Financial Econometrics

Introduction

Financial econometrics can be understood as the application of statistical techniques to data using the statistical programming language R (which is widely used in the financial industry) to answer questions in finance. Therefore, financial econometrics can be used to test theories in finance. As such, it supports financial decision-making.

Course content

Introduction and mathematical foundations

• Steps involved in formulating an econometric model
• Functions
• Matrices

Statistical foundations and dealing with data

• Probability and probability distributions
• Descriptive statistics
• Types of data and data aggregation
• Simple returns vs. log returns

Review: The capital asset pricing model (CAPM)

• The assumptions underlying the CAPM
• Security market line
• Capital market line
• Sharpe ratio, Treynor ratio, Jensen's alpha

The classical linear regression model (CLRM)

• Simple regression
• The assumptions underlying the CLRM
• Properties of the OLS estimator
• Standard errors
• Statistical inference
• t-statistic
• p-value

Further development and analysis of the CLRM

• From univariate to multivariate regression
• Parameter estimation and standard errors in the multivariate regression framework
• Testing multiple hypotheses: the F-test
• R² and adjusted R²

CLRM assumptions and the diagnostic tests

• Assumption 1: Errors have zero mean
• Assumption 2: Errors have constant variance
• Assumption 3: Errors are linearly independent of each other
• Assumption 4: Errors are linearly independent of x-variables
• Assumption 5: Errors are normally distributed
• Multicollinearity
• Omitted variable bias
• Parameter stability tests