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Introduction

In mathematical disciplines, there is always an answer to “why is that so?”, yet due to time-constraints, many presentations of financial and econometric tools are based around black boxes, even when solid mathematical arguments and assumptions led to their development. In this course, we take our time and work through the reasoning and motivation for some well-known and elementary tools in finance and economics that are based on statistics. Examples include the market beta, the Sharpe ratio, confidence intervals and omitted variable bias. We will see why such tools and formulas are the way they are and will use simulation and programming in R to see how they behave outside the conditions they were originally developed under.

Using simple mathematical tools, such as sums, the normal distribution, and central programming techniques such as the for-loop, we aim at understanding these foundational tools in a rather complete manner. This enables the student to much easier understand complex tools used both in later courses at higher levels as well as being able to responsibly use such tools in an industry-setting.

Course content

1. Foundational issues: (A) Averages, variance and covariances, Pearson correlation, ordinary least squares estimation. (B) Population and sample properties. Statistical inference, consistency, and bias.
2. Topics in linear regression, including basic issues in time series analysis and panel data.