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Stress Testing
VAR - what does it mean



(value-at-risk) - the worst expected loss
          over a given horizon
    under normal market conditions
       at a given confidence level
MAIN PROBLEM
 VAR m easure s can
 fai l to i de ntify
 extrem e u n u sual
 situations


        losses
STRESS TESTING



    a process to identify
   and manage situations
      that could cause
    extraordinary losses
STRESS TESTING tools



scenario analysis
stressing models
policy responses
Why stress testing?
         Simulating:

    SHOCKS THAT HAVE NEVER
          OCCURRED


     SHOCKS THAT REFLECT
    PERMANENT STRUCTURAL
            BREAK
Imprementing scenario analysis



   Rp,s =∑Wi,t • Ri,s
   s - selected scenario
   i - number of scenarios
   R - portfolio return
                ❖
generating unidimensional scenarios
         Guidelines for scenarios by DPG

✓Parallel yield curve shifting by ±100 basis points

✓ Yield curve twisting by ±25 basis points

✓ Each of the four combinations or yield curves shift and
twist

✓ Equity index values changing by ±10 percent

✓ Currencies moving by ±6 percent for marjor currencies

✓ Swap spreads changing by ±20 basis points
The OTS*
           requiers
to estimate what would happen to
your economic value under parallel
shifts in the yield curve varying
from -400 to +400 basis points



*The office of Thrift Supervision
The SPAN system*

              The objective:
to identify movements in portfolio values
       under a series of scenarios

➙ SPAN searches for the largest loss that a
portfolio may suffer and sets the margin at
                 that level

           *1998, Chicago Mercantile Exchange
The SPAN system

     ✔ it considers only two risk
     factors

     ✘ places the same probability on
     most of the scenarios

     ✘ ignores correlations between
     risk factors
Multidimensional scenario analysis

 Unidimensional scenarios
 Prospective Scenarios
 Factor Push Method
 Conditional Scenario Method
 Historical Scenatios
 Systematic Scenarios
«Bottom-up approach»

◈ posting a state of the world

◈ inferring movements in market
  as in a top-down approach
Prospective Scenarios


An earthquake
   or war?
  Examine the effect


*not well suited to large, complex pertfolios
Factor Push Method

➙ push up and down ➙ compute the changes to the
portfolio

➙ evaluate a worst-case scenario, pushing all
parametres in the «worst loss» position



*completely ignores correlation

*looking at extreme movements may not be appropriate
Conditional scenario Method
               ≈conditional normal VAR modeling


▸ at the core is the covariance matrix
▸ «KEY» variables R* + other variables R


                   ASL =        ∑ w *R *+ ∑ w R
                                    i   i    i           j   j   j




   ✘ results are much closer to the actual stress loss

  ✘ only in case where correlation plays the main role
Historical Scenarios

✓    helps    to   identify
scenarios that may be
outside the VAR window

✓ are useful to measure
joint movements in financial
variables
Systematic Scenarios
This analysis provides insight into the vulnerabilities of a
                   particular portfolio


                 Maximum-loss criterion
 To identify the wors loss through an optimization that
   respects the correlations between the risk-factor
                     movements ∆⨍

       ML = min loss (∆⨍ ) subject to ⨍ˈ∑⁻¹ ∆⨍ ≤ C
Stress testing model parameters



Sensitivity
                   We need details!
 analysis

  Model
                 Use the same period!
parameters
Managing Stress Tests
       Relevant scenarios require careful planning

(1) Purchase protection or insurance for the
events in quiestion

(2) Modify the portfolio to decrease the impact of
the event

(3) Diversify

(4) Develop a plan to correct course of action

(5) Prepare sources of alternative funding
Conclusions
                  People may be very bad at pre!cting extreme situations




✓stress test can be viewed as an extension of the historical

simulation method at increasingly higher confidence levels

✓«blind spots» or complement to standard VARmth

✓a complement to VARmth rather than a replacement



✗ can lead to a large amount of unfiltered information

✗ the risk to produce large numbers of scenarios

✗ difficult to interpret
Stress testing reminds:

