1. Introduction to Risk Management :
Michael Le
16 April 2008

2. Outline
What is Risk?
2. Risk is Everywhere
3. Risk is Not Equal
4. Measuring Risk
5. Decision Making with Risk
6. Risk Management Systems
1.

4. What is Risk?

The Chinese symbol for risk is a combination
of both danger and opportunity

Risk = P(undesired event) x Consequence

In finance, risk is the variability of actual
returns around the expected return

5. Market Risk

Risk that value of investment will decrease due to
changes in market factors

Common types of market risk
Interest Rate Risk
 Equity Risk
 Currency Risk


Measured with Value at Risk (VaR)

6. Credit Risk

Risk of loss due to default on payment
on a loan or other types of credit

Structured credit risk is measured with
the Merton Model or asset value model

Credit risk is commonly associated with
credit ratings.

7. Operational Risk

Differs from market and credit risk

“The risk of loss resulting from
inadequate or failed internal processes,
people and systems or from external
events”

8. Examples of Operational
Risk
Internal Fraud
 External Fraud
 Employment Practices and Workplace
Safety
 Clients, Products, & Business
 Damage to Physical Assets
 Business Disruption & Systems Failures
 Execution, Delivery, & Process
Management


10. Decision Making with Risk

Decisions making in finance requires a
degree of risk taking with it these are
some of the areas where risk affects
decision making
Investment Choices
 Corporate Finance


11. Investment Choices

Investment choices looks at the different
assets to come up with a portfolio
design for the risk aversion of an
investor
Asset Allocation
 Asset Selection
 Performance Evaluation


12. Corporate Finance

Corporate finance is related to decisions
that corporations make related to
running the business.
Investment decisions
 Financing decisions
 Dividend decisions


14. Risk is Everywhere

Risks will come from places one would
least expect it and in a form that them to
come from and in unanticipated forms.

Good risk management is to be able to
adapt when confronted with the
unexpected.

15. Risk from Global Exposure

The Chinese Correction

27 February 2007

With rumors that China would raise the
interest rate to curb inflation, the
Shanghai Stock Exchange dropped 9%.

16. Result of the Chinese
Correction

DOW Jones Industrial Average (DJIA)
fell 416 points

This was the largest single day fall since
the 9/11 attack in 2001 where the DJIA
fell 684 points.

17. Risk from Different Businesses

Best example of this is with the sub-prime
mortgages and collateral debt obligations
(CDOs)
In 2000, Credit Suisse issued a $340.7
million CDO.
 It was a mix of junk bonds and sub prime
home loans
 By 2006, the CDO losses totaled $125
million


18. Credit Suisse CDO - 2000
Amount (in millions)
Tranche
Rating
$293.5
Senior
AAA
$13.0
Mezzanine
A
$17.0
Mezzanine
BBB-
$11.2
Equity
Not Rated
$6.0
Equity
Not Rated

19. Credit Suisse CDO - 2006
Amount (in millions)
Tranche
Rating
$220.5
Senior
AAA
$0
Mezzanine
A
$0
Mezzanine
BBB-
$0
Equity
Not Rated
$0
Equity
Not Rated

20. Subprime Primer
Banks were originally not allowed to
invest in mortgages because they were
not investment grade.
 In the 1980s, banks started to package
mortgages into collateral debt
obligations through securitization.
 Thus mortgages are now were able to
be traded and invested.


21. 
Subprime mortgages are those from
buyers with weak credit and are usually
charged 2 percentage points higher than
those with good ratings

22. 
Exotic mortgages such as no-doc loans
allowed people with bad credit to take
loans without documentation to show
evidence of income or savings

23. 
Big banks buy the loans from the
lenders and small banks and securitize
the loans into CDOs with the help of
rating companies to achieve the desired
rating.

24. 
By engineering products with high
ratings (AAA), investors liked CDOs
because of the high returns compared to
bonds of same rating.

25. To add noise to the confusion, CDOs
can be multiplied with CDO squareds
and CDO cubeds
 This only hid the underlying assets even
more.


26. 
As subprime mortgages defaulted by
people who had bad credit. The CDOs
began to default as well.

28. 
Due to the complex nature of CDOs it
was hard to see what was the
underlying assets.

We just believed the credit risk ratings

30. Credit Ratings

Common terms
 Issue Rating
 Issuer Rating

31. Who



Fitch Ratings (U.S.)
Moody's (U.S.)
Standard & Poor's (U.S.)






