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Analysis of Financial Data

Book Description

Analysis of Financial Data teaches the basic methods and techniques of data analysis to finance students, by showing them how to apply such techniques in the context of real-world empirical problems.

Adopting a largely non-mathematical approach Analysis of Financial Data relies more on verbal intuition and graphical methods for understanding.

Key features include:

  • Coverage of many of the major tools used by the financial economist e.g. correlation, regression, time series analysis and methods for analyzing financial volatility.

  • Extensive use of real data examples, which involves readers in hands-on computer work.

  • Mathematical techniques at a level suited to MBA students and undergraduates taking a first course in the topic.

Supplementary material for readers and lecturers provided on an accompanying website.

Table of Contents

  1. Copyright
  2. Preface
  3. 1. Introduction
    1. 1.1. Organization of the book
    2. 1.2. Useful background
    3. 1.3. Appendix 1.1: Concepts in mathematics used in this book
      1. 1.3.1. The equation of a straight line
      2. 1.3.2. Summation notation
      3. 1.3.3. Logarithms
  4. 2. Basic data handling
    1. 2.1. Types of financial data
      1. 2.1.1. Time series data
      2. 2.1.2. Cross-sectional data
      3. 2.1.3. The distinction between qualitative and quantitative data
      4. 2.1.4. Panel data
      5. 2.1.5. Data transformations: levels, growth rates, returns and excess returns
      6. 2.1.6. Index numbers
    2. 2.2. Obtaining data
    3. 2.3. Working with data: graphical methods
      1. 2.3.1. Time series graphs
      2. 2.3.2. Histograms
      3. 2.3.3. XY-plots
    4. 2.4. Working with data: descriptive statistics
    5. 2.5. Expected values and variances
    6. 2.6. Chapter summary
    7. 2.7. Appendix 2.1: Index numbers
      1. 2.7.1. Calculating a Megaco price index
      2. 2.7.2. Calculating a stock price index
    8. 2.8. Appendix 2.2: Advanced descriptive statistics
  5. 3. Correlation
    1. 3.1. Understanding correlation
      1. 3.1.1. Properties of correlation
      2. 3.1.2. Understanding correlation through verbal reasoning
    2. 3.2. Understanding why variables are correlated
    3. 3.3. Understanding correlation through XY-plots
    4. 3.4. Correlation between several variables
    5. 3.5. Covariances and population correlations
    6. 3.6. Chapter summary
    7. 3.7. Appendix 3.1: Mathematical details
  6. 4. An introduction to simple regression
    1. 4.1. Regression as a best fitting line
    2. 4.2. Interpreting OLS estimates
    3. 4.3. Fitted values and R2: measuring the fit of a regression model
    4. 4.4. Nonlinearity in regression
    5. 4.5. Chapter summary
    6. 4.6. Appendix 4.1: Mathematical details
  7. 5. Statistical aspects of regression
    1. 5.1. Which factors affect the accuracy of the estimate ?
    2. 5.2. Calculating a confidence interval for β
    3. 5.3. Testing whether β = 0
    4. 5.4. Hypothesis testing involving R2: the F-statistic
    5. 5.5. Chapter summary
    6. 5.6. Appendix 5.1: Using statistical tables for testing whether β = 0
  8. 6. Multiple regression
    1. 6.1. Regression as a best fitting line
    2. 6.2. Ordinary least squares estimation of the multiple regression model
    3. 6.3. Statistical aspects of multiple regression
    4. 6.4. Interpreting OLS estimates
    5. 6.5. Pitfalls of using simple regression in a multiple regression context
    6. 6.6. Omitted variables bias
    7. 6.7. Multicollinearity
    8. 6.8. Chapter summary
    9. 6.9. Appendix 6.1: Mathematical interpretation of regression coefficients
  9. 7. Regression with dummy variables
    1. 7.1. Simple regression with a dummy variable
    2. 7.2. Multiple regression with dummy variables
    3. 7.3. Multiple regression with both dummy and non-dummy explanatory variables
    4. 7.4. Interacting dummy and non-dummy variables
    5. 7.5. What if the dependent variable is a dummy?
    6. 7.6. Chapter summary
  10. 8. Regression with lagged explanatory variables
    1. 8.1. Aside on lagged variables
    2. 8.2. Aside on notation
    3. 8.3. Selection of lag order
    4. 8.4. Chapter summary
  11. 9. Univariate time series analysis
    1. 9.1. The autocorrelation function
      1. 9.1.1. Aside
    2. 9.2. The autoregressive model for univariate time series
    3. 9.3. Nonstationary versus stationary time series
    4. 9.4. Extensions of the AR(1) model
    5. 9.5. Testing in the AR(p) with deterministic trend model
      1. 9.5.1. Testing involving α, γ1, ..., γp−1, and δ
      2. 9.5.2. Testing involving ρ
    6. 9.6. Chapter summary
    7. 9.7. Appendix 9.1: Mathematical intuition for the AR(1) model
  12. 10. Regression with time series variables
    1. 10.1. Time series regression when X and Y are stationary
      1. 10.1.1. Aside for Excel users
    2. 10.2. Time series regression when Y and X have unit roots: spurious regression
    3. 10.3. Time series regression when Y and X have unit roots: cointegration
      1. 10.3.1. Estimation and testing with cointegrated variables
    4. 10.4. Time series regression when Y and X are cointegrated: the error correction model
    5. 10.5. Time series regression when Y and X have unit roots but are not cointegrated
    6. 10.6. Chapter summary
  13. 11. Regression with time series variables with several equations
    1. 11.1. Granger causality
      1. 11.1.1. Granger causality in a simple ADL model
      2. 11.1.2. Granger causality in an ADL model with p and q lags
      3. 11.1.3. Causality in both directions
      4. 11.1.4. Granger causality with cointegrated variables
    2. 11.2. Vector autoregressions
      1. 11.2.1. Lag length selection in VARs
      2. 11.2.2. Forecasting with VARs
      3. 11.2.3. Vector autoregressions with cointegrated variables
    3. 11.3. Chapter summary
    4. 11.4. Appendix 11.1: Hypothesis tests involving more than one coefficient
    5. 11.5. Appendix 11.2: Variance decompositions
  14. 12. Financial volatility
    1. 12.1. Volatility in asset prices: Introduction
    2. 12.2. Autoregressive conditional heteroskedasticity (ARCH)
    3. 12.3. Chapter summary
  15. A. Writing an empirical project
    1. A.1. Description of a typical empirical project
    2. A.2. General considerations
  16. B. Data directory