Ateneo - UPO

There are four modules:
Module 1 – Market and Credit Risk (Prof. Gianluca Fusai) - 6 CFU

Module 2 – Monte Carlo Simulation and Its Application to Option Pricing and Risk Measures. (Prof. Ioannis Kyriakou) - 2 CFU

Module 3 - Algorithmic Trading: The Central Order Book; Inventory Control; Optimal Execution; Automation and Network Latency (Roberto Maria Caloi) - 1 CFU
Module 4 – Counterparty Credit Risk: Capital Requirements for Prudential Framework. (Prof. Dario Onorato) - 1 CFU
Please note that CFU (Crediti Formativi Universitari) represents University Educational Credits.

Teaching materials prepared by the instructors are accessible on the course website (www.dir.uniupo.it).

Additionally, there are other recommended books for further reading:

P. Christoffersen, "Elements of Financial Risk Management," Academic Press, 2nd edition.

P. Glasserman, "Monte Carlo Methods in Financial Engineering," Springer-Verlag New York, 2003.

J. Hull, "Options, Futures, and Other Derivatives," 7th edition, 2014.

P. Jorion, "Value at Risk," 3rd edition, Mc-Graw Hill.
J. Hasbrouck, Empirical Market Microstructure: The Institutions, Economics, and Econometrics of Securities Trading, OUP USA, 2007.

These books are currently available in the library. For more details, please consult the D.I.R. page of the course.

At the end of the course, students should have the following capabilities:
Module 1: Model the Profit and Loss distribution of various asset classes (including stocks, bonds, and derivatives). Calculate risk measures. Construct hedging strategies.
Module 2: Simulate stock price paths using key stochastic processes. These processes include arithmetic and geometric Brownian motion, as well as mean-reverting Gaussian processes. Apply Monte Carlo simulation to price equity derivatives.
Module 3: Explore practical examples of algorithmic trading strategies.
Module 4: Evaluate counterparty credit risk and its relationship with regulatory requirements.

To be eligible for the coursework mode exam, successful completion of the following exams is mandatory: Derivatives, Portfolio Theory, Statistics for Finance, and Business Information for Finance. If these exams have not been successfully completed, coursework mode exam participation will not be allowed.
The other examination modes also require the completion of the prerequisite exams prior to the examination date.

This module will primarily employ a combination of lectures and computer lab sessions. The lab sessions on computers will serve to enhance comprehension of the presented concepts and aid in acquiring practical skills related to the discussed algorithms. The lectures will provide insights into methodologies and their real-world applications. Furthermore, supplementary support materials and content will be accessible through Moodle.

Attendance at lecture classes is not mandatory but is highly recommended. Proficiency in Matlab is required. To be eligible for the coursework mode exam, a minimum attendance of 70% of the classes for Module 1 and 90% of the classes for each of Modules 2, 3, and 4 is mandatory.

Students with physical disabilities, Learning Disabilities or Special Education Needs can request specific services and tools via the Staff Sviluppo e Coordinamento Carriere e Servizi alle Studentesse e agli Studenti, consulting the University webpage: https://www.uniupo.it/en/services/servicesstudents-physical-or-learning-disabilities Students with disabilities, learning disabilities or special education needs, once they have contacted the University Staff, can refer to the tutor in charge of the course to define the examination modalities.

For attending students, the opportunity to take the exam through coursework with in-class discussions is provided. The coursework involves implementing the models discussed in class using Matlab and applying them to practical cases, covering topics addressed in different modules. Additionally, weekly assignments must be presented and discussed. Lastly, students are required to replicate and present a paper on the course topics.

For non-attending students or attending students who do not intend to take the exam using the above-mentioned method, the examination procedures are detailed on the course's official pages (DIR). These procedures may include:
a) Written and oral examination, along with the presentation of a Matlab project.
OR
b) Oral examination only, focusing on a limited number of chapters from the relevant reference text, without requiring the use of Matlab. For this second option, the maximum score attainable is 23.
The exam aims to assess students' comprehension and their ability to both apply the basic concepts covered in the course and analyze complex and articulated problems. In particular, students should be able to provide a detailed problem-solving procedure with appropriate theoretical references.

Module 1 – Quantitative Modeling for Market and Credit Risk and Computation of Risk Measures. Credit risk (Prof. Gianluca Fusai) - 6 CFU
This module focuses on quantitative modeling for market and credit risk, including the computation of risk measures such as Value at Risk and Expected Shortfall. It also covers their applications in portfolios containing financial derivatives.
Module 2 – Monte Carlo Simulation (Prof. Ioannis Kyriakou (City St George’s, University of London, Bayes Business School)) - 2 CFU
This module explores Monte Carlo simulation techniques, including simulating random variables, returns, and stock price paths. It demonstrates applications to option pricing and methods for measuring simulation errors. Variance reduction techniques are also discussed, along with case studies in option pricing.
Module 3 - Algorithmic Trading in Financial Markets (Roberto Maria Caloi (Sella Financial Markets)) - 1 CFU
Module 3 delves into algorithmic trading in financial markets. Topics covered include the Central Order Book, the Roll Model of traded prices, inventory control, the dealer problem, statistical arbitrage, optimal execution, automation, and network latency.
Module 4 – Counterparty Risk Prudential Framework (Prof. Diego Onorato (Intesa San Paolo)) – 1 CFU
This module provides an overview of the scope of application of counterparty credit risk, various methodologies for computing capital charges (with a particular focus on internal models), and planned regulatory developments. Topics include SACCR, CVA Charge, Initial Margin, and model risk.

Upon successful completion of this module, you will be expected to have the following abilities and attributes:
Knowledge:
Take positions in the market and understand their associated risks and potential rewards.
Accurately identify complex risk factors and employ appropriate tools for quantifying their impact.
Synthesize a wide range of complex information to conduct precise and realistic risk assessments.
Demonstrate proficiency in the quantitative skills essential for contemporary risk management.
Perform intricate risk assessments with a high degree of accuracy and effectively communicate the outcomes to various levels of management within financial organizations.
Possess a holistic understanding of the balance between risk and reward in the business context.
Recognize the significance of risk management for diverse stakeholders, including traders, corporations, and institutions.
Identify practical challenges in implementing the aforementioned methods and learn how to address or mitigate their effects.
Comprehend the relative strengths and weaknesses of different methodologies.
Develop moderately complex Matlab code, involving one script file and several function files, to implement the methods discussed.
Skills:
Utilize computational and general IT facilities to apply primary numerical methods relevant to risk calculations, derivatives pricing, and algorithmic trading.
Write coherent reports (as part of the module assessment) and effectively communicate results.
Proficiently program using a numerical platform, such as Matlab.
Values and Attitudes:
Embrace the discipline required to employ a structured approach to problem-solving in quantitative finance.
Recognize and appreciate the importance of financial regulation in the industry.