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Initial Margin for Cleared Swaps

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The presentation was made by Amir Khwaja at the Marcus Evans Central Counterparty Clearing Workshop, 6th-7th February, 2013, London.

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Initial Margin for Cleared Swaps

  1. 1. Initial Margin for Cleared Swaps Amir Khwaja February 7, 2013
  2. 2. Agenda Margining of Bi-lateral and Cleared Swaps Initial Margin VaR - Historical Simulation VaR – New Trades VaR – Advanced Initial Margin – How and When Summary
  3. 3. Market – Interest Rate Swap VM CFs Bank A Firm B
  4. 4. Market – Cleared Interest Rate Swap VM CFs Bank A Firm B IM IM IM Clearing Bank A CCP Broker Firm B VM VM VM CFs CFs CFs
  5. 5. Market – Bi-lateral vs Cleared A Bi-lateral Interest Rate Swap under ISDA CSA  Variation Margin exchanged when Swap MTM > Threshold  Cash-flows exchanged as due  Independent amount is often zero or a fixed amount per trade Cleared Interest Rate Swap  Daily Initial Margin required in eligible securities or cash  Daily Variation Margin paid or received in currency  Cash-flows exchanged as due Initial margin is a significant operational change  It is required when the trade is cleared (T0/T1)  It is a large amount as covers the worst-case market risk loss over the period required to close-out an account (5d/7d)  E.g. IRS 10Y $25M, has IM @ $1m  Historical Simulation VaR is the most common methodology
  6. 6. Initial Margin What is the purpose? In the event of default, use members margin funds to cover loss Clearing House resources in event of a member default  Margin funds of defaulting member  Collateral from Clearing House Default fund  Other Collateral posted by Defaulting member  Member assessments to replenish the Default Fund  Clearing House backstop facilities  Clearing House Capital Portfolio is exposed to market risk loss in the time it takes to hedge and close-out the defaulting members portfolio Members margins should be sufficient for this  Variation margin is the daily P&L for the account  Initial Margin is the amount required by the Clearing House to hold the position
  7. 7. Value-at-Risk Value-at-Risk (VaR) is the most common method used  LCH, CME, SGX, Eurex, JSCC It is a measure familiar to banks for monitoring market risk Is required under Basel II and Basel III in determining the market risk capital requirement of banks So if an account has a VaR of $37 million  We need to know the confidence level e.g. 99%  And the holding period e.g. 5 days  And can then say this means that the account could make or lose more than $37m in a 5 day period, in average on only 1 out of 100 days  Note it does not say whether it could make or lose $37m or $370m! As Margin is the first-line of defence, it is reasonable to use VaR  Default Fund & Clearing House facilities are used to cover the rest
  8. 8. VaR - Historical Simulation Historical Simulation is the most common method to calculate VaR  LCH, CME, SGX, Eurex, JSCC It is the most easily understood of the methods  Variance-Covariance  Monte-Carlo Simulation As has less modelling assumptions than above two (e.g. Normal Dist) It relies on choosing:  A historical period, e.g. 5Y  A holding period e.g. 5 days  Generating daily holding period returns in this period e.g. daily 5d overlapping  Calculating the P&L impact on a portfolio by applying these returns to today  Ordering the P&L outcomes by decreasing loss  Interpolating for the desired confidence level or probability e.g. 99%
  9. 9. VaR - Historical Simulation USD 5Y Swap Rate 5d returns (bps) Sep08 to Sep12 80.00 60.00 40.00 13Dec10 > 20bps 20.00 0.00 -20.00 -40.00 -60.00 20Nov08 > -60bps -80.00
