Designing a system to analyze portfolio risks    and to determine optimum margin               requirements          Serka...
Designing a system to analyze portfoliorisks and to determine optimum margin              requirements     “RACR-2011, 22-...
Overview•   Objective•   Problem statement•   Background information•   Description of the system•   Methods•   Results•  ...
Objective• Designing a real-time risk management system to  evaluate portfolios• Developing a responsive system to analyze...
Problem Statement• Uncertainty and risk are everywhere in finance• Determining the value loss that the derivatives  portfo...
ISE & ISE Data - 1• Istanbul Stock Exchange (ISE) is a dynamic and  growing emerging market with an increasing number of  ...
10000                                                                                            20000                    ...
Components of the System• CME SPAN (http://www.cmegroup.com/clearing/risk-  management/span-overview.html)• SPAN parameter...
CME SPAN• Developed and implemented in 1988 by Chicago  Mercantile Exchange (CME)• Portfolio based risk and margin calcula...
Realtime Risk Management System using SPAN - 1 • Trades are fed to clearing system as soon as they’re   matched. • Trades ...
Realtime Risk Management System using SPAN - 2        “RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR”   11
Realtime Risk Management System using SPAN - 3 • Similar mechanism is used for processing orders of risky   accounts. • Si...
Realtime Risk Management System using SPAN - 4        “RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR”   13
SPAN Parameters•   Price scan range (PSR)•   Volatility scan range (VSR)•   Inter-commodity spread credit•   Short option ...
Variables used to judge methods• All variables used to estimate VaR and judge methods we use. We  start by calculating the...
-0,2                                                                                       -0,15                          ...
Methods discussed•   Extreme Value Theory•   Historical Simulation•   GARCH•   EGARCH•   Asymmetric CaViaR          “RACR-...
Extreme Value Theory• Models possibility of big losses.• Focuses on tails instead of the entire distribution.• Our approac...
Historical Simulation• Non-parametric method• Directly uses historical data to estimate the current  market conditions• De...
GARCH• Established by Tim Bollerslev in 1986• Models current volatility by recent volatility values  and returns.• We calc...
EGARCH• GARCH model considers positive and negative  returns equally.• In practice, positive and negative returns have  as...
Asymmetric CAViaR• Conditional autoregressive value at risk (CAViaR)  methods introduced by Engle and Manganelli.• Focus o...
Results (1990/12-2010/12)“RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR”   23
Results (2004/01-2010/12)“RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR”   24
Comparing PSR Estimation Methods - 1• When the methods tested against near term data, none of the  methods is able to meet...
Comparing PSR Estimation Methods - 2• When average margins are taken into account we see that  extreme value comes with a ...
Graph of PSR Estimates vs. Current Margin Rate        “RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR”   27
Conclusions• We presented a methodology for estimating PSR  parameter for SPAN because its important for a risk  managemen...
THANKS ...“RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR”   29
Upcoming SlideShare
Loading in...5
×

Designing a system to analyze portfolio risks and to determine optimum margin requirements

715

Published on

Presentation of our paper for RACR 2011 held in Laredo,TX

Published in: Business, Economy & Finance
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
715
On Slideshare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
5
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Designing a system to analyze portfolio risks and to determine optimum margin requirements

