance industry. The quantitative methods are explained in detail with examples from real financial problems like option pricing, risk management, portfolio selection, etc. Codes are provided in R programming language to execute the methods. Tables and figures, often with real data, illustrate the codes. References to related work are intended to aid the reader to pursue areas of specific interest in further detail. The comprehensive background with economic, statistical, mathematical, and computational theory strengthens the understanding. The coverage is broad, and linkages between different sections are explained. The primary audience is graduate students, while it should also be accessible to advanced undergraduates. Practitioners working in the finance industry will also benefit. 410 0$aIndian Statistical Institute Series,$x2523-3122 606 $aStatistics 606 $aSocial sciences$xMathematics 606 $aStochastic analysis 606 $aMachine learning 606 $aStatistics$xComputer programs 606 $aStatistics in Business, Management, Economics, Finance, Insurance 606 $aMathematics in Business, Economics and Finance 606 $aStochastic Analysis 606 $aStatistics 606 $aMachine Learning 606 $aStatistical Software 615 0$aStatistics. 615 0$aSocial sciences$xMathematics. 615 0$aStochastic analysis. 615 0$aMachine learning. 615 0$aStatistics$xComputer programs. 615 14$aStatistics in Business, Management, Economics, Finance, Insurance. 615 24$aMathematics in Business, Economics and Finance. 615 24$aStochastic Analysis. 615 24$aStatistics. 615 24$aMachine Learning. 615 24$aStatistical Software. 676 $a332.028553 700 $aSen$b Rituparna$01370247 702 $aDas$b Sourish 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910733712103321 996 $aComputational finance with R$93397892 997 $aUNINA