FIN30270 Advanced Risk Management

Academic Year 2022/2023

The module comprises three parts.
The first part will consist of reviewing the basic concepts of Financial and Investment Theory as well as making the connection of the main results to Constrained Optimisation, as applied algorithmically via the JuMP (Mathematical Programming for Julia) package of the (MATLAB-like) programming language Julia. Additionally, an introduction to coupling methods for random association (including Copulas) will be included.

The second part of the module will extend these methods to the Multi-Period (Dynamic) setting, utilising such methods as Dynamic Programming and Monte Carlo (Simulation) -based methods, Markov Decision Processes (MDP), and Stochastic Optimisation, also using Julia/JuMP

The third and final part will involve one or more Case Studies applying the methods of the first two sections to tackle such problems as Risk of Default from Credit Risk data and Dynamic Asset Allocation.

It is assumed that students have an understanding of basic portfolio theory, are familiar with the main types of securities and financial derivatives, and have been previously exposed to basic concepts in risk modelling, especially value at risk (VaR), as well as some experience in coding, allowing this course to concentrate on the application of advanced techniques and methodologies for risk management and measurement.
A core component of the course is the use of coding in the Julia programming in order to apply these techniques and methodologies in practice. [No previous knowledge of Julia is assumed.]
The course will be delivered through a mix of formal frontal lectures and tutorials, complemented by workshops and guest lecturer seminars.

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Curricular information is subject to change

Learning Outcomes:

On completing this module students will
• be able to formulate a basic Financial Decisionmaking and/or Risk Management problem mathematically as a Constrained Optimisation problem and deploy a standard solver to obtain a solution

• know how to use derivative instruments (including forward contracts, futures contracts, call option contracts, put option contracts, interest rate swaps, currency swaps, call futures options, put futures options, credit default swaps, total return swaps, asset backed securities, and collateralised debt obligations) to take and manage risk

• be able to use Latent Variable and Computational methods to detect heightened levels of Portfolio Risk (including Credit Risk)

• understand the principles and approaches to managing risk in a multi-period (dynamic) setting

Indicative Module Content:

Financial Theory and Corporate Policy: Pearson New International Edition, 4/E
Thomas E. Copeland, J. Fred Weston, Kuldeep Shastri, (CWS) (ISBN: 9781292021584)
(review and selected content only)

A Gentle introduction to Optimization

Introduction to Julia

[Introduction to Stochastic Optimisation - TBA]

Student Effort Hours: 
Student Effort Type Hours




Autonomous Student Learning




Approaches to Teaching and Learning:
Lectures (some by lecturer, some from external sources), notes and textbooks

Homework and associated Tutorials, involving Theory and Computation

Group Projects 
Requirements, Exclusions and Recommendations

Not applicable to this module.

Module Requisites and Incompatibles
FIN30090 - Treasury and Risk Management

Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Examination: Final Exam 2 hour End of Trimester Exam No Graded No


Group Project: Presentation - Value at Risk Unspecified n/a Graded No


Group Project: Risk Management Unspecified n/a Graded No


Group Project: Market and/or Credit Risk Modelling Unspecified n/a Graded No


Carry forward of passed components
Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Online automated feedback

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Mr Shivam Agarwal Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Tutorial Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 14:00 - 14:50
Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 09:00 - 10:50