SLIDE 1 Stress Testing
Financial Institutions
Rebel A. Cole
CARTAC
and
DePaul University
SLIDE 2 Stress Tests
Complement to statistical risk-
management models, such as Value-at- Risk (VaR), that are based upon the normal distribution.
VaR: the expected one-day loss that
- ccurs 1 out of 100 times (99%
confidence).
This would occur 2.5 times per year.
SLIDE 3 The Normal Distribution
Probability
68% 95% > 99% – 3 – 47.9% – 2 – 27.6% – 1 – 7.3% 13.0% + 1 33.3% + 2 53.6% + 3 73.9% Mean = 13.0%
SLIDE 4 Problems with Value-at-Risk
Most applications of VaR are based upon
historical values and the normal distribution.
- Normal may not be appropriate
- The recent historical experience may not be
appropriate
- Distributions are assumed stable over time.
There is a wide confidence interval around an
estimated tail probability.
Data intensive: requires a lot of historical data,
so it doesn’t work for credit risk.
SLIDE 5
Stress Tests as Complements to VaR
Focus of Stress Tests is on tail
probabilities—the “Tails” of the Normal distribution, especially the “left tail.”
Highly unlikely, but possible, adverse
events and outcomes.
It only takes one “outlier” to wipe out a
firm’s capital completely. No second chances (unless you are Citibank).
SLIDE 6 Stress Tests
Initially used primarily for "market risks":
- Interest-Rate Risk,
- Foreign-Exchange Risk,
- Equities Risk and
- Commodities Risk.
More recently, stress testing has
expanded to cover:
- Credit Risk and
- Liquidity Risk.
SLIDE 7
Stress Tests: Sensitivity vs. Scenario
Sensitivity Test: What is the impact of a
large movement in a financial variable (such as the interest rate or foreign- exchange rate) on the value of a firm’s portfolio.
Reason for movement is not specified. Lacks historical and economic content.
SLIDE 8
Stress Tests: Sensitivity vs. Scenario
Scenario Tests: What is the impact on the value of a firm’s
portfolio from simultaneous movements in several financial variables, driven by some event, historical or hypothetical.
The tie to a historical/ hypothetical event
provides historical and economic content
SLIDE 9 Stress Tests: Scenarios
Example:
The “Black Monday” Scenario (Oct. 1987) (Dow-Jones Industrial average fell by 25% )
Assumption: Equities fall by 25% in one day.
- a downward spike in the interest rates likely
will follow
- interest-rate risk rises
- FX risk rises
- credit risk rises as equity losses hit investors
SLIDE 10 Stress Tests: Scenarios
2001 BIS Survey of Stress-Testing
Practices at 38 Large Financial Institutions
- Very few ran such scenarios with multiple
simultaneous shocks.
- Outcome of recent crisis is therefore not very
surprising.
- VaR and the relative calm of the past few
years had lulled risk managers into a sense of complacency.
SLIDE 11
Maturity Gap vs. Repricing Gap
Often confused. Maturity gap is important for analysis of
liquidity risk: when do assets and liabilities mature?
Repricing gap is important for interest-
rate risk: when do assets and liabilities reprice?
SLIDE 12
Interest-Rate Risk: Repricing Gap vs. Duration Gap
How will a firm’s capital ratio change in
response to a change in interest rates?
Repricing gap analysis will provide
information about mismatches.
Duration gap analysis will provide an
actual number.
SLIDE 13 Liquidity Risk
Three basic analysis tools:
- Ratio Analysis
- Liquidity Gap Maturity Ladder
- Cash-Flow Forecasts
SLIDE 14
Foreign-Exchange Risk
Gap analysis of foreign-denominated
assets and liabilities.
Apply percentage shock to this gap to
estimate changes in capital.
SLIDE 15
Credit Risk
Earliest analysis was to simply assume a
percentage shock to NPLs without tying to any cause.
More recently, estimate relation between
NPLs and macro-economic variables.
For example, if GDP falls by 1% , then by
how much do NPLs typically rise?
Problems: is the historical relationship
stable over time?
SLIDE 16 Conclusions
Stress Tests are a valuable complement to
- ther risk-management tools.
Enable an analyst to explore “what if” we
encounter “tail-probability” events, such as the 1987, 1998 or 2008 financial crises.