  1. 1. Designing a system to analyze portfolio risks and to determine optimum margin requirements Serkan KABA & Murat ACAR skaba@takasbank.com.tr macar@takasbank.com.tr ISE Settlement and Custody Bank Inc. Istanbul/TURKEY “RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 1
  2. 2. Designing a system to analyze portfoliorisks and to determine optimum margin requirements “RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 2
  3. 3. Overview• Objective• Problem statement• Background information• Description of the system• Methods• Results• Conclusions “RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 3
  4. 4. Objective• Designing a real-time risk management system to evaluate portfolios• Developing a responsive system to analyze portfolios and orders in real-time• Updating the portfolio risks accordingly• Determining the best method to estimate margin levels for every asset class “RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 4
  5. 5. Problem Statement• Uncertainty and risk are everywhere in finance• Determining the value loss that the derivatives portfolio could hypothetically suffer with some given probability and assumptions• Defining margins required by brokers from their customers for certain kinds of transactions• Protecting brokers from losses that may result from adverse price changes affecting the customers net balance “RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 5
  6. 6. ISE & ISE Data - 1• Istanbul Stock Exchange (ISE) is a dynamic and growing emerging market with an increasing number of publicly traded companies, state-of-the-art technology and strong foreign participation• The derivatives market began operation in 2001. In 2005, Turkish Derivatives Exchange (TURKDEX) started its operation as a successor• ISE National 100 Index data is used to estimate PSR values for ISE100 derivatives. “RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 6
  7. 7. 10000 20000 30000 40000 50000 60000 70000 80000 0 05/01/1990 01/05/1990 23/08/1990 12/12/1990 02/04/1991 01/08/1991 21/11/1991 10/03/1992 06/07/1992 21/10/1992 11/02/1993 15/06/1993 01/10/1993 20/01/1994 11/05/1994 02/09/1994 20/12/1994 10/04/1995 04/08/1995 22/11/1995 18/03/1996 09/07/1996 25/10/1996 20/02/1997 17/06/1997 02/10/1997 23/01/1998 26/05/1998 10/09/1998 31/12/1998 05/05/1999 02/09/1999 22/12/1999 24/04/2000 10/08/2000 28/11/2000 30/03/2001 18/07/2001 07/11/2001 06/03/2002 24/06/2002 10/10/2002 04/02/2003 02/06/2003 17/09/2003 16/01/2004 ISE & ISE Data - 2 11/05/2004 27/08/2004 21/12/2004 14/04/2005 02/08/2005 24/11/2005 20/03/2006 06/07/2006 27/10/2006“RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 16/02/2007 06/06/2007 24/09/2007 21/01/2008 08/05/2008 26/08/2008 25/12/2008 14/04/2009 04/08/2009 25/11/2009 18/03/2010 07/07/2010 01/11/2010 7
  8. 8. Components of the System• CME SPAN (http://www.cmegroup.com/clearing/risk- management/span-overview.html)• SPAN parameters and parameter estimation• Realtime risk management system using SPAN “RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 8
  9. 9. CME SPAN• Developed and implemented in 1988 by Chicago Mercantile Exchange (CME)• Portfolio based risk and margin calculation.• Scenario based: 16 scenarios taken into consideration and worst loss is taken into account.• Currently used by more than 50 exchanges and clearinghouses. “RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 9
  10. 10. Realtime Risk Management System using SPAN - 1 • Trades are fed to clearing system as soon as they’re matched. • Trades are processed in clearing system and new positions are calculated. • New positions are sent to SPAN daemon for margin calculations. For efficiency multiple SPAN calculation engines are used and multiple portfolios are evaluated concurrently. • Margin requirements are sent back to clearing system and account risk is updated accordingly. • Notification is sent to exchange if the account becomes risky. “RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 10
  11. 11. Realtime Risk Management System using SPAN - 2 “RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 11
  12. 12. Realtime Risk Management System using SPAN - 3 • Similar mechanism is used for processing orders of risky accounts. • Single order for risky accounts are fed to clearing system as soon as they’re issued. • Orders are processed in clearing system and hypothetical positions are calculated. • Hypothetical positions are sent to a separate SPAN daemon for margin calculations. • Margin requirement (if the order is matched) are sent back to clearing system and the new account risk is determined. • Notification is sent to exchange if the account becomes non-risky. “RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 12
  13. 13. Realtime Risk Management System using SPAN - 4 “RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 13
  14. 14. SPAN Parameters• Price scan range (PSR)• Volatility scan range (VSR)• Inter-commodity spread credit• Short option minimum (SOM)• Intra-commodity spread charge• Spot month charge “RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 14
  15. 15. Variables used to judge methods• All variables used to estimate VaR and judge methods we use. We start by calculating the daily return series and estimating VaRs. Then we judge methods based on characteristics of these VaR estimates. xt ISE National 100 index series xt ISE National 100 index return series r t= −1 xt − 1 Exceedances T ∑ et Total Exceedances t=1 T ∑ et Exceedance rate t= 1 T Average Margin “RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 15
