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© 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
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Why is price reliability important?
3© 2010 The Actuarial Profession www.actuaries.org.uk
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
Market Data Challenges for
Solvency II
© 2010 The Actuarial Profession www.actuaries.org.uk
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?
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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
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Comparison of Definitions
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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
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Examples of Data Challenges
Market Data Challenges for
Solvency II
© 2010 The Actuarial Profession www.actuaries.org.uk
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
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Example II: Asian Bond Markets
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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
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Case Study: Equity Implied Volatilities
Market Data Challenges for
Solvency II
© 2010 The Actuarial Profession www.actuaries.org.uk
Equity Implied Volatility Data Availability
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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
Traditional Data Sources
• 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
Traditional Data Sources
• 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
Traditional Data Sources
• 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
Traditional Data Sources
• 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
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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%
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
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
Market Data Challenges for
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
26© 2010 The Actuarial Profession www.actuaries.org.uk
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.
27© 2010 The Actuarial Profession www.actuaries.org.uk
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