ESG for ALM Modeling

27 Pages·2012·0.78 MB·English

by  Eric Yau

(cid:7637)(cid:7637)(cid:21399)(cid:15289)(cid:9907)(cid:19558)(cid:19263)(cid:11040)(cid:13995)(cid:18364)(cid:22966)(cid:13995) AIRC Seminar
(cid:11690)(cid:10204)(cid:26704)(cid:26194)(cid:14793)(cid:10035) Market Risk Modeling

(cid:11938)(cid:20246)(cid:20911)(cid:12657)(cid:17616) - An Introduction

Eric Yau
Consultant, Barrie & Hibbert Asia
[email protected]

December 2012
Agenda
+
Introduction to market risk modeling
– What we are trying to do here
– Common applications
– Components of ESG model

+
The devil is in the details (part 1)
– Constructing the initial yield curve
– Liquidity premium
– Mark-to-model in the absence of market prices

+
Workshop!
P.1 2
Introducing market risk
modeling
3
What we are trying to do here
+
What is the fair valuation of embedded financial derivatives on my
liability book?
– Liability is not tradable

+
What are the risk exposures of my net assets, and how should I
measure them?
– Risk exposure is multi-asset, multi-currency, multi-time period

+
What would my balance sheet look like in a probabilistic world? E.g.
what is the chance of having NAV less than X billion?
– Developing a view above the future state (or distribution) of the world is a
subjective matter

+
How should I manage my asset-liability in light of such risks?
– Requires thorough understanding of the risk nature of assets and liabilities on your
book
P.2 4
Model components
Risk Modeling Engine:
Economic Model Model Market Economic Scenario Generator
Assumption Assumption Choice Data
calibration
Economic Scenario Generator
Economic scenarios:
Base and Sensitivities

Projection Engine:
ALM System
Liability Asset
ALM System
Portfolio Portfolio 100
Millions 80
HKD 60
40
20
Generate analysis for both asset 0
1 3 5 7 9 1113151719212325272931333537394143454749
and liability portfolios: -20
-40
* Valuation -60
* Risk / Capital measures -80
-100
* Mismatch position
-120
5
What is ESG? – Monte Carlo Simulation
+ Produce economically coherent joint distributions of financial and economic
factors.
+ Generated using sophisticated models that capture the dynamics of financial
markets – dependency, tail risk etc.
Example output for interest rates
Typical variables being modeled -
+ Interest rates
+ Inflation
+ Credit
+ Equity
+ Alternative investment
+ Option implied volatility
+ FX
P.3 6
Common applications
7
Economic balance sheet
+
Mark-to-market (or mark-to-model) for both assets and liabilities
Economic Balance Sheet
A

MCEV

Adjustment
L

Market
value
Market-
consistent
value

Assets Liabilities
+
A better reflection of the true economics of the firm
+
MVL / MCEV calculation typically requires stochastic projection
– Liability = complex non-linear function of multiple risk factors
– Options and guarantees require stochastic quantification
P.4 8
Stochastic vs deterministic
+
We live in a probabilistic world
+
Example: interest rate
Guaranteed rate Pricing / valuation interest rate (deterministic)
Consideration

+ distributional assumption
+ parameters (e.g. volatility)
+ multi-asset
Expected
+ multi-time period
Profit
+ etc
Actual interest rate (random)
How should we price in such an event?
9
Liability valuation / pricing

+
ESG model selection and calibration has to be appropriate for
liabilities
– Model choice - simple vs complex
– Expert judgement must be prudent, reliable and justifiable