  10. 10. VaR - Historical Simulation Assume our portfolio has a PV01 (PVBP, DV01) of $1million  Assume for simplicity that USD 5Y Swap is the only market factor for the portfolio  (In reality there are many market risk factors for USD and other Currencies)  For a 1 bps rise in the 5Y Swap rate, our Profit will be $1m  For a 1 bps fall in the 5Y Swap rate, our Loss will be $1m  We can calculate the PL Series for our portfolio by multiplying the bps returns on each day by $1 million, which is shown below Profit Loss Sep08 to Sep12 80.00 60.00 40.00 20.00 0.00 -20.00 -40.00 20Nov08 > $60m -60.00 -80.00
  11. 11. VaR - Historical Simulation This PL Series  Has a PL value for each business day from 5 Sep 08 to 4 Sep 12  A total count of 1043 values  Each of which corresponds to a specific scenario date, starting on 5 Sep 08  And the first element represents the PL outcome of applying the 5-day return shift between 1 Sep 08 and 5 Sep 08 to todays market data and todays portfolio We call this the PL vector of the portfolio  The first few elements of which are shown below -26.51 09/05/2008 -5.97 09/08/2008 -12.75 09/09/2008 -6.47 For these dates 09/10/2008 -3.29 09/11/2008 -15.83 09/12/2008 -14.76 09/15/2008 -37.08 09/16/2008 -9.98 09/17/2008 -16.55 09/18/2008 22.76 09/19/2008 71.55 09/22/2008
  12. 12. VaR - Historical Simulation The PL vector can then be re-ordered by decreasing loss  Keeping a note of the scenario date and PL of each  The first part of this is shown below 1 -62.58 11/20/2008 2 -56.16 12/17/2008 3 -47.79 10/21/2008 4 -46.24 10/22/2008 Re-order by PL For these dates 5 -44.95 11/21/2008 6 -42.47 06/17/2009 7 -41.29 08/14/2009 8 -39.73 12/18/2008 9 -39.62 10/06/2008 10 -37.45 10/07/2008 VaR Date 11 -37.08 09/16/2008  Now we can determine the VaR  Which we will define as the loss of the 11th worst PL  (We could define as 10th worst or interpolate between 10th and 11th)  So VaR is $37.08m  Occurs on the scenario date of 16-Sep-08, we call this the VaR Date  This is the week of Lehman’s bankruptcy filing
  13. 13. VaR - Historical Simulation A Histogram is a good way to view the PL vector  Allocate each PL to a bin range  Frequency is high for small PLs, giving the distribution below 250 Mean -1.21 Standard Deviation 13.03 Kurtosis 2.63 200 5d PLs Skewness 0.17 Sep08 to Sep12 Range 134.13 Minimum -62.58 Maximum 71.55 VaR 99% -37.08 150 Frequency Count 1043 100 50 0 -70 -65 -60 -55 -50 -45 -40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80
  14. 14. VaR - Historical Simulation  Zooming in to the largest losses 11th largest loss Mean -1.21 Standard Deviation 13.03 Kurtosis 2.63 Skewness 0.17 Largest loss Range 134.13 Expected Shortfall Minimum -62.58 Maximum 71.55 VaR 99% -37.08 Count 1043
  15. 15. VaR – Historical Simulation An account has an Initial margin as determined by the CCP VaR is a non-additive measure  So we cannot calculate the VaR of a new trade  And add to the VaR of the acount  To estimate the new VaR / Initial Margin of the account Adding a new trade to the account can:  Make no change to the VaR  Make a small increase in the VaR  Make a small decrease in the VaR  Make a large increase in the VaR  Make a large decrease in the VaR Let us explore why
  16. 16. VaR – New Trades VaR is determined by a specific scenario loss  So for our 4Y period, there are @ 260 x 4 or actually 1,043 observations  For 99%, we assume the 11th largest loss determines the VaR  This scenario date, is known as the VaR Date  For our sample portfolio the VaR is $37m  Resulting from the USD 5Y Swap Rate dropping 37 bps
  17. 17. VaR – New Trades If the new trade is not sensitive to the USD 5Y Swap Rate  For instance if it is a JPY 1Y Swap  It may mean the Loss on VaR date will not change at all  As even though there are scenarios for JPY 1Y Swap  On the VaR Date the scenario value may be 0 (entirely plausible)  So the VaR PL will not change  And VaR will remain as $37m
