FIN30270 Advanced Risk Management

Academic Year 2022/2023

Broadly speaking, this module is an introduction to some of the more advanced methodological techniques and issues required for Quantitative Risk Management, both for Individual and Institutional Decisionmaking.
Additionally, a qualitative introduction to the 'Landscape' of Financial Risk Management from an Institutional perspective will be presented

Among the specific methodological topics to be covered [tentatively] will be


-Optimal Long-term Investment Growth in a Multi-Period setting and the Information Theory ('Kelly') connection

-Mathematical Programming for Risk Management; Constrained optimisation (including Mean-Variance optimisation)

-Arrow-Debreu Securities and Model-Free Derivatives Pricing

-Risk Assessment - Value at Risk (VaR) vs. C-VaR and other 'Coherent' risk measures, both parametric and Monte Carlo/Bootstrapping

-Methods for Credit/Debit Value Adjustment (CVA & DVA)

-Models for representing Statistical Association; Gaussian Copulae (plus associated pitfalls).

-General Multi-Period Financial Optimisation and Risk Management - Intro to Dynamic and Stochastic Programming

-Machine Learning models for Risk Management (Time Permitting)



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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

• Be able to compute (and simulate via Monte Carlo) Value at Risk, Expected Shortfall, and Credit Value Adjustment (CVA/XVA)

• know how to use derivative instruments to manage risk

• be able to use Latency and other 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:

In addition to a general overview of the qualitative issues encountered in Financial Risk management, the following
specific topics will be among those to be covered and emphasised.

-Optimal Long-term Investment Growth in a Multi-Period setting and the Information Theory ('Kelly') connection

-Mathematical Programming for Risk Management; Constrained optimisation (including Mean-Variance analysis)

-Arrow-Debreu Securities and Model-Free Derivatives Pricing

-Risk Assessment - Value at Risk (VaR) vs. C-VaR and other 'Coherent' risk measures, both parametric and Monte Carlo/Bootstrapping

-Methods for Credit/Debit Value Adjustment (CVA & DVA)

-Models for representing Statistical Association; Gaussian Copulae (plus associated pitfalls).

-General Multi-Period Financial Optimisation and Risk Management - Dynamic and Stochastic Programming

-Machine Learning models for Risk Management (Time Permitting)

Student Effort Hours: 
Student Effort Type Hours
Lectures

24
Tutorial

12
Autonomous Student Learning

90
Total

126
Approaches to Teaching and Learning:
Lectures (some by lecturer, some from external sources), notes and textbooks

Individual Homework supported by Tutorials (tentatively), involving Theory and Computation.
Students will be required to submit work in the Jupyter Notebook (.ipynb) format

Group Homework and Projects to be uploaded on Brightspace 
Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Incompatibles:
FIN30090 - Treasury and Risk Management


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Class Test: Optional - Near Middle of term (TBA)
May be replaced by marks from Final Exam		 Unspecified n/a Alternative linear conversion grade scale 40% No

20
Examination: Final Exam 2 hour End of Trimester Exam No Alternative linear conversion grade scale 40% No

40
Group Project: 3 Parts - TBA
Will include an 'Individual Contribution' component		 Unspecified n/a Alternative linear conversion grade scale 40% No

40

Carry forward of passed components
No
 
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?

All Continuous Assessment will be Group-Based according to the following steps: 1. Each member of the group submits online a draft of required work or individual contribution to group work (time schedule provided, prior to due date) 2. Groups complete required Assignment or Task 3. Group Submission Members of a Group shall all receive the same grade, except in cases where one or more members' early drafts are consistently lacking relative to the others'. Feedback will be online (with comments and explanation where needed) Final Examination will be assessed with online automated and partially individualised feedback.

Risk Management and Financial Institutions (5th ed.) by John Hull (Wiley 2018)
[https://worldcat.org/title/1011550876] (Required, selected chapters only)

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

A Gentle introduction to Optimization (recommended)
https://towardsdatascience.com/a-gentle-introduction-to-optimization-f95938ce475e

Introduction to Julia (recommended)
http://avinashu.com/tutorial/indexjulia.html

[Introduction to Stochastic Optimisation - TBA]



Name Role
Dr Conall O'Sullivan Lecturer / Co-Lecturer
Mr Shivam Agarwal Tutor
Xiaomeng Wang Tutor