# Statistical Analysis Of Financial Data In R

Although there are many books on mathematical finance, few deal with the statistical aspects of modern data analysis as applied to financial problems. This textbook fills this gap by addressing some of the most challenging issues facing financial engineers. It shows how sophisticated mathematics and modern statistical techniques can be used in the solutions of concrete financial problems. Concerns of risk management are addressed by the study of extreme values, the fitting of distributions with heavy tails, the computation of values at risk (VaR), and other measures of risk. Principal component analysis (PCA), smoothing, and regression techniques are applied to the construction of yield and forward curves. Time series analysis is applied to the study of temperature options and nonparametric estimation. Nonlinear filtering is applied to Monte Carlo simulations, option pricing and earnings prediction. This textbook is intended for undergraduate students majoring in financial engineering, or graduate students in a Master in finance or MBA program. It is sprinkled with practical examples using market data, and each chapter ends with exercises. Practical examples are solved in the R computing environment. They illustrate problems occurring in the commodity, energy and weather markets, as well as the fixed income, equity and credit markets. The examples, experiments and problem sets are based on the library Rsafd developed for the purpose of the text. The book should help quantitative analysts learn and implement advanced statistical concepts. Also, it will be valuable for researchers wishing to gain experience with financial data, implement and test mathematical theories, and address practical issues that are often ignored or underestimated in academic curricula.

## Statistical Analysis of Financial Data in R

RenÃ© Carmona is the Paul M. Wythes '55 Professor of Engineering and Finance at Princeton University in the department of Operations Research and Financial Engineering, and Director of Graduate Studies of the Bendheim Center for Finance. His publications include over one hundred articles and eight books in probability and statistics. He was elected Fellow of the Institute of Mathematical Statistics in 1984, and of the Society for Industrial and Applied Mathematics in 2010. He is on the editorial board of several peer-reviewed journals and book series. Professor Carmona has developed computer programs for teaching statistics and research in signal analysis and financial engineering. He has worked for many years on energy, the commodity markets and more recently in environmental economics, and he is recognized as a leading researcher and expert in these areas.

Statistical Analysis of Financial Data covers the use of statistical analysis and the methods of data science to model and analyze financial data. The first chapter is an overview of financial markets, describing the market operations and using exploratory data analysis to illustrate the nature of financial data. The software used to obtain the data for the examples in the first chapter and for all computations and to produce the graphs is R. However discussion of R is deferred to an appendix to the first chapter, where the basics of R, especially those most relevant in financial applications, are presented and illustrated. The appendix also describes how to use R to obtain current financial data from the internet.

Chapter 2 describes the methods of exploratory data analysis, especially graphical methods, and illustrates them on real financial data. Chapter 3 covers probability distributions useful in financial analysis, especially heavy-tailed distributions, and describes methods of computer simulation of financial data. Chapter 4 covers basic methods of statistical inference, especially the use of linear models in analysis, and Chapter 5 describes methods of time series with special emphasis on models and methods applicable to analysis of financial data.

"I thoroughly enjoyed reading the first two chapters of the book. Often, the first couple of chapters of a book provide a "boilerplate" discussion of the characteristics of the data and R. Here, the first two chapters are very well developed, to the point that they provide a good general resource to readers approaching the analysis of financial data from several different perspectives. For example, students in statistics usually approach the entire analysis of time series having in mind the potential application to the analysis of financial data, but they know nothing about the characteristics of the data and the financial markets...Just like the previous chapters, I broadly enjoyed reading this chapter. Prof. Gentle explains the topics clearly and often uses simulations to convey the intuition. That's also the way I like to teach these concepts and I think it enhances understanding among economics and finance students. I also commend the way he discusses the lag and difference operators and how they are implemented in R. He devotes quite some space to them, and I believe that is good as many texts go over these concepts too quickly for many students. Likewise, the discussion of the AR(I)MA models is very detailed and clear. Jan Annaert, University of Antwerp and Antwerp Management School

The course is divided into three parts of approximately the same lengths. Density estimation (heavy tail distributions) and dependence (correlation and copulas). Regression analysis (linear and robust alternatives, nonlinear, nonparametric,classification.) Machine learning (TensorFlow, neural networks, convolution networks and deep learning). The statistical analyzes, computations and numerical simulations are done in R or Python.

CFRM 410 Probability and Statistics for Computational Finance (3)Covers basic concepts and methods of probability and statistical analysis and modeling for computational and quantitative finance. Coverage is carefully aligned with leading problems concerning prices and returns of individual assets and portfolios of assets. Key applications include financial risk management and portfolio performance analysis. Prerequisite: CFRM 405.View course details in MyPlan: CFRM 410

CFRM 420 Introduction to Computational Finance and Financial Econometrics (3)Covers probability models, data analysis, quantitative, and statistical methods using applications in finance, and introduction to and use of the R programming system for data analysis and statistical modeling. Prerequisite: CFRM 405, CFRM 410, or instructor permission.View course details in MyPlan: CFRM 420

CFRM 421 Machine Learning for Finance (4)Fundamentals of machine learning techniques with applications to finance. Assessing, organizing, and analyzing financial data, and learning the analytical tools and numerical schemes in machine learning to perform statistical analysis on financial data. Develops practical financial tools such as trading rules and risk indicators. Prerequisite: CFRM 405 and CFRM 410.View course details in MyPlan: CFRM 421

CFRM 425 R Programming for Quantitative Finance (3)Introduction to R programming language for applications in quantitative finance. Covers R syntax, data structures & manipulation, data analysis and statistics. Working with time series and computing asset returns with R will be covered, as will be the R package system and contributed packages. Recommended: The course does not require prior R programming experience, but programming experience in another language is acceptable.View course details in MyPlan: CFRM 425

CFRM 501 Investment Science (4)Introduction to the mathematical, statistical and financial foundations of investment science. Topics include: utility functions, mean-variance portfolio theory, tail risk measures, factor model types for portfolio construction, classical and robust methods of fitting factor models, and covariance and correlation estimation. Prerequisite: CFRM 425. Offered: A.View course details in MyPlan: CFRM 501

CFRM 502 Financial Data Science (4)Covers applications of statistical techniques for analyzing financial data, as well as modeling and computational methods in key areas in quantitative finance. Includes factor modeling, financial time series, and portfolio analytics. Focuses on advanced topics in statistical finance, finance theory, and financial applications. Prerequisite: CFRM 501.View course details in MyPlan: CFRM 502

CFRM 504 Options and Other Derivatives (4)Covers financial instrument options and derivatives. Explores how to price options and other derivatives and use them to hedge investment risk. Involves theory, statistical modeling, numerical methods, and computation using the R programming language. Prerequisite: co-requisite: CFRM 501 or permission of instructor. Offered: A.View course details in MyPlan: CFRM 504

CFRM 506 Financial Data Access and Analysis with SQL, VBA, and Excel (4)Provides skills in retrieving and manipulating financial data and in creating computational solutions to quantitative finance problems using SQL, VBA, and Excel. Also teaches skills in leveraging the powerful financial data modeling and analysis capabilities of R in conjunction with SQL, VBA, and Excel. Prerequisite: either CFRM 501 or equivalent, or permission of instructor. Offered: A.View course details in MyPlan: CFRM 506 041b061a72