04294nam 22007935 450 991073371210332120231106123453.0981-19-2008-710.1007/978-981-19-2008-0(CKB)26748005300041(MiAaPQ)EBC7250384(Au-PeEL)EBL7250384(OCoLC)1379265692(DE-He213)978-981-19-2008-0(PPN)270616527(EXLCZ)992674800530004120230516d2023 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierComputational Finance with R /by Rituparna Sen, Sourish Das1st ed. 2023.Singapore :Springer Nature Singapore :Imprint: Springer,2023.1 online resource (352 pages)Indian Statistical Institute Series,2523-31229789811920073 Includes bibliographical references and index.Part 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. .This 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.Indian Statistical Institute Series,2523-3122StatisticsSocial sciencesMathematicsStochastic analysisMachine learningStatisticsComputer programsStatistics in Business, Management, Economics, Finance, InsuranceMathematics in Business, Economics and FinanceStochastic AnalysisStatisticsMachine LearningStatistical SoftwareEnginyeria financerathubR (Llenguatge de programació)thubLlibres electrònicsthubStatistics.Social sciencesMathematics.Stochastic analysis.Machine learning.StatisticsComputer programs.Statistics in Business, Management, Economics, Finance, Insurance.Mathematics in Business, Economics and Finance.Stochastic Analysis.Statistics.Machine Learning.Statistical Software.Enginyeria financeraR (Llenguatge de programació)332.028553Sen Rituparna1370247Das SourishMiAaPQMiAaPQMiAaPQBOOK9910733712103321Computational finance with R3397892UNINA