Tim Higgins

S2, 2015

Tim Higgins

Enterprise Risk Management 2 - WEEK 1

Learning Objectives

Upon successful completion of the requirements for this course, students will be able to:

1 Describe the properties and limitations of common risk measures. 2 Describe how models can be used in the ERM decision-making process.

3 Demonstrate an understanding of different quantitative techniques for modelling and measuring risk including:

Risk aggregation methods

Statistical distributions

Copulas

Time series techniques

Extreme value theory

4

5

Recommend appropriate models for risk assessment based on quantitative and qualitative analysis.

Assess different types of risk within an organisation including: market risk, credit risk, operational and other risks.

Tim Higgins

Enterprise Risk Management 2 - WEEK 1

Course Schedule

Week

1

2

3

4

5

6

7

8

9

10

11

12

13

Syllabus

Introduction

to the Course

Risk Measurement

Risk Modelling - Part 1

A review of statistical distributions

Financial time series - Part 1

Financial time series - Part 2

Copulas - Part 1

Copulas - Part 2

Model fitting techniques

Extreme value theory

Risk

Modelling

Part

2

Analysing Market Risk - Part 1

Analysing Market Risk - Part 2

Analysing Credit Risk - Part 1

Analysing Credit Risk - Part 2

Analysing Operational Risk and Other

Risks

Revision

Tim Higgins

Reading

ActEd Ch.10

ActEd

ActEd

ActEd

ActEd

Ch.11

Ch.12

Ch.13

Ch.13 and 14

ActEd

ActEd

ActEd

ActEd

Ch.14

Ch.15

Ch.16

Ch.17

ActEd Ch.18 and 19

ActEd Ch.19

ActEd Ch.20 and 21

Enterprise Risk Management 2 - WEEK 1

Course Assessment

Assessment piece

Mid-Semester Exam

Assignment

Final Exam

Value

20%

20%

60%

Due date

Week 8

Week 12

TBA

All assessment is compulsory and non-redeemable.

Tim Higgins

Enterprise Risk Management 2 - WEEK 1

Risk Measurement

Tim Higgins

Enterprise Risk Management 2 - WEEK 1

Week 1: Objectives and readings

Describe the properties and limitations of common risk measures, including Value at Risk (VaR)

Tail Value at Risk (TVaR)

Probability of ruin

Expected shortfal

Describe how to choose a suitable time horizon and risk discount rate Readings:

ActEd ST9: Chapter 10

Sweeting: Ch1, section 1.6

Sweeting: Ch13, section 13.6

Sweeting: Ch15, section 15.4

Tim Higgins

Enterprise Risk Management 2 - WEEK 1

Properties of Risk Measures

A risk measure is an operation that assigns a value to a risk

Coherence:

Monotonicity

If L1 ≤ L2 then F (L1 ) ≤ F (L2 )

Sub-additivity

F (L1 + L2 ) ≤ F (L1 ) + F (L2 )

Positive homogeneity

F (kL) = kF (L)

Translation invariance

F (L + k) = F (L) + k

Convexity:

F (λLx + (1 − λ)Ly ) ≤ λF (Lx ) + (1 − λ)F (Ly )

Tim Higgins

Enterprise Risk Management 2 - WEEK 1

Deterministic approaches

Notional approach (e.g., risk weightings)

Factor sensitivity approach

Scenario sensitivity approach

Tim Higgins

Enterprise Risk Management 2 - WEEK 1

Deterministic approaches: Notional approach example

Apply multiples to assets or liabilities to allow for uncertainty in their values.

Example:

APRA General Insurance solvency and capital adequacy standards

Insurance risk charge

Net insurance liabilities calculated to be sufficient with 75% probability. What about the other 25%?

Risk charge applied to outstanding claim and premium liability estimates Risk charge is a percentage that depends on class of liability

(based on uncertainty)

Link to GPS 115

Tim Higgins

Enterprise Risk Management 2 - WEEK 1

Deterministic approaches: Factor sensitivity approach

Calculate impact on assets and liabilities of a change to a single underlying risk factor.

Example:

APRA General Insurance solvency and capital adequacy standards

Asset risk charge

Old solvency standards (prior to 2013) used a notional approach (investment capital factors). Problems: overly simplistic; doesn’t address asset/liability mismatch

New solvency standards use a factor sensitivity approach

Link to GPS 114

Factor approach can be criticised because risk factors are only