R Programming and Its Applications in Financial Mathematics

This book provides various calculation methods for financial mathematics utilizing R programming, and includes basic finance theories and statistical analysis. In addition to the ample applications of R programming provided, the book delivers simple descriptions to assist readers in the immediate application of the methods to financial data.

This book provides an introduction to R programming and a summary of financial mathematics.

It is not always easy for graduate students to grasp an overview of the theory of finance in an abstract form. For newcomers to the finance industry, it is not always obvious how to apply the abstract theory to the real financial data they encounter. Introducing finance theory alongside numerical applications makes it easier to grasp the subject.

Popular programming languages like C++, which are used in many financial applications are meant for general-purpose requirements. They are good for implementing large-scale distributed systems for simultaneously valuing many financial contracts, but they are not as suitable for small-scale ad-hoc analysis or exploration of financial data. The R programming language overcomes this problem. R can be used for numerical applications including statistical analysis, time series analysis, numerical methods for pricing financial contracts, etc.

This book provides an overview of financial mathematics with numerous examples numerically illustrated using the R programming language.

Autorentext

After finishing a Ph.D course at Kyoto University, Dr. Daisuke Yoshikawa worked for Mizuho-DL financial technology and Bank of Japan. Meanwhile, Dr. Yoshikawa published a few refereed journal papers on finance. Currently, Dr. Yoshikawa is working for Hokkai-Gakuen University as a lecturer.

Inhalt

Preface. Introduction to R programming. SECTION I: STATISTICS IN FINANCE. Statistical Analysis with R. Time Series Analysis with R. SECTION II: BASIC THEORY OF FINANCE. Modern Portfolio Theory and CAPM. Interest Rate Swap and Discount Factor. Discrete Time Model: Tree Model. Continuous time model and Black-Scholes Formula. SECTION III: NUMERICAL METHODS IN FINANCE. Monte Carlo Simulation. Derivative Pricing with Partial Differential Equations. SECTION IV: APPENDIX. Optimization with R. Noise reduction via Kalman Filter. The Other References on R. References. Index.