Forecasting

Enseignants

Candelon Bertrand;

Langue
d'enseignement

Anglais

Préalables

You should have a knowledge of basic topics in statistics, econometrics and finance such as those covered in the following courses:
Fundamental mathematical and statistical concepts (such as those covered in Mathématiques avancées et fondements d'économétrie [ LECGE1337 ])
Advanced Finance [LLSMS2100A or LLSMS2100B]
In addition, this course is reserved for students with a bachelor's degree in business engineering or students with equivalent quantitative method skills

Thèmes abordés

This course overviews topics in computational finance and financial econometrics (data sciences applied to finance).
The emphasis of the course will be on making the transition from an economic model of asset return behavior to an econometric model using real data.
This involves:

1. exploratory data analysis;
2. specification of models to explain the data;
3. estimation and evaluation of models;
4. testing the economic implications of the model;
5. forecasting from the model.

The modeling process requires the use of economic theory, matrix algebra, optimization techniques, probability models, statistical analysis/econometrics, and statistical software (R).
Both edX and DataCamp plateforms will be used to allow practical training and continuous learning on R.

Acquis
d'apprentissage

A la fin de cette unité d’enseignement, l’étudiant est capable de :
1	Upon completion of this course, students are expected to complete the following key tasks: Have a good understanding of important issues in financial econometrics and computational finance; Be able to apply concepts and tools learned in class. Upon completion of this course, students are expected to develop the following capabilities :          3. Knowledge and reasoning;          4. Critical thinking skills.

Contenu

Ce cours se propose de couvrir les aspets théoriques et pratiques de la prévision. Les sujets abordés sont:
. Rappel des bases de l'économétrie des séries temporelles.
. Processus AR, MA, ARMA.
. Racines unitaires et non-stationarité.
. Modèles VAR and VECM.
. Modèles récent pour la prévision.
Tous les exercices et projets seront faits sour R.

Méthodes d'enseignement

Lectures, classes inversées, ateliers, interventions d'experts, projet final.

Modes d'évaluation
des acquis des étudiants

Workshop hebdomadaire, projet final, défence orale.

Ressources
en ligne

Moodle et teams

Bibliographie

Forecasting: Principles and Practice (FPP): Rob J Hyndman and George Athanasopoulos,  https://otexts.com/fpp2/
Introduction to Econometrics with R (IER): Christoph Hanck, Martin Arnold, Alexander Gerber, and Martin Schmelzer,  https://www.econometrics-with-r.org/ 

Faculté ou entité
en charge

CLSM