+
Using a richer model will make this easy to deliver in practice…

P.5 10
Equity volatility – simple models
ESG Generated IV Surface from a
Market Implied Volatility Surface simple model (TVDV)
40% 40%
35% 35%
30% 30%
V V
I 25% 35%-40% I 25% 35%-40%
20% 30%-35% 20% 30%-35%
15% 25%-30% 15% 25%-30%
6 6
0. 8 20%-25% 0. 8 20%-25%
0. 1 15%-20% 0. 0 15%-20%
1.
Strike 1.2 3 5 Matur10 ity Strike 1.2 3 5 10
1 1.4 1 1.4 Maturity
+
Simple model only appropriate for limited range of liability valuations
– eg all ATM
+
If not all, need to segment book…
– Dependencies between policies ?
+
… or hard-to-justify ‘averaging’ assumption?
11
Equity volatility – richer model
ESG Generated IV Surface from
Market Implied Volatility Surface sophisticated model (SVJD)
40% 40%
35% 35%
V 30% V 30%
I 25% 35%-40% I 25% 35%-40%
20% 30%-35% 20% 30%-35%
15% 0.6 0.8 1 221505%%%---322050%%% 15% 0.6 0.8 1 221505%%%---322050%%%
Strike 1.2 1 1.4 3 5 Matur10 ity Strike 1.2 1 1.4 3 5 Matur10 ity
+
Richer model provides simpler, easier to justify solution
P.6 12
ESG Economy Model Structure
Alternative Asset Returns (eg Corporate Bond
Equity Returns Property Returns Excess
commodities) Returns
returns
(if any)
Credit risk model
Real-economy; GDP
Initial swap and
and real wages
government nominal Nominal short rate
bonds
Nominal minus Exchange rate
real is inflation (PPP or Interest
expectations rate parity)
Index linked
Real short rate
government bonds
Realised Inflation and
Foreign nominal
“alternative” inflation
short rate and
rates (i.e Medical)
inflation
Macro economic
+ variables
Joint distribution
– Correlation assumptions ensure plausible economic relationship across asset classes

13
Risk factor distribution
+
Use joint distribution of financial risk factors to deduce income
statement and balance sheet distribution
Example: Credit/Equity risk
+
E.g. asset price falls
should be associated
with negative credit shocks

P.7 14
Global equity modeling example
“Global” Risk Risk Risk Risk Risk
Risk Factor Factor Factor Factor Factor
Factor 1 2 3 4 5 6

HK US Japan
Equities Equities Equities

+
Indices have exposure in different risk factors
+
Volatility of equities moves up and down together
– When vol goes up, correlation goes up too

15
Sources of risk…
Interest rate & Equity Alternative
Interaction of risk factors
inflation risk risk investments
90 6
80
5 m)
m) 70 £
aR (£ 60 4 mium (
V
s re
u 50 P
rpl 3 sk
Su 40 Ri
e in 30 2 cted
g e
n p
a x
h 20 E
C
1
10
0 0
Nominal Int. Rates Real Int. Rates Experienced Inflation Credit Domestic Equities OverseasEquities SourPropertyces of RAlternativesisk Currency Active Risk Mortality Total diversification All Risks
P.8 16
Applications
+
Specific consideration to applications
What it is for Consideration Calibration
(1) Liability valuation / pricing Estimate fair value of (cid:2) Valuation of options / (cid:3)MC
liability guarantees
(2) Economic capital Assess level of resources (cid:2) Tail risk modeling (cid:3)MC
required to withstand (cid:3)RW
adverse scenarios
(3) Risk factor distribution Understand impact of (cid:2) Relationship between (cid:3)RW
market movements on each risk factor
company financials
(4) Strategic asset allocation Determine optimal asset (cid:2) Realistic assumption / (cid:3)RW
strategies based on distribution
asset/liability portfolio (cid:2) Alignment with risk
metrics
17
Components of ESG model
P.9 18
Core components of ESG
+
Mathematical modelling: ESG Software
– Research, develop, and maintain state of the art mathematical models
– Deliver these in an efficient, flexible, and user friendly format

+
Financial economic expertise and research: Model Calibration
– Market Consistent: Set up models to replicate observable market prices
– Real World: Set up models so that they produce realistic asset return behaviour

+
Documentation
– Communications and validation of results are important too!

19
Typical ESG modeling process
Calibration Content ESG - Calculation Engine Output scenarios
Mathematical
models
Generate
model
parameters
ESG/WSG
specific to
£65m
application and
market
Market or conditions (cid:4)r (cid:7)(cid:9)(cid:2)m(cid:6)r(t)(cid:3)(cid:4)t(cid:5)(cid:8)(cid:4)Z (t)
1 1 1
historical
A series of mathematical models
data
implemented in software
(cid:4)r (cid:7)(cid:9)(cid:2)m(cid:6)r(t)(cid:3)(cid:4)t(cid:5)(cid:8)(cid:4)Z (t)
1 1 1
Documentation covering the whole process
P.10 20