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Computational finance with R

Sen, Rituparna.

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Computational finance with R

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Computational finance with R/ by Rituparna Sen, Sourish Das.
作者:	Sen, Rituparna.
其他作者:	Das, Sourish.
出版者:	Singapore :Springer Nature Singapore : : 2023.,
面頁冊數:	1 online resource (xiii, 353 p.) :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Financial engineering. -
電子資源:	https://doi.org/10.1007/978-981-19-2008-0
ISBN:	9789811920080

Computational finance with R
Sen, Rituparna.

Computational finance with R[electronic resource] /by Rituparna Sen, Sourish Das. - Singapore :Springer Nature Singapore :2023. - 1 online resource (xiii, 353 p.) :ill., digital ;24 cm. - Indian statistical institute series,2523-3122. - Indian statistical institute series..

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.

ISBN: 9789811920080

Standard No.: 10.1007/978-981-19-2008-0doiSubjects--Topical Terms:

550926
Financial engineering.

LC Class. No.: HG176.7

Dewey Class. No.: 658.15

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520 $a 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.
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