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An Introduction to Analysis of Financial Data with R

Book Description

A complete set of statistical tools for beginning financial analysts from a leading authority

Written by one of the leading experts on the topic, An Introduction to Analysis of Financial Data with R explores basic concepts of visualization of financial data. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-world empirical research.

The author supplies a hands-on introduction to the analysis of financial data using the freely available R software package and case studies to illustrate actual implementations of the discussed methods. The book begins with the basics of financial data, discussing their summary statistics and related visualization methods. Subsequent chapters explore basic time series analysis and simple econometric models for business, finance, and economics as well as related topics including:

  • Linear time series analysis, with coverage of exponential smoothing for forecasting and methods for model comparison

  • Different approaches to calculating asset volatility and various volatility models

  • High-frequency financial data and simple models for price changes, trading intensity, and realized volatility

  • Quantitative methods for risk management, including value at risk and conditional value at risk

  • Econometric and statistical methods for risk assessment based on extreme value theory and quantile regression

  • Throughout the book, the visual nature of the topic is showcased through graphical representations in R, and two detailed case studies demonstrate the relevance of statistics in finance. A related website features additional data sets and R scripts so readers can create their own simulations and test their comprehension of the presented techniques.

    An Introduction to Analysis of Financial Data with R is an excellent book for introductory courses on time series and business statistics at the upper-undergraduate and graduate level. The book is also an excellent resource for researchers and practitioners in the fields of business, finance, and economics who would like to enhance their understanding of financial data and today's financial markets.

    Table of Contents

    1. Coverpage
    2. Titlepage
    3. Copyright
    4. Dedication
    5. Contents
    6. Preface
    7. 1 FINANCIAL DATA AND THEIR PROPERTIES
      1. 1.1 Asset Returns
      2. 1.2 Bond Yields and Prices
      3. 1.3 Implied Volatility
      4. 1.4 R Packages and Demonstrations
      5. 1.5 Examples of Financial Data
      6. 1.6 Distributional Properties of Returns
      7. 1.7 Visualization of Financial Data
      8. 1.8 Some Statistical Distributions
      9. Exercises
      10. References
    8. 2 LINEAR MODELS FOR FINANCIAL TIME SERIES
      1. 2.1 Stationarity
      2. 2.2 Correlation and Autocorrelation Function
      3. 2.3 White Noise and Linear Time Series
      4. 2.4 Simple Autoregressive Models
      5. 2.5 Simple Moving Average Models
      6. 2.6 Simple ARMA Models
      7. 2.7 Unit-Root Nonstationarity
      8. 2.8 Exponential Smoothing
      9. 2.9 Seasonal Models
      10. 2.10 Regression Models with Time Series Errors
      11. 2.11 Long-Memory Models
      12. 2.12 Model Comparison and Averaging
      13. Exercises
      14. References
    9. 3 CASE STUDIES OF LINEAR TIME SERIES
      1. 3.1 Weekly Regular Gasoline Price
      2. 3.2 Global Temperature Anomalies
      3. 3.3 US Monthly Unemployment Rates
      4. Exercises
      5. References
    10. 4 ASSET VOLATILITY AND VOLATILITY MODELS
      1. 4.1 Characteristics of Volatility
      2. 4.2 Structure of a Model
      3. 4.3 Model Building
      4. 4.4 Testing for ARCH Effect
      5. 4.5 The ARCH Model
      6. 4.6 The GARCH Model
      7. 4.7 The Integrated GARCH Model
      8. 4.8 The GARCH-M Model
      9. 4.9 The Exponential Garch Model
      10. 4.10 The Threshold Garch Model
      11. 4.11 Asymmetric Power ARCH Models
      12. 4.12 Nonsymmetric GARCH Model
      13. 4.13 The Stochastic Volatility Model
      14. 4.14 Long-Memory Stochastic Volatility Models
      15. 4.15 Alternative Approaches
      16. Exercises
      17. References
    11. 5 APPLICATIONS OF VOLATILITY MODELS
      1. 5.1 Garch Volatility Term Structure
      2. 5.2 Option Pricing and Hedging
      3. 5.3 Time-Varying Correlations and Betas
      4. 5.4 Minimum Variance Portfolios
      5. 5.5 Prediction
      6. Exercises
      7. References
    12. 6 HIGH FREQUENCY FINANCIAL DATA
      1. 6.1 Nonsynchronous Trading
      2. 6.2 Bid–Ask Spread of Trading Prices
      3. 6.3 Empirical Characteristics of Trading Data
      4. 6.4 Models for Price Changes
      5. 6.5 Duration Models
      6. 6.6 Realized Volatility
      7. Appendix A: Some Probability Distributions
      8. Appendix B: Hazard Function
      9. Exercises
      10. References
    13. 7 VALUE AT RISK
      1. 7.1 Risk Measure and Coherence
      2. 7.2 Remarks on Calculating Risk Measures
      3. 7.3 Riskmetrics
      4. 7.4 An Econometric Approach
      5. 7.5 Quantile Estimation
      6. 7.6 Extreme Value Theory
      7. 7.7 An Extreme Value Approach to Var
      8. 7.8 Peaks Over Thresholds
      9. 7.9 The Stationary Loss Processes
      10. Exercises
      11. References
    14. Index