A good book recommends the application of R language in quantitative finance

Source: Internet
Author: User

To recommend a R language in the quantitative financial aspects of the Good book, is my teacher prepared, and I am very fortunate to participate in the preparation of this book process. The book was officially online in May 2015, and it has been around two years since the end of 2013. As soon as the book came out, it received a lot of attention and the readers were very good. The book itself belongs to a series of "Quantitative Economics series", a series of books with a similar cover and a blue background. Other books in this series may have been seen and are popular in the market. I would like to outline the contents of the book "R language applied in quantitative finance", which begins with the basics of r language, such as basic data structure, drawing, installation of packages, etc. and introduced some resources to learn r language, it is very rare, this book will r language in quantitative finance package done a comb, let everyone know that there are those packages can do quantitative finance, this is very good for beginners to help.

In the second chapter of the book, we mainly introduce some basic statistical models that r software can do, such as linear regression, stepwise regression, factor analysis, generalized linear regression model, some models inside time series, such as Granger causality test, error correction model and so on. And some simple optimization theories, such as linear optimization. And all of them give a complete case and detailed code.

In the third chapter of the book, it mainly introduces the application of R language in machine learning, such as neural network, support vector machine, and lasso model of high-dimensional problem. Each model has a special case that allows you to perceive the unique nature of these models.

In the fourth chapter of the book, the application of R language and quantitative finance is really beginning to be introduced. This chapter is mainly from the introduction of our common financial database began, that is to tell you how to obtain financial data, followed by the introduction of various data formats, and how to read, to do some preprocessing.

In the fifth chapter, the author introduces how R language calculates the asset yield and the distribution characteristics of the asset yield.

In the sixth chapter of the book, it introduces a large class of models in quantitative finance, such as volatility modeling, GARCH model and SV model, and high-frequency fluctuation modeling. This book gives a lot of case studies, with a lot of code attached.

In the seventh chapter of the book, we introduce the extremum distribution and corresponding model, and focus on the regression model of the number of digits. At present, there are few Chinese in the market for the regression of the number of digits, and the teacher Xu studies the number of years, which not only introduces the regression of the linear number of digits, but also the regression of nonlinear numbers and the use of the combination of nonparametric estimators.

In the eighth chapter of the book, the paper introduces the financial portfolio investment, and introduces the model calculation based on R.

In the Nineth chapter of the book, the paper introduces the pricing model of financial assets, involving CAPM,APT and option pricing model.

In the tenth chapter of the book, The Cointegration analysis is introduced.

In the 11th chapter of the book, the paper introduces the herding effect, and introduces the application of the neural network-based regression model in the herd effect.

In the last chapter of the book, some elements of micro-financial quantification are introduced, followed by the use of R+hadoop.

The book is rich in content. And each content gives its own accumulation in this respect, and in the form of a case to give, the book has 450 pages, all the code and data are public, can download from my github

Github:https://github.com/xiaoguozhi/r-programming-with-applications-to-financial-quantitive-analysis

Sincerely recommend to everyone


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