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Learning Objectives


The course provides a basic knowledge of the theory of coding for Financial Applications using Matlab. The course will also focus on theoretical and applied aspects of the linear regression model. Students will apply their knowledge to write some codes to solve classical problems in the field of Quantitative Finance.


It is required to understand the basics of Matlab programming and linear regression theory. The understanding of elementary Matlab codes with statistical applications and the ability to formulate linear regression models to be applied to the study of problems related to finance (such as, for example, the capital asset pricing model) are required.


At the end of the course, students must be able to formulate the analysis of a simple financial problem in terms of linear regressions, derive the theoretical properties of the formulated model (in particular of the estimators used to estimate the model) and to produce efficient Matlab codes able to perform such analyzes on real datasets.


At the end of the course, it is required to have the tools to evaluate in which economic contexts it is reasonable to apply linear regression models and, more specifically, the ability to judge which formulation is the best depending on the context (time series of asset prices or GDP etc ...).


At the end of the course, students are required to be able to express themselves in a rigorous, precise and synthetic language both as regards the theory of OLS estimators and their asymptotic properties, as well as the commands and user interface of Matlab.


The course provides the tools to work independently on basic econometric models and on how to translate these problems into suitable codes for estimating them on time series of various kinds. Students, at the end of the course, will have a good learning autonomy in relation to scientific articles of an econometric-financial nature.


standard results of calculus and linear algebra


The course is divided into two blocks.

Block 1: Matlab.
Working with the MATLAB User Interface
Variables and Commands
Working with Vectors
Working with Matrices
Automating Commands with Scripts
Dates and Times
Working with Tabular Data
Conditional Data Selection
Working with Missing Data
Writing Functions
Increasing Automation with Programming Constructs
Fitting Models to Empirical Data
Troubleshooting Code

Block 2: Static Regression.
Simple linear regression model:
• OLS estimators: derivation through first order conditions.
• Definition and interpretation of the coefficient of determination.
• Unbiasedness of OLS estiamtors: theory and practice (with Matlab).
• Conditional variance of OLS estiamtors: theory and practice (with Matlab).
• Unbiased estimator of error variance.
• Statistical inference: hypothesis testing and t-statistic.
• Statistical inference: the Capital Asset Pricing Model and the beta of a stock.
Multiple linear regression model:
• Recap of matrix algebra and gradient of a function.
• OLS estimators: derivation through first order conditions.
• Unbiasedness of OLS estiamtors.
• Conditional variance-covariance matrix of OLS estiamtors.
• Unbiased estimator of error variance.
• Multicollinearity.
• Blueness of the OLS estimator: the Gauss-Markov theorem.
• Multiple hypothesis testing.
• Maximum Likelihood Estimator.
• Model comparison.
• Omitted and irrelevant variables.
• Measurement errorrs.
• OLS asymptotics.


Matlab for Financial Applications
Wooldridge J. M. (2016). Introductory Econometrics: A Modern Approach.
Brooks C. (2014). Introductory Econometrics for Finance.


Matlab for Financial Applications
Wooldridge J. M. (2016). Introductory Econometrics: A Modern Approach.
Brooks C. (2014). Introductory Econometrics for Finance.

Teaching methods

Lectures are based on the online course "Matlab for Financial Applications" by Mathworks. Part of the lectures consists of a written test to verify the knowledge of the students on the last topics studied and foster the discussion with the lecurer.

Exam Rules

This is a Pass or Fail course.
The exam consists of one written test.
To pass the course it is required to answer correctly to at least 80% of the questions of the written test (final exam).

Exemption tests will be administered during the course.
Passing all the exemption test will be sufficient to pass the course.
Passing some, but not all, of the exemption tests will lower the threshold to pass the final exam. Each passed test lowers the threshold by 10%, that is, passing one test brings the threshold to 70%, two tests to 60%, etc…

The exemptions will be valid only for the winter session.
There will be no make ups for missed exemptions tests.

The student's evaluation includes a written test in which basic Matlab programming problems and linear regressions on artificial datasets are proposed. The student will have to demonstrate the ability to produce efficient codes for the analysis of financial datasets with particular reference to the theory of linear regressions using OLS.

A short interview will take place to confirm the result of the written exam, if necessary.