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© The Z/Yen Group of Companies 2008
| |
Professor Michael Mainelli
Executive Chairman, Z/Yen Group Limited
[An edited version of this article first
appeared as "Derivative Processing Counts", Journal of Risk Finance, The
Michael Mainelli Column, Volume 9, Number 1, pages 92-95, Emerald Group
Publishing Limited (January 2008)]
Present Processing Imperfect
The global ‘credit crunch’ of 2007 has been
widely reported and discussed. Something less discussed is a ‘dog that
didn’t bark’ story – the absence of credit market operational failure.
Operational failures accompany many financial crises, for instance, Barings,
Daiwa, Enron, Long Term Capital Management and Kidder Peabody came to light
during periods of wider financial market problems. Market stresses tend to
crack operational nuts. So where is the accompanying credit market
operational disaster?
At first glance, while there have been worries, and it has been neither smooth
nor easy, things are under control. During the first half of the decade,
unprecedented growth of the credit markets led to concerns about the security of
settlement, particularly with a growing backlog of “unprocessed” credit default
swap (CDS) contracts. Credit market backlog is typically measured by the
number of outstanding confirmations exceeding 30 days. During 2005
outstanding CDS confirmations exceeding 30 days were nearly 100,000 while deals
being made every month were just under 150,000. In 2005 the Federal
Reserve obtained commitment from 14 major dealers to upgrade their systems and
reduce the backlog. In January 2006 the dealers claimed that they had met
their commitment and achieved a 54% reduction in outstanding confirmations
exceeding 30 days. From January 2006 to February 2007 the dealers did even
better, such that confirmations exceeding 30 days were down to less than 10,000
in autumn/winter 2006/2007, with monthly trading volumes exceeding 150,000.
But in 2007 volumes rose as concerns over credit markets led to increased
trading, from around 150,000 deals per month to over 400,000. As volumes
rose so did the backlog, to just under 30,000 in July 2007. One can note
that the backlog is fairly steady as a proportion of monthly trading at around
7%. Perhaps this is not so bad as the derivatives processing factories are
handling a significant, and unexpected, volume spike in a linear way.
However, 30,000 outstanding confirmations over 30 days can hide a lot of
surprises.
Looking to the future, we can expect to see volumes increase even further while
average deal size and fees decline. Volumes will rise as global markets
incorporate the growing economies of China, India, Russia and Brasil. New
products in commodities, carbon, weather, insurance and betting will increase
derivative product ranges and complexity. The front office has really only
just started to explore the frontiers of innovation, while the back office
remains Dickensian. The cost of processing derivatives will rise as a
proportion of profit, and margins will decline. Of course, if the industry
can’t sort out credit derivative processing, then regulatory interest will grow
not just in the role of credit rating agencies in the recent credit crunch, or
threats to Basel 2 as regulators ponder the now-less-attractive relationships
among ratings, models and capital requirements. One can expect regulators
to set more demanding leverage, margin and settlement requirements.
Something has to give.
What We Seem To Have Here Is A Failure To Learn
Further, the increases in trade volume and changes in cost-per-trade since 2004
question the scalability of OTC derivatives processing. 27% of interest
rate derivative processing cost is in manual confirmation processing, 24% in
equity derivatives, 19% in credit derivatives. Only 10% of the
cost-per-trade is in “adding value”, i.e. helping customers with valuation,
collateral management or other relationship matters. While average
derivatives trade volume per investment bank has trebled, from roughly 30,000
trades per year to 90,000 trades per year, the average cost-per-trade has only
moved from $250 to around $200. This implies little scalability, i.e. in a
highly efficient operation the cost-per-trade should have tumbled to around $90,
but is impeded in some way. Or people aren’t learning.
