ULMS Electronic Module Catalogue
The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title Quantitative Modelling of Risk
Code ECON927
Coordinator Professor OT Henry
Finance and Accounting
Olan.Henry@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2021-22 Level 7 FHEQ First Semester 15

Pre-requisites before taking this module (other modules and/or general educational/academic requirements):
 

Modules for which this module is a pre-requisite:
 

Programme(s) (including Year of Study) to which this module is available on a required basis:
 

Programme(s) (including Year of Study) to which this module is available on an optional basis:
 

Teaching Schedule
  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 24

 5

         29
Timetable (if known)              
Private Study 121
TOTAL HOURS 150

Assessment
EXAM Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Examination There is a resit opportunity. Standard UoL penalty applies for late submission. This is an anonymous assessment. Assessment Schedule (When) :1  2 hours    80       
CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Mid-term test There is a resit opportunity. Standard UoL penalty applies for late submission. This is an anonymous assessment. Assessment Schedule (When) :1  1 hour    20       

Aims

To equip students with the quantitative and statistical tools that are important for risk management;

To cover state-of-the-art quantitative techniques and highlight their practical relevance for risk management;

To expose students to the application of these tools and the key properties of financial data through a set of computer-based workshops and exercises;

To cover a number of important topics, including numerical methods, regression analysis, matrix algebra, factor modelling and volatility modelling.

Learning Outcomes

(LO1) Display a solid understanding of advanced numerical methods used in the risk management industry;

(LO2) Estimate regression models and understand their implications for hedge ratios;

(LO3) Identify and model the risk factors present in a portfolio of assets;

(LO4) Critically evaluate different volatility forecasting models and assess their empirical performance;

(LO5) Devise risk management strategies for option prices.

(S1) Communication skills

(S2) Organisation skills

(S3) IT skills

(S4) Teamwork

(S5) Commercial awareness

(S6) Lifelong learning

(S7) Ethical awareness

Teaching and Learning Strategies

Teaching Method 1 - Lecture
Description:
Attendance Recorded: Not yet decided

Teaching Method 2 - Seminar
Description:
Attendance Recorded: Not yet decided

Self-Directed Learning Description: Independent Study

Syllabus
 

Introduction:
Probability and random variables;
Definition of moments;
Integration and differentiation.

Numerical Methods:
Taylor expansion;
Interpolation methods;
Optimization.

Regression Analysis:
Ordinary least squares;
Hypothesis testing and model diagnostic;
Applications for risk management, eg hedge ratios.

Risk management in a multi-asset world:
Review of matrix algebra;
Portfolio mathematics;
Modelling the dynamics of a portfolio: economic and statistical approaches.

Volatility models:
Time-series models: EWMA, GARCH, historical volatility models;
Market implied model: option implied volatility;
Assessing volatility forecasts: statistical and economic loss functions.

Derivatives markets:
Futures, forwards and options;
Option risk factors and the Greeks;
Hedging in derivatives markets.

Recommended Texts
Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.