93019 - ARPM BOOTCAMP

Scheda insegnamento

SDGs

L'insegnamento contribuisce al perseguimento degli Obiettivi di Sviluppo Sostenibile dell'Agenda 2030 dell'ONU.

Istruzione di qualità

Anno Accademico 2020/2021

Conoscenze e abilità da conseguire

You will gain a broad overview of 4 learning modules: Data Science for Finance, Financial Engineering for Investement, Quantitative Risk Management, Quantitative Portfolio Management. You will grasp the intuition behind all the quantitative techniques covered by the course, and master selected topics within each learning module. Students will be able to avoid the most common pitfalls in risk management and portfolio management applications. Students will be able to interact with their classmates (and with the ARPM community) using a common language and notation. Students will grasp the intuition behind all the techniques covered by the course, and they will be able to navigate the ARPM Lab to find detailed reference material to deepen their knowledge and practice.

Contenuti

In order to attend the course in the best possible way, it is highly recommended to choose the ARPM Bootcamp before 10th February, so you will be able to have the access to the platform and start your classes on time!

Please, once added it to your study plan, send also an email (before 10th February) from your Unibo email account to marialuigia.loiudice@unibo.it [mailto:marialuigia.loiudice@unibo.it] (write in the subject e-mail “ARPM BOOTCAMP attendance”) to confirm your attendance.

The Master in Quantitative Finance in partnership with ARPM [https://www.arpm.co/lab/], offers Advanced Risk and Portfolio Management as one of its courses as elective, assigning 6 CFU.

Upon successful completion of the course, you will be able to:
- correctly map all the techniques adopted in quantitative finance onto a unified theoretical framework, appreciating the interconnections, and gaining a fresh perspective on the known techniques;
- avoid the most common pitfalls in risk management and portfolio management applications;
- interact with your classmates (and with the ARPM community) using a common language and notation;
- navigate the ARPM Lab to find detailed reference material to deepen your knowledge of the topics covered by the course, and more.

The program includes the most advanced quantitative techniques in:

Data science and machine learning
Econometrics
Factor modeling
Portfolio construction

Algorithmic trading
Investment risk management
Liquidity modeling
Enterprise risk management



To ensure a balanced mix of theory and applications, the curriculum is best taught through four all-encompassing, mutually exclusive, core learning courses that cover all the topics of the ARPM Lab. More precisely:

Data Science for Finance

Financial Engineering for Investment

Quantitative Risk Management

Quantitative Portfolio Management


Refreshers are also offered to brush up on the basic concepts:

Mathematics

Python

MATLAB

Testi/Bibliografia

The ARPM Lab contains all the support materials to learn and practice the concepts covered during the lectures:

Video lectures

Theory  

Case studies  

Data animations

Code

Documentation  

Slides  

Exercises

Metodi didattici

The ARPM Lab consists of lectures, review sessions and exercises sourced from the content of the platform.
You will have a fully access to the online lessons (on-demand version) from the comfort of your home or office. You will be requested to follow a scheduled program and to practice on them. Your improvement will be recorded and the topics will be discussed, checked and evaluated during the periodic encounters with the teacher. The encounters will be offered both in presence and online.

Modalità di verifica e valutazione dell'apprendimento

Your improvement will be recorded and the topics will be discussed, checked and evaluated during the periodic encounters with the teacher. The encounters will be offered both in presence and online.

The final grade will be the mean of the periodic evaluations.

Orario di ricevimento

Consulta il sito web di Silvia Romagnoli