The learning curve in derivatives processing is steep, and major players are not
climbing it fast enough. A good rule of thumb is that you shouldn’t trade
what you can’t settle. ‘Buy-side’ client satisfaction is only
‘satisfactory’ to ‘good’, virtually never excellent. Z/Yen Group
calculates an OpRisk Safety Estimator. The OpRisk Safety Estimator is the
R2 (R-squared) value for the ‘economy of scale’ curve, a logarithmic line of
best-fit for cost-per-trade versus transaction volume – basically, “how tightly
do industry participants fit an economy of scale curve for a product”. The
Estimator is derived from operations costs only, trying to represent the
headcount costs for the core trade processing lifecycle which generally (each
product has obvious differences) includes pre-settlement/matching/static data,
confirmations processing, settlement, customer relationship management,
management & administration. The OpRisk Safety Estimator excludes IT costs
as the lifecycle apportionment would distort the figures. The table below
sets out the 2004 and 2005 (last year with results) figures, where 1.00 is very
safe and 0.00 indicates high OpRisk.
OpRisk Safety Estimators for Selected Products,
2004 and 2005
|
Product
(1=safe, 0 = risky) |
OpRisk Safety Estimator
2004 |
OpRisk Safety Estimator
2005 |
Improvement |
|
European Cash
Equities |
0.90 |
0.98 |
8% |
|
European Repo |
0.89 |
0.89 |
0% |
|
European Bonds |
0.84 |
0.87 |
3% |
|
US Cash Equities |
0.43 |
0.66 |
23% |
|
Global FX |
0.22 |
0.46 |
24% |
|
Global Currency
Options |
0.83 |
0.38 |
-45% |
|
European Stock
Lending |
0.73 |
0.34 |
-39% |
|
Global Credit
Derivatives |
0.68 |
0.31 |
-37% |
|
Global IR
Derivatives |
0.32 |
0.10 |
-22% |
|
Global Equity
Derivatives |
0.48 |
0.06 |
-42% |
[Source: Z/Yen Group Limited]
It is worrying to see that while equities and FX products are improving, in some
cases dramatically, all derivatives products have been getting worse. Yes,
volumes have increased, but over the same period US cash equity volumes rocketed
and simultaneously posted a 23% improvement to their OpRisk Safety Estimator.
One might expect significant improvement to derivative OpRisk figures given all
the 2006 and 2007 activity, but one can’t be complacent. While there have
clearly been significant improvements in derivatives processing, other traded
markets have improved more.
Some participants look to better management via techniques such as Six Sigma
programmes (3.4 defects per million operations). As deployed in comparable
industries such as mobile telephony or airlines or credit card processing, Five
Sigma confidence levels (320 defects per million operations) are approached.
Derivatives processing rarely gets close to Three Sigma (66,800 defects per
million). Improvement requires fundamental change, not slightly better
management.
Bad, Bad Data
But why is there backlog at all? Sure, OTC derivatives markets are difficult to
confirm and settle; there are no exchanges; special terms and conditions
proliferate. The fundamental cause of most confirmation problems is bad
data. ‘Bad data’ is inaccurate, incomplete, late or inaccessible data that
causes matching or processing problems. A common problem is providing
inaccurate counterparty entries, e.g. XYZ Asset Management Bermuda instead of
XYZ Asset Management Bahamas. Bad data also encompasses redundant data
that conflicts with other data fields. And things are getting worse.
Data to compute fee calculations is now part of settling some products.
Fee field computational data can raise timing issues, such as do we both agree
on which LIBOR moment applies to the fee? Or model issues, such as do we
calculate moving averages in the same way? These computations lead to more
confirmation and settlement problems.
Bad data leads to significant costs. There are the direct costs of
mistakes and interest. Clients are dissatisfied with service.
Operational risks are higher than they could be, in capital charges as well as
losses. Venue and execution decisions are suboptimal. Poor
investment decisions are made. Liquidity is lower than it could be.
Finally, concerns over processing leading to more regulatory oversight and more
cost. Moreover, there is an enormous opportunity cost - markets are
smaller than they might otherwise be.