  18. 18. VaR – New Trades It is more likely that this trade will cause a small change  As the JPY 1Y is more likely to have a non-zero shift on a large USD shift day  This means that all the tail scenarios (1-11) will change slightly  Either increasing loss (shift left) or decreasing loss (shift right)  So the 11th loss scenario may move left or right from its value of -37.08  Below it is shown increasing to a VaR of $37.75m -62.58 -63.24 -56.16 -57.31 -47.79 -48.55 -46.24 -46.44 Small Changes -44.95 -45.15 -42.47 -42.83 -41.29 -41.55 -39.73 -40.10 -39.62 -40.01 -37.45 -38.35 -37.08 -37.75
  19. 19. VaR – New Trades If the new trade is sensitive to the USD 7Y Swap Rate  For instance if it is a USD 7Y Swap  As USD 5Y and 7Y rates are highly correlated, would expect that a move of -37 bps in the 5Y would have a similar direction and size move for the 7Y  It will make a large change on the loss on the VaR Date  It is likely to change many of the surrounding tail scenarios  So much so that the VaR Date will change to a different one  This is likely to mean a much larger change in VaR, either higher or lower  Depending on whether the trade is risk reducing or risk increasing 11/20/2008 -62.58 11/20/2008 -66.24 12/17/2008 -56.16 12/17/2008 -59.31 10/21/2008 -47.79 10/22/2008 -52.55 10/22/2008 -46.24 10/21/2008 -49.44 11/21/2008 -44.95 Large Changes 11/21/2008 -48.15 06/17/2009 -42.47 06/17/2009 -46.83 New VaR Date 08/14/2009 -41.29 08/14/2009 -45.55 12/18/2008 -39.73 12/18/2008 -43.10 10/06/2008 -39.62 10/06/2008 -42.66 10/07/2008 -37.45 09/16/2008 -41.62 09/16/2008 -37.08 12/19/2008 -39.55
  20. 20. VaR – Market prices VaR changes even when no new trades in the portfolio Small effect  Each PL in the tail will change by a small amount due to different market prices  As each Swap will have a slightly different mtm value Large effect  If market prices differences are large enough  Change in order of tail scenarios, so a new VaR Date (11th scenario) 11/20/2008 -62.58 11/20/2008 -66.24 12/17/2008 -56.16 12/17/2008 -59.31 10/21/2008 -47.79 10/22/2008 -52.55 10/22/2008 -46.24 10/21/2008 -49.44 11/21/2008 -44.95 Large Changes 11/21/2008 -48.15 06/17/2009 -42.47 06/17/2009 -46.83 New VaR Date 08/14/2009 -41.29 08/14/2009 -45.55 12/18/2008 -39.73 12/18/2008 -43.10 10/06/2008 -39.62 10/06/2008 -42.66 10/07/2008 -37.45 09/16/2008 -41.62 09/16/2008 -37.08 12/19/2008 -39.55
  21. 21. VaR – Scenario roll-off VaR changes even when market data does not change Sometimes by a very large amount Caused by old scenarios rolling out of the historical window  For example the Sep/Oct 2008 will no longer be in our 4Y historical period  This is likely to mean a large change (decrease) in VaR 11/20/2008 -62.58 12/17/2008 -56.16 10/21/2008 -47.79 10/22/2008 -46.24 • Five Tail Scenarios are in Sep/Oct 2008 11/21/2008 -44.95 • These will drop off 06/17/2009 -42.47 • New scenarios in 2012 will have smaller PLs 08/14/2009 -41.29 • Other lower loss scenarios will replace these five 12/18/2008 -39.73 10/06/2008 -39.62 • The VaR will decrease substantially 10/07/2008 -37.45 09/16/2008 -37.08
  22. 22. VaR – Day to Day Changes Adding a new trade to the account can:  Make no change to the VaR  Make a small increase in the VaR  Make a small decrease in the VaR  Make a large increase in the VaR  Make a large decrease in the VaR Even with no new trades VaR can change  By a small amount  By a large amount This can seem non-intuitive Unless we learn to consider the PL Vectors and Histogram
  23. 23. VaR – Advanced Exponential weighting  Rather than give equal weight to each of the scenatios  Give more weight to recent observation dates over older dates  On the intuition that recent history is a better guideline to the near future  A more responsive VaR  So if recent history is more volatile, the VaR is more influenced more by these scenarios and less by the earlier ones so quicker to increase