VAR is no guarantee of
   worst case loss

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Stress Testing

  • 2. VAR - what does it mean (value-at-risk) - the worst expected loss over a given horizon under normal market conditions at a given confidence level
  • 3. MAIN PROBLEM VAR m easure s can fai l to i de ntify extrem e u n u sual situations losses
  • 4. STRESS TESTING a process to identify and manage situations that could cause extraordinary losses
  • 5. STRESS TESTING tools scenario analysis stressing models policy responses
  • 6. Why stress testing? Simulating: SHOCKS THAT HAVE NEVER OCCURRED SHOCKS THAT REFLECT PERMANENT STRUCTURAL BREAK
  • 7. Imprementing scenario analysis Rp,s =∑Wi,t • Ri,s s - selected scenario i - number of scenarios R - portfolio return ❖
  • 8. generating unidimensional scenarios Guidelines for scenarios by DPG ✓Parallel yield curve shifting by ±100 basis points ✓ Yield curve twisting by ±25 basis points ✓ Each of the four combinations or yield curves shift and twist ✓ Equity index values changing by ±10 percent ✓ Currencies moving by ±6 percent for marjor currencies ✓ Swap spreads changing by ±20 basis points
  • 9. The OTS* requiers to estimate what would happen to your economic value under parallel shifts in the yield curve varying from -400 to +400 basis points *The office of Thrift Supervision
  • 10. The SPAN system* The objective: to identify movements in portfolio values under a series of scenarios ➙ SPAN searches for the largest loss that a portfolio may suffer and sets the margin at that level *1998, Chicago Mercantile Exchange
  • 11. The SPAN system ✔ it considers only two risk factors ✘ places the same probability on most of the scenarios ✘ ignores correlations between risk factors
  • 12. Multidimensional scenario analysis Unidimensional scenarios Prospective Scenarios Factor Push Method Conditional Scenario Method Historical Scenatios Systematic Scenarios
  • 13. «Bottom-up approach» ◈ posting a state of the world ◈ inferring movements in market as in a top-down approach
  • 14. Prospective Scenarios An earthquake or war? Examine the effect *not well suited to large, complex pertfolios
  • 15. Factor Push Method ➙ push up and down ➙ compute the changes to the portfolio ➙ evaluate a worst-case scenario, pushing all parametres in the «worst loss» position *completely ignores correlation *looking at extreme movements may not be appropriate
  • 16. Conditional scenario Method ≈conditional normal VAR modeling ▸ at the core is the covariance matrix ▸ «KEY» variables R* + other variables R ASL = ∑ w *R *+ ∑ w R i i i j j j ✘ results are much closer to the actual stress loss ✘ only in case where correlation plays the main role
  • 17. Historical Scenarios ✓ helps to identify scenarios that may be outside the VAR window ✓ are useful to measure joint movements in financial variables
  • 18. Systematic Scenarios This analysis provides insight into the vulnerabilities of a particular portfolio Maximum-loss criterion To identify the wors loss through an optimization that respects the correlations between the risk-factor movements ∆⨍ ML = min loss (∆⨍ ) subject to ⨍ˈ∑⁻¹ ∆⨍ ≤ C
  • 19. Stress testing model parameters Sensitivity We need details! analysis Model Use the same period! parameters
  • 20. Managing Stress Tests Relevant scenarios require careful planning (1) Purchase protection or insurance for the events in quiestion (2) Modify the portfolio to decrease the impact of the event (3) Diversify (4) Develop a plan to correct course of action (5) Prepare sources of alternative funding
  • 21. Conclusions People may be very bad at pre!cting extreme situations ✓stress test can be viewed as an extension of the historical simulation method at increasingly higher confidence levels ✓«blind spots» or complement to standard VARmth ✓a complement to VARmth rather than a replacement ✗ can lead to a large amount of unfiltered information ✗ the risk to produce large numbers of scenarios ✗ difficult to interpret
  • 22. Stress testing reminds: VAR is no guarantee of worst case loss

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