What can be done about bad data? A common recommendation to most process
problems is ‘simplify, automate, integrate’. The focus on “master confirmation
agreements” has helped to simplify things by focusing on a more limited number
of items for matching and agreement. Automation has helped, but
significant pockets of chaos remain, and automating chaos is not a solution.
One significant pocket is simple counterparty identification. The static
master data on counterparties needs a ‘single ID’. Some suggestions include
voice identification to confirm the counterparty, or matched ID pairs for deals
in addition to separate entry of each counterparty. Integration is helped
by things such as DTCC’s Trade Information Warehouse, but there is a limit to
integration in a peer-to-peer market without an exchange. And OTC is, by
definition, hard to turn into an exchange.
Derivatives Processing Superhighway
Two routes to improvement do stand out – more informed use of technology and
client involvement in reducing client input errors. There has been a lot
of talk about new technology in derivatives processing – componentization, low
latency, data management and customer-centric systems. Less common is talk
of dynamic anomaly & pattern response – systems for eliminating data entry
errors and spotting anomalies before they cause problems. There is a
crying need for adaptive and evolving computer systems that can handle
situations too complicated for rules, such as processing partially complete
derivatives. If derivative markets are to grow as many observers expect,
if dark pools are to provide many more trading opportunities, if algorithmic
trading continues to grow, then back office systems will need to become vastly
more sophisticated or they will prevent market growth. Back offices need
to adopt front office techniques, moving from top-down, rule-based systems where
humans spend too much time processing exceptions, to dynamic and adaptive
systems where humans process fewer exceptions because machines made some
sensible decisions.
Clients are the source of most bad data problems, as well as the victims.
Client selection is already apparent – buy-side clients that produce processing
problems are less welcome or given disadvantageous rates. Clients could be
given better data entry tools that reduce errors. In addition to more
informed use of technology and client involvement, one can expect to see some
market solutions. One creative market solution could be data ‘insurance’;
some party provides a central data registry for a cost, but the cost includes
indemnity and fines when the registry is in error. Another might be
structuring fees against successful settlement. Yet another might be the
sell-side moving more into full service for the buy-side and taking on some of
the operational risk and cost. MiFID and RegNMS already indicate that the
sell-side might provide more compliance and routing services to buy-side
clients, why not clearer responsibility for settlement risk?
Janet Wynn of DTCC points out that the financial services industry has built
some great derivative processing highways but left many problems in the on-ramps
and off-ramps. Perhaps there need to be better standards for licensing
firms and drivers of derivatives, audited processing standards such as those
used for connecting to the credit card networks. From bad credit rating
data to bad deal entry data, the industry hurts. It’s in the industry’s
own interest to fix derivatives processing. Bad data costs.
Further Reading
Michael Mainelli, "Toward
a Prime Metric: Operational Risk Measurement and Activity-Based Costing"
,
Operational Risk (A Special Edition of The RMA Journal),
pages 34-40, The Risk Management Association (May 2004).
A good source for statistics on derivatives
processing is Markit Metrics, where the industry releases aggregate metrics to
the public -
http://www.markit.com/information/products/metrics.html.
Thanks
My thanks to my colleague of many years, Jeremy
Smith, Head of Z/Yen Limited, now part of McLagan Partners, for providing
insight and information. I would also like to thank the Journal of
Financial Transformation and Capco for hosting an evening discussion in
Amsterdam on 10 October 2007 with Janet Wynn and Adriaan Hendrikse, hosted by
Michael Enthoven, that helped me develop my thinking.
|
Professor Michael Mainelli, PhD
FCCA FSI, originally undertook aerospace and computing research,
followed by seven years as a partner in a large international
accountancy practice before a spell as Corporate Development Director of
Europe’s largest R&D organisation, the UK’s Defence Evaluation and
Research Agency, and becoming a director of Z/Yen (Michael_Mainelli@zyen.com).
Michael is Mercers’ School Memorial Professor of Commerce at Gresham
College (www.gresham.ac.uk) |
|
Z/Yen operates as a commercial
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such as developing an award-winning risk/reward prediction engine,
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