IMF-FSB Users Conference
WANTED datasets during the
financial markets turmoil
- Securities and Derivatives Yuko Kawai, Bank of Japan
8 July, 2009
Agenda

Market observations during the turmoil

What we wanted to know

Then-available datasets and what we had missed
2
Part I
Market observations during the
market turmoil
Before the crisis

“Shadow Banking” sector risk has loomed up
Banks
$
$$$$
$$$
$$
Final Borrowers
Final Investors
Brokers
“Shadow
Banking”
$$
$$$$
Funds
$$
4
Before the crisis

We did not have the full knowledge about “them”
What are they? How
Banks
LARGE are they? What
share of money
$$$$ flow
between final investors
(and who are they?) and$$
Final Borrowersfinal borrowers isBrokers
intermediated by this
sector?
$
$$$
Final Investors
“Shadow
Banking”
$$
$$$$
Funds
$$
5
During the crisis

Money flows dwindled, loss/defaults surged everywhere
Banks
Loss
$
Defaults
Final Borrowers
$$
Defaults
Brokers
Final Investors
Defaults
“Shadow
Banking”
$
Funds
$
Defaults
6
During the crisis

Through market data,
we tried to identify….
Banks
How resilient are
they? How are the
financing/capital
raising conditions?
Loss
$
Defaults
Final Borrowers
How levered were
they? By what
instrument?
$$
Defaults
Brokers
How much is the
current/maximum
loss?
$
Final Investors
Defaults
“Shadow
Banking”
Funds
Defaults
Who holds what
risk? How
much?
$
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Other considerations

Cross-border money flow/arbitrage

Heightened correlation of price movements among
different asset classes and market locations

Maturity mismatch (short-term financing vs. long-term
asset holding)

Hyper-leverage through re-securitization

Re-intermediation of risk upon the drawdown of liquidity
facility provided by banks to off-balance sheet balance
sheets
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Part II
What we wanted to know
1. Magnitude of “De-leveraging”

Detect which particular market/product suffers
dysfunction.
✓ Check the price movements.

Estimate the current and possible maximum influence for
final borrowers and investors of troubled products.
✓ Check the composition of investors and ultimate borrowers.

Evaluate the systemic implication.
✓ Check the transaction volume <issuance, secondary>/outstanding
amounts of such market/products.
10
Spillover of risks / Recovery progress

Identify the risk transmission mechanism from the
troubled ones to other markets/products.
✓ Check who the cross-over dealers/investors are.
✓ Find the cross-market trading strategies.

Evaluate the impact of the central bank’s unconventional
methods in the money market operation over the
concerned products/market.

Estimate the level or risk appetite, availability of money
liquidity.
✓ Check the concerned market conditions (price, transaction
volume, volatility and dispersion of the prices).
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Implications to financial stability and the real economy

Estimate the impacts of turmoil on major bank/brokers’
capital.
✓ Check the outstanding volume and potential loss exposure of
troubled bank assets.
✓ Check the market conditions for bank equity issuance.

Analyze the recovery of borrowings by
household/corporate sector.
✓ Check the issuance volume and price of various bonds and loans.
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Part III
Then-available datasets and
what we had missed
Examples of “flow” products
Price
Flow
Stock
Buyers and
Sellers
Risk
holders
Listed
securities
Exchange
Exchange
Exchange
(Exchange)
(Exchange,
FoF)
Corporate
Bond
Vendor,
Broker
(clearing?)
Vendor,
FoF
N/A
(FoF)
CDS
Vendor,
Broker
DTCC
ISDA, BIS
rough
estimate by
BBA
N/A
GSE
securities
Vendor,
Broker
?
Vendor,
FoF, Issuer
N/A
FoF
“vendor” includes news/data vendors such as Bloomberg, MarkIt, LipperTass, Datastream, Dealogic
( ) means “not always available, depending on the product”
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“Shadow Banking” products – tough ones
RMBS
Price
Flow
Stock
Buyers and
Sellers
Risk
holders
(Broker)
Primary (Vendor,
Broker)
(Rating
Agency,
Vendor)
N/A
N/A
N/A
N/A
CMBS
Secondary
– N/A
SIV
N/A
(Broker)
(Rating
Agency)
N/A
N/A
ABCP
Fed
N/A
Fed
N/A
N/A
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“shadow banking” products, additional data required


For first-level securitization: Private RMBS, CLO, CMBS,
ABS …

Outstanding amounts by underlying loan quality (subprime, Alt-A,
Jumbo), or by rating

Rating transition
Second-level securitization: ABCP conduits, SIV, ARS,…

Outstanding amounts by underlying asset class

Liquidity (commitment line) providers, level of drawdown, trigger of
drawdown
16
Market Liquidity – difficult to identify

Proxy by Offer-bid, on- vs. off-the-run spreads,
transaction volume, volatility, or daily high-low spreads
17
Cross market interaction - even tougher


“Combination” of correlated products must be detected.

By statistical analysis

By collecting market-common trading strategies
“Cross-over market participants” must be identified.

Through regulatory bodies’ monitoring of exposures of regulated
entities with global activities ?
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Observations

“Price” data is relatively easy to obtain, from exchanges,
broker/dealers (through vendors or directly), or central banks’ survey.



Standardized index-type derivatives were often used as the benchmark to
individual cash products.
Availability of “Volume” data depends.

Data for “Issuance (primary flow)” and “Outstanding” can be obtained
from rating agencies, vendors (Thomson Reuters, Datastream). Issuance
may also be obtained from arrangers.

Data of “Transaction volume (secondary flow)” of unlisted products is
scarcely available.
Information of “Investors”, “Buyers” and “Sellers” are almost nonexistent, except for products covered by FoF and/or ad-hoc survey.
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What did we miss ? (1)

Market data coverage is limited even for TRADITIONAL products.
e.g., who are the major sellers of listed equities?

Developments of hard-to-recognize products and trading infrastructure
make the situation worse.
e.g., dark pool, off-balance-sheet derivatives, VIEs


Newly developed products/markets, if not too customized, mostly have
price/issuance volume datasets while holders’ information is very limited.
Investor (Holder) information and shadow banking sector information
are hard to obtain.

Information obtained through regulatory monitoring canNOT be freely
shared. Furthermore, even monitoring information is imperfect as not all
the risk holders are monitored by regulators, or they can be CROSSBOARDER.
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What did we miss ? (2)

“Genuine” risk exposure is difficult to identify.

Notional amount does not necessarily reflect the risk amount.

Mark-to-market value, or even the potential exposure calculated
for risk management purpose under some stress scenario, did not
produce useful information given the massively excessive liquidity
environment.
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Tentative Conclusion

It’s impossible to obtain perfect datasets for every single product
without a significant time lag.

Comprehensive statistics/survey are time consuming and incur heavy
costs, while information may be obsolete when published.

Such statistics may not include new developments as they have to ask
the same questions for the purpose of continuity, and therefore may not
work as a forward-looking risk detector during the turmoil.
➵ Some sort of coordinated efforts to compile data and qualitative
assessments gathered through the regulatory monitoring across the
globe may help.
➵ Sharing the list of “data sources available to the public (or quasipublic)” will greatly help.
➵ Rating agencies, clearing houses, and central counterparties may be
able to offer data with more details.
➵ To gather anecdotal information ahead of data collection will help,
especially when changes are so quick.
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