Market Data Challenges for Solvency II · 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0% 50.0%...
Transcript of Market Data Challenges for Solvency II · 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0% 50.0%...
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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
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Agenda
• Why is data important?
• Regulatory and accounting classifications
• Examples of data challenges
• Case Study: Equity Implied Volatilities
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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?
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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
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Regulatory and Accounting Classifications
Market Data Challenges for
Solvency II
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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
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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
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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
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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
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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
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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
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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
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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
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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?
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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
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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
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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
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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
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Conclusions
Market Data Challenges for
Solvency II
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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
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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.
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