APPLIES TO ACADEMIC YEAR 2013/2014

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 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 the difference between an econometric model and a financial model.
• 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.
• Understand the concept of Autoregressive (AR) modelling.
• Understand, at a basic level, important time series issues such as Stationarity, Cointegration and Error Correction (ECM) modelling.
• Understand the difference between the unconditional and conditional variance and how the latter can be modelled with an ARCH-type model.

• Perform basic data handling
• Estimate financial models formulated as linear regression models (Econometric models).
• Test the statistical assumptions underlying OLS.
• Take corrective action if some of these assumpyions are violated.
• Estimate AR models.
• Test for Stationarity and Cointegration.
• Estimate Error Correction Models (ECMs).
• Test for ARCH effects.
• Estimate basic ARCH models and GARCH(1,1) models

1. Foundations for empirical methods in finance.
   • Basics of probability
   • Basics of inference
   • Introduction to implementation with R
2. Linear regression analysis.
   • Simple regression analysis
   • Regression analysis with multiple explanatory variables
   • Limits and assumptions of regression analysis
3. Introduction to time series.
   • Second-order stationarity
   • ARMA modelling
   • Limits of ARMA models: time varying volatility and unit roots

Computer-based tools
The software package R will be available on BI's computers. Other tools include Google, Yahoo finance and It's Learning.

Learning process and workload
A class will typically consist of a review of the last class, a lecture introducing new material and exercises that are solved on the white board by students.

Each topic will be accompanied by a hands-on practical application of an empirical finance topic.

The software package R will be an integral part of the coursework. R is a software that has become a standard for data analysis inside academia and corporations, especially in the finance industry. It is an open source software available free of charge on internet. The use of R will introduce students to some of the basics of programming. Programming is a skill typically required in the financial industry.

If a student misses a class, it is her/his responsibility to obtain any information provided in class that is not included on the course homepage/It's learning or in the text book.

A course of 7,5 ECTS credits corresponds to a workload of 200 hours. The following is an indication of the time required for different activities: