APPLIES TO ACADEMIC YEAR 2015/2016

EXC 3672 Analyses of Financial Data

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
The course aims 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 i) introduce students to important empirical quantitative techniques that are used in finance and more generally in business, ii) make students able to apply them appropriately, and iii) prepare students for subsequent course work in finance and business. More specifically, on completion of the course the students’ acquired knowledge and skills should be as follows:
On completion of the course students should:

• Understand basic measures of location, tendency and dispersion such as the expectation, median, variance, standard deviation, skewness, kurtosis.
• have learned to interpret the above mentioned numerical measures.
• Understand at a basic level the notion of conditional expectation and conditional variance.
• Understand basic measures of association such as covariance and correlation.
• Understand what is meant by correlation and regression analysis - and the difference between them.
• Understand some of the peculiarities of financial data.
• Understand at basic level what a programming language is.
• Understand the difference between an estimated model and “true” 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 practical implications of the assumptions on which OLS rely.

• Perform basic data handling
• Estimate financial models formulated as linear regression models
• Test the statistical assumptions underlying OLS.

• Probability basics
• When and why econometric can work
• Econometric basics

• Data and computer basics: data in finance, what is a programming language
• Introduction to R: Basic data manipulation with R
• Introduction to programming with R: Control structures in R, Monte Carlo simulation

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