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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. Part I.

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Imf fsb users conference l.jpg

IMF-FSB Users Conference

WANTED datasets during the financial markets turmoil - Securities and Derivatives -

Yuko Kawai, Bank of Japan

8 July, 2009


Agenda l.jpg

Agenda

  • Market observations during the turmoil

  • What we wanted to know

  • Then-available datasets and what we had missed


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PartI

Market observations during the market turmoil


Before the crisis l.jpg

Before the crisis

  • “Shadow Banking” sector risk has loomed up

Banks

Final Borrowers

Final Investors

$

$$$$

$$$

$$

Brokers

“Shadow

Banking”

$$

$$

$$$$

Funds


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Before the crisis

  • We did not have the full knowledge about “them”

What are they? How LARGE are they? What share of money flow between final investors (and who are they?) and final borrowers is intermediated by this sector?

Banks

Final Borrowers

Final Investors

$

$$$$

$$$

$$

Brokers

“Shadow

Banking”

$$

$$

$$$$

Funds


During the crisis l.jpg

During the crisis

  • Money flows dwindled, loss/defaults surged everywhere

Banks

Loss

Final Borrowers

Final Investors

Defaults

$

Defaults

$$

Brokers

Defaults

“Shadow

Banking”

Funds

$

$

Defaults


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During the crisis

How resilient are they? How are the financing/capital raising conditions?

  • Through market data,

    we tried to identify….

Banks

Loss

Final Borrowers

Final Investors

Defaults

$

Defaults

$$

Brokers

Defaults

“Shadow

Banking”

How much is the current/maximum loss?

How levered were they? By what instrument?

Who holds what risk? How much?

Funds

$

$

Defaults


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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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PartII

What we wanted to know


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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.


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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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PartIII

Then-available datasets and what we had missed


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Examples of “flow” products

“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


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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


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Market Liquidity – difficult to identify

  • Proxy by Offer-bid, on- vs. off-the-run spreads, transaction volume, volatility, or daily high-low spreads


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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 non-existent, 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 CROSS-BOARDER.


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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 quasi-public)” 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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