  24. 24. VaR – Advanced Filtered Histsim (FHS) or Volatility Scaling  Uses current volatility to influence returns  On the intuition that in time-series data volatility is clustered i.e. there are longer periods of small market moves, punctuated by shorter periods of very high market moves USD 5Y Swap Rate Sep08 to Sep12 80.00 60.00 40.00 20.00 0.00 -20.00 -40.00 -60.00 -80.00
  25. 25. VaR – Advanced Filtered Histsim (FHS) or Volatility Scaling  Uses current volatility to influence returns  On the intuition that in time-series data volatility is clustered i.e. there are longer periods of small market moves, punctuated by shorter periods of very high market moves  So VaR should be increased in the volatile periods and decreased in the stable periods  We need to include this volatility dynamics  Otherwise will generate a higher or lower number of exceptions than our 99%  FHS worked well in the Swap market in the lead up, during and post the 2007- 2008 Financial Crises  So Initial Margin increasing faster at start of Crisis and then decreasing faster as Crisis period ended  Exactly the behaviour that a Clearing House and Clearing members would want from the margins to ensure they were adequate (not too high or too low)
  26. 26. VaR – Advanced Liquidity Adjustment  Exit cost of a position  Large position size in a currency or tenor  Need to know average daily trading volume for this currency & tenor  Need the bid/ask spread for this currency & tenor  A position that will take longer than our holding period (5d) to sell will incur a larger loss in bid/ask spread and longer time to sell  So the VaR should be increased with a Liquidity adjustment factor Credit Risk Multiplier  Clearing House or Clearing Member may impose this  As a multiplier on the VaR (e.g. 1.5 times)
  27. 27. VaR – Advanced Initial Margin requirements can be high for a derivatives portfolio  If large positions or long term or volatile markets  Collateral needs to be posted to cover this  It is important to understand the dynamics of Initial Margin (VaR) The previous slides give you an intuitive feel How do you get more detail?  Ask the Clearing House (CCP) or Clearing Broker (CB) for the risk margin methodology documentation  Send them a portfolio and ask for margin reports  Good for back-loading exercises  Good for comparison with other CCPs
  28. 28. Initial Margin – How and When CCP calculates  Next day and sends reports to Clearing Broker (CB)  Clearing Broker sends you a statement  For large trades or on large market move days  CCP or CB may call intra-day When do you need to know your margin?  Next day, so you can check the statement and send funds  By end-of-day, so you can pre-fund the margin account  Intra-day  Per trade, to allocate funding cost  Pre trade, to decide which CB/CCP So you cannot just rely on the next-day CCP margin statement
  29. 29. Initial Margin – How can you estimate? CCP/CB may provide you with tools  Excel workbook or Web calculator  Issues: Usability/Performance/Cost/Multiple CBs,CCPs You can build your own  Historical Simulation is widely understood and used  Issues: Resources/Expertise/Cost/Time Independent Vendors  Focused on offering this  Issues: Integration/Cost/Time
  30. 30. Summary Independent Amount often zero in a bi-lateral Swap Initial Margin is always required for a cleared Swap It is a significant amount at the portfolio account level Historical Simulation VaR is the method used by CCPs  Differences in details e.g. Historical period, Confidence level  Generally CCP’s Initial Margin amounts are similar Initial Margin changes in a non-intuitive manner, so  PL vectors, Histograms, Scenario Dates  Risk Reducing and Risk Increasing trades  Impact of changing PLs in the tail scenarios Relying solely on the next-day CCP/CB statement is not sufficient
  31. 31. Contact Details  My contact details for questions: amir@clarusft.com

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