Название: R Programming and Its Applications in Financial Mathematics Автор: Shuichi Ohsaki, Jori Ruppert-Felsot, Daisuke Yoshikawa
Издательство: CRC Press
ISBN: 1498766099
Год: 2018
Страниц: 258
Язык: английский
Формат: True PDF, DjVu
Размер: 15.1 MB
В этой книге представлено введение в программирование на языке R и краткое изложение финансовой математики. Важность получения базовых знаний о вычислительных методах продолжает возрастать для тех, кто работает в отрасли финансовых услуг. Теория вычислительного финансирования развилась вместе с достижениями в области вычислительной техники. Таким образом, цель этой книги - представить введение в различные вычислительные методы, используемые в индустрии финансовых услуг, с помощью языка программирования R. Эта книга предназначена для аспирантов, заинтересованных в вычислительном финансировании, младших банкиров, экономистов и трейдеров, работающих в финансовой отрасли.
This book provides an introduction to R programming and a summary of financial mathematics.
The importance of having basic knowledge of computational methods continues to increase for those working in the financial services industry. Computational finance theory has developed along with advancements in computing technology. The objective of this book is therefore to present an introduction to the various computing methods used in the financial service industry, via the R programming language. This book is intended for graduate students who are interested in computational finance, junior bankers, economists and traders working in the finance industry.
We have several reasons why R has been used, rather than other available languages; e.g., C++, C#, Java, Python, EXCEL VBA, and MatLab, etc. First, R is available free and is an open source programming language. As such it is easily extensible through the addition of packages and has a large and active community of R users. And as a high-level scripting language, it is very easy to set up and to use. Second, R was developed for statistical computation and thus comes equipped with various packages which enable us to do regression analysis and statistical tests, etc. This reduces the time and effort required for implementation of many statistical methods and allows us to execute numerical experiments easily. Third, R comes equipped with a graphic capability to visualize large sets of data and the results of analysis. This is an attractive feature, particularly if we want to produce high-quality graphics for publication.
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