Computational finance with R / Rituparna Sen, Sourish Das.
Sen, Rituparna, author.; Das, Sourish, author.
2023
HG176.7
Available Online

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Title Computational finance with R / Rituparna Sen, Sourish Das.
Author Sen, Rituparna, author.
ISBN 9789811920080 (electronic bk.)
9811920087 (electronic bk.)
9811920079
9789811920073
DOI https://doi.org/10.1007/978-981-19-2008-0
Imprint Singapore : Springer, 2023.
Language English
Description 1 online resource (xiii, 353 pages) : illustrations (black and white, and colour).
Other Standard Identifiers 10.1007/978-981-19-2008-0 doi
Call Number HG176.7
Dewey Decimal Classification 658.15
Summary 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.
Bibliography, etc. Note Includes bibliographical references (pages 349-351) and index.
Access Note Access limited to authorized users.
Source of Description Description based on print version record.
Added Author Das, Sourish, author.
Series Indian Statistical Institute series. 2523-3122
Available in Other Form Print version: 9789811920073
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
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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. .

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