Econometrics for Finance module (BU52017)

This module provides an understanding of theories and concepts and essential skills for estimating and interpreting economic models. You will learn to apply techniques to finance problems by translating theoretical models, analysing data, and interpreting results.

You will learn how to apply statistical and mathematical techniques to analyse financial data. By combining economic and financial theory, analysing statistics and financial modelling, you will be able to gain key insights to help make important business decisions.

What you will learn

In this module, you will:

• gain knowledge and understanding of key concepts, including the linear regression model and the impact of violating its assumptions on regression results
• learn about the statistical properties of financial data, such as stationarity, time series estimation, and tests for unit roots and cointegration
• develop subject-specific practical and intellectual skills
• learn how to present estimation output, including graphs, concisely and informally
• cultivate problem-solving skills, interpret estimation output, and make inferences from the information provided
• evaluate different methodologies and theories used in empirical finance, such as the Efficient Market Hypothesis.

By the end of this module, you will be able to:

• understand asset pricing, risk management, portfolio optimisation, and other financial decision-making processes
• use econometric methods to test and validate financial theories and make informed predictions about market behaviour
• discuss the empirical methods used in analysing financial asset prices
• quantify the relationships between economic variables and financial markets
• use Eviews, the leading econometric software gaining experience before end of module exam

Assignments / assessment

Weekly quizzes (20%)

• Online multiple choice
• To be completed as part of your self led learning

Final Exam (80%)

• In-person and will mainly consist of open-ended questions and Eviews output

Teaching methods / timetable

As an active learner, you will engage with the module in various ways, including (but not limited to) on-campus lectures, computing labs, interactive online sessions, assigned readings and videos, short quizzes, collaborative group work, and contribution to discussion boards.