A. M. Best (U.S.)
Baycorp Advantage (Australia)
Dominion Bond Rating Service (Canada)
Pacific Credit Rating (Peru)
Egan-Jones Ratings Company (U.S.)
Capital Intelligence Ltd (Cyprus)

33. Credit Ratings are Not
Equal

Ratings from one type of instrument do
not translate directly for comparison with
another instrument

“In CDO –land, there’s almost no
difference between Baa and Ba” –
Arturo Cifuentes, former Moody’s
executive

34. 
Corporate bonds rated Baa (Moody’s)
from 1983 to 2005
 Default rate 2.2 percent over 5 year periods

CDOs rated Baa (Moody’s) from 1983 to
2005
 Default rate 24 percent over 5 year periods

35. Rating agencies work with banks

In financial engineering for securitization,
rating agencies consult with banks on how
to structure the CDO

CDOs aren’t regulated like bonds. They are
sold in private placements and current
values are not posted

Financial regulators effectively outsourced
the monitoring of CDOs to rating agencies

36. Analyze the Money

Revenue between rating bonds and
CDOs (S&P)
 Corporate bonds - $212,500
 CDOs - $600,000

Revenue from analyzing CDOs in 2006
 Moodys - $204 million
 Fitch Ratings - $480.5 million

38. Issues with CDO ratings
Garbage in, garbage out
 Due to complex nature (many moving
parts), must account for possibility of
many things going wrong.


Financial products are being more
complex for current methodologies.

39. 
“The credit ratings and observations
contained herein are solely statements
of opinion and not statements of fact or
recommendations to purchase, hold, or
sell any securities or make any other
investment decisions.
Accordingly, any user of the information
contained herein should not rely on any
credit rating or other opinion contained
herein in making any investment
decision.” – S&P

40. Credit Ratings

The lack of transparency and potential
conflict of interest makes it hard for
ratings to be taken at face value.

Good risk management would involve
understanding how the products were
rated.

42. Measuring Risk

Quantifying and measuring risk is one of
the key points of risk management

Focus will be on common risk
measurements
 Value at Risk (VaR)
 Profit and Loss (PnL)

43. Value at Risk (VaR)
Focuses on volatility both up and down
 VaR statistic is made up of 3 parts

 Time Period
 Confidence Interval
 Loss amount (percentage)

“What is the most I can lose with 95%
confidence in the next month?”

44. VaR Calculation
3
ways of calculating:
 Historical
 Variance Covariance
 Monte Carlo Simulation

45. VaR: Historical Model

Assumes history will repeat itself

Arranges historical returns into buckets
from order of worst to best returns

46. Investopedia

47. VaR: Variance Covariance
Method
 Assumes
that stock returns are
normally distributed
 Variance
measures how actual
returns vary around the expected
return
 Covariance
tells us how two assets
are correlated

48. Normal Distribution
http://www.ifa.com/images/12steps/step8/f8-1.jpg

49. Variance
σP 2= wA2 σA2 + wB2 σB2 + 2wAwB σAσBρAB
Correlation Coefficient
ρAB = COVAB
σAσB

50. 





Portfolio with 2 assets A and B
70% invested in A
30% invested in B
Standard Deviation of A (σA) is 10%
Standard Deviation of B (σB) is 20%
Correlation coefficient (ρAB) is 0.5

σP 2= (0.7) 2(10) 2 + (0.3) 2(20) 2 +
2(0.7)(0.3)(10)(20)(.05) = 127

Portfolio Std. Dev = σP = 11.27%

51. Investopedia

52. Putting it all together

Build normal distribution
 Variance = σP 2
 Standard Deviation = σP
 Mean (weighted average rate of return)
Confidence
Std. Dev.
Calculation
Result
95%
2.64%
1.65 x 2.64
4.36%
99%
2.64%
2.33 x 2.64
6.16%

53. VaR: Monte Carlo
Simulations
Develop model for future stock prices
 Run multiple hypothetical trials
 Randomly generate trials (random
inputs)


54. Investopedia

55. Comparison

Monte Carlo – Complex

Historical Method – Requires gathering
historical data and number crunching

Variance Covariance – Easiest because
number are on readily available

57. What time is it?
To convert one VaR of one time period
to another time period
 Multiply standard deviation by square
root of the time period.
 Recalculate


Ex. σDaily = 2.5%
σmonthly = σDaily x √20 = 11.18%

58. Profit and Loss (PnL)

Statement that summarizes
the revenues, costs and expenses
incurred during a specific period of time.
DAILY PnL
Market Moves
Swap Rates
FX Changes
Rates resets
Trading
Amendments
$9,000
$25,000
-$100,000
$5,000
$80,000
-$1,000

59. 
It’s market close time, suddenly you
hear

“How’s my PnL?”

“Let me check”

Now we will look at how it put all
together in a bank

60. Risk Management Systems

What is it trying to achieve

Generate risk number and figures
 VaR
 PnL

Produce reports

61. Basic Flow
System reads security positions for book
 Read the closing values
 Reads the individual trades in the book
 Calculate the mark to market value of
the trade
 Calculate PnL,VaR, Sensitivities
 Done


62. Reality

There are thousands of books, hundreds of
trading desks and different business units
Different ways of storing and sending data.
 Different close times for data

Must be calculated for next morning
(T+1PnL)
 Reports generated within one hour of
market close is called T+0PnL


64. Thank you

This concludes the presentation 