  16. 16. -0,2 -0,15 -0,1 -0,05 0,1 0,2 0,05 0,15 0,25 0 05/01/1990 01/05/1990 23/08/1990 12/12/1990 02/04/1991 01/08/1991 21/11/1991 10/03/1992 06/07/1992 21/10/1992 11/02/1993 15/06/1993 01/10/1993 20/01/1994 11/05/1994 02/09/1994 20/12/1994 10/04/1995 04/08/1995 22/11/1995 18/03/1996 09/07/1996 25/10/1996 20/02/1997 17/06/1997 02/10/1997 23/01/1998 26/05/1998 10/09/1998 31/12/1998 05/05/1999 02/09/1999 22/12/1999 24/04/2000 10/08/2000 28/11/2000 30/03/2001 18/07/2001 07/11/2001 06/03/2002 24/06/2002 10/10/2002 04/02/2003 02/06/2003 17/09/2003 16/01/2004 11/05/2004 27/08/2004 21/12/2004 14/04/2005 02/08/2005 24/11/2005 20/03/2006 06/07/2006 27/10/2006 16/02/2007“RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 06/06/2007 24/09/2007 21/01/2008 08/05/2008 26/08/2008 25/12/2008 14/04/2009 04/08/2009 25/11/2009 18/03/2010 ISE National 100 Index daily return series (rt) 07/07/2010 01/11/2010 16
  17. 17. Methods discussed• Extreme Value Theory• Historical Simulation• GARCH• EGARCH• Asymmetric CaViaR “RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 17
  18. 18. Extreme Value Theory• Models possibility of big losses.• Focuses on tails instead of the entire distribution.• Our approach uses Peaks Over Threshold method which uses values above a high threshold and Generalized Pareto Distribution (GPD) as limiting distribution of these values. “RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 18
  19. 19. Historical Simulation• Non-parametric method• Directly uses historical data to estimate the current market conditions• Determines nth worst loss in historical data depending on the window size and confidence level.• 750 day window is used as opposed to other methods which we used a 500 day window. “RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 19
  20. 20. GARCH• Established by Tim Bollerslev in 1986• Models current volatility by recent volatility values and returns.• We calculate the VaR using the volatility estimate by assuming that the returns are normally distributed.• We use GARCH(1,1) since higher lags dont improve the results. “RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 20
  21. 21. EGARCH• GARCH model considers positive and negative returns equally.• In practice, positive and negative returns have asymmetric effect on volatility. For equities, volatility increases more with a negative return shock where as foreign exchange volatilities exhibit an opposite relationship.• EGARCH model established by Nelson is a form of GARCH which considers these asymmetries.• We calculate the VaR using the volatility estimate using Students t distribution. “RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 21
  22. 22. Asymmetric CAViaR• Conditional autoregressive value at risk (CAViaR) methods introduced by Engle and Manganelli.• Focus on behavior of a quantile.• They model quantiles autoregressively relationship.• We use the asymmetric variation which takes into account asymmetric effect of returns on volatility. “RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 22
  23. 23. Results (1990/12-2010/12)“RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 23
  24. 24. Results (2004/01-2010/12)“RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 24
  25. 25. Comparing PSR Estimation Methods - 1• When the methods tested against near term data, none of the methods is able to meet the 5‰ exceedance level.• Extreme value and EGARCH, come close to this performing below 1% exceedance rate.• Next best performers are CAViaR asymmetric and historical simulation performing below 1.5% exceedance rate.• Results obtained from long term data show that exceedance rates for extreme value, EGARCH and historical simulation decreased slightly, whereas that of CAViaR asymmetric decreased drastically for long term.• We can conclude that CAViaR asymmetric could have performed better for earlier years of ISE and other 4 slightly underperformed in the near term. “RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 25
  26. 26. Comparing PSR Estimation Methods - 2• When average margins are taken into account we see that extreme value comes with a price, higher margin rates.• Long term margin averages of the best perfoming models, extreme value and EGARCH, are 11% and 8.9% respectively, way above the 7.5% maintenance margin rate currently used.• Near term averages are more acceptable 8.8% and 5.6% respectively.• Analyzing average margins, since both methods cause exceedances below 1%, approximately 2 exceedances per year, we can say that EGARCH can be more usable since it produces much lower margin rates. “RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 26
  27. 27. Graph of PSR Estimates vs. Current Margin Rate “RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 27
  28. 28. Conclusions• We presented a methodology for estimating PSR parameter for SPAN because its important for a risk management system to calculate optimum margin rates.• PSR estimation methods can be further compared by analyzing other characteristics such as margin variance.• Margins changes can be decreased by smoothing, in other words discarding tolerable changes in margin levels.• The other parameters of SPAN that we mentioned can be estimated using similar methods. “RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 28
  29. 29. THANKS ...“RACR-2011, 22-25 May, 2011, Laredo, TX USA” “S. KABA & M. ACAR” 29
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×