Statistical Analysis of Financial Data in R

  • René Carmona

Part of the Springer Texts in Statistics book series (STS)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Data Exploration, Estimation And Simulation

    1. Front Matter
      Pages 1-1
    2. René Carmona
      Pages 3-68
    3. René Carmona
      Pages 69-120
    4. René Carmona
      Pages 121-195
  3. Regression

    1. Front Matter
      Pages 197-197
    2. René Carmona
      Pages 199-276
    3. René Carmona
      Pages 277-341
  4. Time Series & State Space Models

    1. Front Matter
      Pages 343-343
    2. René Carmona
      Pages 345-421
    3. René Carmona
      Pages 473-533
  5. Background Material

    1. Front Matter
      Pages 535-535
    2. René Carmona
      Pages 537-558
  6. Back Matter
    Pages 559-588

About this book


Although there are many books on mathematical finance, few deal with the statistical aspects of modern data analysis as applied to financial problems. This book fills this gap by addressing some of the most challenging issues facing any financial engineer. It shows how sophisticated mathematics and modern statistical techniques can be used in concrete financial problems.

Concerns of risk management are addressed by the control of extreme values, the fitting of distributions with heavy tails, the computation of values at risk (VaR), and other measures of risk. Data description techniques such as principal component analysis (PCA), smoothing, and regression are applied to the construction of yield and forward curve. Nonparametric estimation and nonlinear filtering are used for option pricing and earnings prediction.

The book is intended for undergraduate students majoring in financial engineering, or graduate students in a Master in finance or MBA program. Because it was designed as a teaching vehicle, it is sprinkled with practical examples using market data, and each chapter ends with exercises. Practical examples are solved in the computing environment of R. They illustrate problems occurring in the commodity and energy markets, the fixed income markets as well as the equity markets, and even some new emerging markets like the weather markets.

The book can help quantitative analysts by guiding them through the details of statistical model estimation and implementation. It will also be of interest to researchers wishing to manipulate financial data, implement abstract concepts, and test mathematical theories, especially by addressing practical issues that are often neglected in the presentation of the theory.


financial data distributions financial data with R financial engineering with R mathematical finance methods for quantitative analysist univariate data distributions

Authors and affiliations

  • René Carmona
    • 1
  1. 1.Financial EngineeringPrinceton UniversityPrincetonUSA

Bibliographic information