Read Statistics and Data Analysis for Financial Engineering: with R e…Note 2 : Recent changes to the reading list are denoted with. Note 3 : My Book chapters are preliminary and incomplete and are not guaranteed to be free of errors. Also, as the quarter progresses I will be making changes and additions to the notes so check the revision dates to make sure you have the most up to date set of notes. Please let me know if you find typos or other errors. Ruppert, chapter 2 Returns. EZ, Book chapter on return calculations.
Quant Reading List 2019 - Math, Stats, CS, Data Science, Finance, Soft Skills, Economics, Business
Statistics and Data Analysis for Financial Engineering: 2nd Edition
This is a nice book that blends modern statistical techniques with practical R code that makes it easy to explore, understand, and model financial data. Readers unfamiliar with this book can see what others have said here. To learn this material as well as possible I worked through the book's problems and exercises and wrote up my solutions and put them in book form. The R scripts used in the solutions for the various chapters are given below. The solution manual has detailed explanations of the R codes below and further explanations of the questions asked in the end of chapter exercises. Note that this solution manual is for the 2nd edition of the textbook. One should note that there are are large number of overlapping problems between the two editions of the textbook.
Financial engineers have access to enormous quantities of data but need powerful methods for extracting quantitative information, particularly about volatility and risks. Key features of this textbook are: illustration of concepts with financial markets and economic data, R Labs with real-data exercises, and integration of graphical and analytic methods for modeling and diagnosing modeling errors. Despite some overlap with the author's undergraduate textbook Statistics and Finance: An Introduction, this book differs from that earlier volume in several important aspects: it is graduate-level; computations and graphics are done in R; and many advanced topics are covered, for example, multivariate distributions, copulas, Bayesian computations, VaR and expected shortfall, and cointegration. The prerequisites are basic statistics and probability, matrices and linear algebra, and calculus. Some exposure to finance is helpful. David Ruppert is Andrew Schultz, Jr. His research areas include asymptotic theory, semiparametric regression, functional data analysis, biostatistics, model calibration, measurement error, and astrostatistics.
About this book
It seems that you're in Germany. We have a dedicated site for Germany. The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. These methods are critical because financial engineers now have access to enormous quantities of data.