30285 - EMPIRICAL METHODS FOR FINANCE (INTRODUCTION TO ECONOMETRICS FOR FINANCE)
Department of Finance
Course taught in English
CLEAM (6 credits - I sem. - OP | SECS-P/05) - CLEF (6 credits - I sem. - OP | SECS-P/05) - CLEACC (6 credits - I sem. - OP | SECS-P/05) - BESS-CLES (6 credits - I sem. - OP | SECS-P/05) - WBB (6 credits - I sem. - OP | SECS-P/05) - BIEF (6 credits - I sem. - OBCUR | SECS-P/05) - BIEM (6 credits - I sem. - OP | SECS-P/05) - BIG (6 credits - I sem. - OP | SECS-P/05) - BEMACS (6 credits - I sem. - OP | SECS-P/05) - BAI (6 credits - I sem. - OP | SECS-P/05)
Course Director:
MARIANO MASSIMILIANO CROCE
MARIANO MASSIMILIANO CROCE
Suggested background knowledge
Students are expected to have already attended a core course in statistics and to be familiar with undergraduate calculus and linear algebra. Prior exposure to financial courses (financial markets and institutions, investments and corporate finance) is also recommended to understand the applications covered in class.
PREREQUISITES
Only for BIEF students: the exam code 30001 Statistics is a prerequisite of the exam empirical methods for finances.
Mission & Content Summary
MISSION
The objective of this course is to introduce the main econometric methods and techniques used in empirical finance. This is an ambitious task that brings together different type of knowledge: finance theory, statistics, programming.
You learn how to use software(s) to specify, estimate and simulate model of financial data to be used for asset allocation, risk measurement and risk management.
The teaching strategy is based on providing inputs (learning opportunities) to students that are supposed to active elaborate them to produce their knowledge. The course is designed to give opportunities. The decision of how many opportunities to take and how to take them is left to course participants. The final assessment is designed to evaluate the solidity of the foundation in the relevant tools for financial time-series modelling achieved by the students at the end of the course.
CONTENT SUMMARY
After going over `The introduction to the course’ lecture, we address the following:
- Basic knowledge in finance, statistics, probability.
- Introduction to programming.
- Returns: definitions, interpretation; measurement; data collection; analysis.
- Modeling and Simulating Returns.
- Estimating Linear Models of Returns.
- Interpreting Regression Results.
- TBA.
- High-order risk sources.
The detailed schedule will be issued on Bboard and will be updated day-by-day to reflect what
done in class.
Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
The objective of this course is to introduce the main econometric methods and techniques used in empirical finance. This is an ambitious task that brings together different type of knowledge: finance theory, statistics, programming. You learn how to use software, to specify, estimate and simulate model of financial data to be used for asset allocation, risk measurement and risk management. The course is designed to give opportunities. The decision of how many opportunities to take and how to take them is left to course participants.
APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
- Apply econometric techniques to study returns both in the time-series and in the cross section.
Teaching methods
- Lectures
- Practical Exercises
- Individual works / Assignments
DETAILS
The main inputs provided to the students are references, slides, notes, draft Python codes and exercises designed to provide challenges that stimulate learning. The empirical applications are based on databases freely available on the web.
Assessment methods
| Continuous assessment | Partial exams | General exam | |
|---|---|---|---|
|
x | ||
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x |
ATTENDING AND NOT ATTENDING STUDENTS
The final grade of attending students will be a weighted average of:
-- homeworks (they will count for 20%) ;
-- in-class quiz (provided in the middle of the semester);
and
-- final exam.
Students will be expected to go over an In-class Quiz in the middle of the semester. This quiz may count for 20% of the final grade if the student performs better in the quiz than in the final
exam. Otherwise, I just give zero weight to the quiz and shift this 20% to the final exam. The final exam counts for 60%--or 80% if the performance in the final is better than in the quiz. This scoring scheme gives a lot of insurance to students (Have a bad day on the day of the quiz? No worries, perform better in the final and I will discard your Quiz with poor results).
This computation of the grade applies to students taking the exam in December or January. In all other cases, the final grade will depend solely on the performance in the final exam.
There is no difference between attending and non-attending students concerning both teaching methods and expectations. All relevant materials and homeworks are delivered online through Bboard. All homeworks can be submitted online. Hence non-attending students can deliver their HWs as attending students. Their final grade will be a weighted average of both their homeworks (20% weight) and their final exam (80% weight). This computation of the grade applies to students taking the exam in December or January. In all other cases, the final grade will depend solely on the performance in the final exam (100% weight). Hence a non-attending student who takes my exam in December or January without having delivered the homeworks has a final score computed as: 20%*0 +80%*exam performance. A non-attending student taking the exam before or after the summer break get 100%*exam performance.
Teaching materials
ATTENDING AND NOT ATTENDING STUDENTS
- MAIN BOOK: “Introductory Econometrics for Finance” by Chris Brooks, 2nd Edition
- J. COCHRANE, "Asset pricing. Revised Edition". (only 2 chapters)
Last change 16/07/2024 14:08