Market Data Challenges for Solvency II · 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0% 50.0% 26/11/2008 02/03/2009 29/05/2009 21/08/2009 16/11/2009 11/02/2010 11/05/2010 05/08/2010 0

© 2010 The Actuarial Profession www.actuaries.org.uk

Life conference and exhibition 2010Adam McIlroy, Markit and Stephen Carlin, Barrie & Hibbert

Market Data Challenges for

Solvency II7-9 November 2010

Agenda

• Why is data important?

• Regulatory and accounting classifications

• Examples of data challenges

• Case Study: Equity Implied Volatilities

1© 2010 The Actuarial Profession www.actuaries.org.uk

Why is Market Data Important?

• Many current and emerging regulatory or reporting regimes use

the ideas of fair or market-consistent value of assets and

liabilities

– Solvency II Technical Provisions, MCEV, IFRS

• The long-term nature of life insurance contracts means that

often the relevant instruments for valuation may not traded in

„liquid‟ markets

– There may be questions over the price reliability of these

assets

• The decision whether-or-not to include these assets can affect

the results of a valuation exercise


Why is price reliability important?


0%

1%

2%

3%

4%

5%

6%

0 10 20 30 40 50 60 70 80

Example: EUR Swap Curve Zero Rates

Cut-off = 50

Cut-off = 30

Cut-off Points

Value of 60-yr fixed cash flow is

20% higher if the 50-year swap

rate is included in the yield curve

construction

Regulatory and Accounting Classifications


Solvency II


Solvency II – Deep, Liquid & Transparent Markets

• “A deep market is a market in which a large number of assets

can be transacted without affecting the price of the financial

instruments used in the replications

• A liquid market is a market where assets can be easily

converted through an act of buying or selling without causing a

significant movement in the price

• A transparent market is a market in which current trade and

price information is readily available to the public”

• Many of the OTC derivates currently used in market-consistent

valuations don‟t satisfy this definition – what impact will this

have?


IFRS Fair Value Hierarchy

• Level 1

– Input is a quoted price in an active market for identical assets

and liabilities

• Level 2

– Input is observable for the asset or liability, either directly or

indirectly

• Level 3

– Input is unobservable


Comparison of Definitions


Level of Liq

uid

ity

Reliable

DLT

Observable

esp. OTC

Markets

Unobservable

IFRS: Level 1 Level 2 Level 3

Board for Actuarial Standards: ‘TAS-D’

• Reliability objective: “users for whom a piece of actuarial

information was created should be able to place a high degree

of reliance on the information’s relevance, transparency of

assumptions, completeness and comprehensibility, including

the communication of any uncertainty inherent in the

information.”

• Documentation

– Data requirements

– Data definitions

– Validation

– Incomplete of inaccurate data


Examples of Data Challenges


Solvency II


Example I: Where are the longest observable maturities for swap markets?

10

0

10

20

30

40

50

HKD

CN

Y

AU

D

JPY

GB

P

EUR

CH

F

USD

CA

D

Bloomberg Max Term

Reuters Max Term

Longest Govt Bond

B&H Swap Survey

QIS 5

Example I: Comments

• QIS 5 view influenced by need for industry to hedge on a large

scale without moving price

– Strong interpretation of DLT and many „reliable‟ prices may

excluded

• Data vendor cut-off determined by number of quotes received

and willingness to supplement with „evaluated‟ prices


Example II: Asian Bond Markets


Example II: Comments

• Evaluation criteria include

– Bid-offer spread

– Issue size

– Number of trades/Volume

– Last trade date

• It can be difficult to source this information – especially for OTC

markets


Case Study: Equity Implied Volatilities


Solvency II


Equity Implied Volatility Data Availability


Traditional Data Sources

• Traditional sources of „independent‟ data are flawed

• Brokers, Exchanges, Data Vendors, Counter Party Valuations

• Do not undertake the rigorous checking and benchmarking

• Danger in accepting a model price

• Multiple vendors and sources are not consistent with the goal of

a single source of Fair Value


• Brokers

– Can be unreliable

– Reported bid-offers can be skewed by market makers

– Not available in illiquid markets

– May be “indicative” only

– Ideally quotes should be sourced from more than one broker

– Ideally quotes should be sourced via a medium that is

available to many traders


• Exchanges

– Excellent source of information if good turnover

– Typically limited to short dated near ATM

– Each exchange may have a different method to settle

contracts

– Last trade may be manipulated

– Settlements can have model based assumptions

– In illiquid markets, settlements can be stale

– Quote size may be small and not relevant for OTC markets


• Model based pricing services

– Based on limited publicly available information

– No benchmark or validation process

– A single unqualified view of the market

– “Black Box” approach

– Automated process and “best fit” curves may be away from

actual prices

– Model assumptions and smoothing techniques may lack

transparency


• Counter party valuations

– Lack of independence

– No benchmark or validation process

– A single view of the market

– What happens if the counter-party disappears?

Pricing Source Contributions


0 5 10 15 20 25 30 35 40

MSCI EAFE

S&P 500 TOTAL RETURN

MSCI WORLD INDEX

RUSSELL 2000

ASX 200

AEX

FTSE 100

NIKKEI 225

EURO STOXX 50

S&P 500

6m FTSE 100 Implied Volatility

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

50.0%

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0

5

10

15

20

25

30

35

No. of Contributors 6m Implied Vol High Low

10yr S&P 500 Implied Volatility

24.00%

26.00%

28.00%

30.00%

32.00%

34.00%

36.00%

38.00%

30/06/2009 12/08/2009 24/09/2009 05/11/2009 11/01/2010 24/02/2010 08/04/2010 20/05/2010 02/07/2010 16/08/2010

0

5

10

15

20

25

30

No. of Contributors S&P 500 10yr Implied Vol low High

Cleaning metrics

• Price based submissions

• Internal consistency checks are performed on implied

vol, forwards, dividend yields and discount factors. Example

metrics are:

– Stale Data Check

– Distance from consensus

– Outlier likelihood

– Curve shape

– Curve continuity

• Additional factors such as number of contributors, market

activity and distribution spread are considered

Conclusions


Solvency II

© 2010 The Actuarial Profession www.actuaries.org.uk25

Conclusions

• Choices made on data selection and usage can have a first-

order effect on valuation results

• Not all prices are equally reliable so it‟s important to understand

how prices are derived

– You need to have visibility of vendor methods and processes

– No one source is 100% reliable

• The prices you use and how you use them will ultimately require

some subjective judgement

• Therefore, it‟s important that the characteristics of data and any

corresponding judgements are documented


Questions or comments?

Expressions of individual views by

members of The Actuarial Profession

and its staff are encouraged.

The views expressed in this presentation

are those of the presenter.


Documents

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