Info
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Course 2019-2020 a.y.

30188 - INTRODUCTORY FINANCIAL ECONOMETRICS

Department of Finance

Course taught in English

Go to class group/s: 31

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) - 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)

Classes: 31 (I sem.)
Instructors:
Class 31: 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.


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.

Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
  • Use software to specify, estimate and simulate model of financial data to be used for asset allocation, risk measurement and risk management.
APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
  • Apply econometric techniques to portfolio allocation and risk measurement.

Teaching methods
  • Face-to-face lectures
  • Exercises (exercises, database, software etc.)
DETAILS

The main inputs provided to the students are references, slides, notes, 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
  • Written individual exam (traditional/online)
  •     x
  • Individual assignment (report, exercise, presentation, project work etc.)
  •     x
    ATTENDING AND NOT ATTENDING STUDENTS

    The final grade depends on the individual performance of each student in all of the course-related examinations (final exam, individual Quizzes, ... ). The grading weights is detailed in class.


    Teaching materials
    ATTENDING AND NOT ATTENDING STUDENTS
    • J. COCHRANE, Asset pricing. Revised Edition.
    • TBA, communicated by July.
    Last change 25/07/2019 17:41