APPLIES TO ACADEMIC YEAR 2016/2017

FIN 3618 Financial Econometrics

Responsible for the course
Kjell Jørgensen

Department
Department of Finance

Term
According to study plan

ECTS Credits
7,5

Language of instruction
English

Introduction
Financial Econometrics can be understood as the application of statistical techniques to answer questions in finance. Financial Econometrics can therefore be used to test theories in finance. As such, it is supports financial decision-making.

Learning outcome
Acquired knowledge
During the course students should develop knowledge of:

• The role of Financial Econometrics in financial research.
• The steps involved in formulating, estimating and evaluating an econometric model.
• The role of descriptive statistics.
• The difference between correlation and regression.
• The concept of regression analysis using Ordinary Least Squares (OLS).
• Statistical inference within the regression model.
• How violations of the classical assumptions under lying OLS affect the regression model.
• Different model mis-specifications and biases.
• The concept of parameter stability/structural breaks.
• Univariate time series models (ARMA models).
• Forecasting financial variables.
• The concept of simultaneity bias.
• The concept of exogeneity.
• The estimation procedures for simultaneous equations systems.
• Vector autoregressive (VAR) models.
• Impulse responses and variance decompositions.
• The concept of stationarity.
• The concept of cointegration.
• Equilibrium or error correction models.

• Estimate and interpret descriptive statistics for the variables used in the model of interest.
• Estimate regression models using OLS.
• Make statistical inference (Hypothesis testing and/or Confidence Intervals) on the parameter estimates of the model.
• Perform the various tests of the classical assumptions underlying OLS.
• Identify potential mis-specifications and biases.
• Perform parameter stability tests (test for structural breaks).
• Use Information Criterias in order to select the appropriate Univariate time series model (ARMA model).
• Estimate and interpret Univariate time series models (ARMA models).
• Use forecasting techniques in order to forecast different financial variables.
• Use different evaluation criteria for forecast precision.
• Perform tests for exogeneity.
• Apply appropriate estimation techniques for simultaneous equation systems (ILS, 2SLS).
• Estimate vector autoregressive (VAR) models.
• Estimate impulse responses and variance decompositions.
• Perform tests for stationarity (Unit root tests).
• Perform tests for cointegration.
• Estimate equilibrium or error correction models.

• What is Financial Econometrics about
• Types of financial data
• Returns in financial modelling
• Steps involved in formulating an econometric model

• Functions
• Matrices
• Probability and probability distributions
• Descriptive statistics

• Regression versus correlation
• Simple regression
• The assumptions underlying the classical linear regression model
• Properties of the OLS etsimator
• Precision and standard errors
• Statistical inference

• Multiple regression
• Testing multiple hypothesis: the F-test
• Goodness of fit statistics

• Heteroscedasticity
• Autocorrelation
• Non-stochastic explanatory variables
• Multicolinearity
• Specification mistakes and biases
• Parameter stability tests

• Moving average (MA) processes
• Autoregressive (AR) processes
• The Box-Jenkins methodology (Information criteria)
• Forecasting financial variables

• Simultaneous equations bias
• Exogeneity
• Estimation procedures for simultaneous equations systems
• Vector autoregressive (VAR) models
• Impulse responses and variance decompositions

• Stationarity and unit root testing
• Cointegration
• Equilibrium correction or error correction models
• Methods of parameter estimation in cointegrated systems