APPLIES TO ACADEMIC YEAR 2014/2015

EXC 3610 Empirical Methods in Finance

Responsible for the course
Benjamin Holcblat

Department
Department of Financial Economics

Term
According to study plan

ECTS Credits
7,5

Language of instruction
English

Introduction
Welcome to Empirical Methods in Finance. Empirical methods aim at taking advantage of the information contained in data for decision-making. We are all prone to different kind of biases such as "overconfidence" or "recency." Quantitative empirical methods help us to discipline decision making process. More specifically, they allow to test the existence of a relation between variables (e.g., does inflation affect nominal interest rates?), quantify this relation (e.g., a one percent increase in inflation should lead to how much increase in nominal interest rate?) and forecast the evolution of variables (e.g, which interest rate should we expect in six month from now?).

Learning outcome
The aims of this course are to introduce students to important econometric techniques that are used in empirical finance and to create awareness with students of how these techniques can be applied. More specifically, on completion of the course the students' acquired knowledge and skills should be as follows:

Acquired knowledge
On completion of the course students should:

• Understand the importance of basic data handling involving different graphical representations, descriptive statistics such as the mean, median, variance, standard deviation, skewness, kurtosis.
• Be able to interpret the above mentioned numerical measures.
• Understand the difference between the unconditional and conditional variance, and what the ARCH effect is.
• Understand the law of large number and central limit theorem, and how they are used in practice
• Understand what a Monte-Carlo simulation is.
• Understand basic concepts of programming: loops ("while" and "for"), if condition ("if") and logical operators ("and" and "or" )
• Understand the difference between an econometric model and a financial model.
• Understand what is meant by correlation and regression analysis - and the difference between them.
• Understand that correlation is not causation.
• Understand some of the peculiarities of financial data.
• Understand what is meant by Ordinary Least Squares (OLS) - the estimation technique used in order to estimate our econometric model.
• Understand how to interpret the estimated model.
• Understand the statistical assumptions that OLS rests upon.

• Perform basic data handling
• Basic programming skills
• Run basic Monte Carlo simulations
• Estimate financial models formulated as linear regression models (Econometric models).
• Test the statistical assumptions underlying OLS.
• Take corrective action if some of these assumptions are violated.

• Probability basics
• Why and when econometrics can work
• Econometrics basics

• Data and computer basics
• Introduction to R
• Introduction to programming

• Simple regression analysis
• Regression analysis with multiple explanatory variables
• Limits and assumptions of regression analysis