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.This book covers all aspects of computation, namely numerical, simulation, and statistical, in a single volume. Numerical procedures, their advantages, applications in finance and execution in R, are presented in Part I. Despite the advantages, it is not always possible to solve financial problems numerically. In such cases, simulation methods are very useful. These are presented in Part II. The final part concentrates on statistical methods. These enable the reader to train the economic models to real data, test the suitability of the model, and forecast important quantities like risk. The basic statistical topics of descriptive, inferential, multivariate, and time series analysis are presented with their applications in finance. A whole chapter is devoted to the quantification of risk and another to high-frequency data. Two chapters expose the reader to cutting-edge Machine Learning techniques. Two topics related to simulation, namely Bayesian Monte Carlo and resampling methods, are included in the third section as they require some basic statistical knowledge. Necessary theory of mathematical finance and extreme value, as well as an introduction to R, is presented in the Appendix.

Computational Finance With R PDF

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