The document discusses various methods of measuring financial risk. It covers measures of exposure like risk exposure calculations, gap analysis, and stress testing. It then discusses value-at-risk (VaR) as a common measure of market risk, how it estimates potential portfolio losses based on historical data, and how it can be calculated using variance-covariance models or historical simulation. Monte Carlo simulation is also mentioned as another technique for calculating VaR.

1. Essentials of Financial Risk Management
Chapter 9: Measuring Risk
Lecture : Prof: Chheang Meng Hiek
Prepared by: PHET CHHO
DIEP ROUM
Karen A. Horcher John Wiley & Sons, Inc. 1

2. Measuring Risk
I. What is Risk?
II. Measures of Exposure
III. Value–at–Risk
IV. Credit Risk Measurement
V. Operational Risk Measurement
VI. Summary
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3. Chapter be able to
• Differentiate between measures of exposure
and measures of risk.
• Consider the strengths and weaknesses of risk
measurement Methodologies.
• Identify alternative strategies for estimating
risks.
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4. I. What is Risk?
• Risk is the business of probabilities….can be
defined as the chance of something
happening that will impact upon objectives.
• The estimated chance - The chance that an
investment's actual return will be different
than expected.
• A probabilities or threat of a damage, Loss, or
other negative occurrence that is caused by
external or internal vulnerabilities.
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5. Category of Risk
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6. Category of Risk - Cont
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7. Category of Risk - Cont
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8. Category of Risk - Cont
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9. II. Measures of Exposure
• What is the exposure?
 A problem when you have a number of possible
risks is that it can be difficult to decide which risks
are worth putting effort into addressing.
 One component of risk management.
 Risk Exposure is a simple calculation that gives a
numeric value to a risk, enabling different risks to
be compared.
 Risk Exposure of any given risk = Probability of risk
occurring x total loss if risk occurs
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10. Measures of Exposure – Cont
 There are two views of risk management.
 The day to-day or tactical standpoint.
 High-level or strategic view.
 To sum up:
 In order to manage risk, it is necessary to have the
capability to monitor risk from both standpoints in
order to assess potential loss to the organization.
 Risk management requires both quantitative and
qualitative analysis.
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11. Measures of Exposure – Cont
• Gap Analysis
 Measures the sensitivity of an exposure, asset, or portfolio
to market rate or price changes by considering the
mismatch between assets and liabilities.
 Currency exposure-arising from foreign currency - Cash
flows
 For example, if an organization has more euro inflows than
outflows in a given period, but the mismatch reverses the
following period, then the euro cash flows offset one
another with only a timing difference. If over the course of
a longer period, such as a fiscal cycle, there are more euros
coming in than going out, the difference provides exposure
to a falling euro.
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12. Measures of Exposure – Cont
• Leverage and Direction
 The use of leverage increases the potential for loss.
Therefore, the impact of any leverage or gearing strategy is
important to consider when calculating the amount that
an organization could potentially lose. The calculation of
potential loss without considering the impact of leverage
underestimates potential losses.
 Direction is the nature of an exposure or trading position,
either long or short. A long position will obviously benefit
from a rise in prices, while a short position benefits from a
price decline. Both leverage and direction are factors in the
potential size of a loss given an adverse market move.
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13. Measures of Exposure – Cont
• Instrument Sensitivity
 Can be a useful way to measure potential for risk.
 Duration - Estimate of the sensitivity of fixed income securities’ prices
to small changes in interest rates. For assessing gaps between assets
and liabilities.
 Convexity, Measures the rate of change of duration, Be used to further
refine the sensitivity of a fixed income security or exposure to interest
rate changes.
 Option delta- the option’s value given a change in the price of the
underlying.
• Scenario Analysis
 Commonly focuses on estimating what a portfolio's value would
decrease to if an unfavorable event, or the "worst-case scenario",
were realized.
 involves computing different reinvestment rates for expected returns
that are reinvested during the investment horizon.
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14. Measures of Exposure – Cont
• Stress Testing
 A simulation technique used on asset and liability portfolios to
determine their reactions to different financial situations. Stress tests
are also used to gauge how certain stressors will affect a company or
industry. They are usually computer-generated simulation models that
test hypothetical scenarios.
• Financial Crises
 A situation in which the value of financial institutions or assets drops
rapidly. A financial crisis is often associated with a panic or a run on
the banks, in which investors sell off assets or withdraw money from
savings accounts with the expectation that the value of those assets
will drop if they remain at a financial institution.
 Not Predictable.
 Correlations between markets and instruments, may break down
entirely.
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15. III. Value–at–Risk
• Used measure of market risk. It is the maximum
loss which can occur with X% confidence over a
holding period of n days.
• Used to estimate the probability of portfolio
losses based on the statistical analysis of
historical price trends and volatilities.
• Systematic methodology to quantify potential
financial loss based on statistical estimates of
probability.
• Can be used by any entity to measure its risk
exposure.
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16. Value-at-Risk – Cont
• Methods to Calculate
 Using historical data
 Using stochastic simulation, random or Monte Carlo
scenario generation. Monte Carlo simulation is based
on randomly generated market moves. Volatilities and
correlations are calculated directly from underlying
time-series data, assuming a normal distribution.
 Value-at-risk using the variance/covariance
(parametric) approach. Volatilities and correlations
are calculated directly from the underlying time
series, assuming a normal distribution.
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17. Value-at-Risk – Cont
• Variance-Covariance Method
 Normally Distributed – Correlations
 Historical data on investment returns
 Simple historic volatility: this is the most straight forward method but
the effects of a large one-off market move can significantly distort
volatilities over the required forecasting period.
Example:
An IBM stock is trading at $115 with a 1-year standard deviation of 20%. In
the normal distribution, 95% confidence level is 1.645 standard deviations
away from the mean.
Therefore, our VaR at 95% confidence level will be:
VaR (95%) = 115* 0.20 * 1.645 = 37.835
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18. Value-at-Risk – Cont
• VaR of a Portfolio
Generally VaR will not be calculated for a single position, but a
portfolio of positions. In such a case will require the portfolio volatility.
The portfolio volatility of a two-asset portfolio is given by:
• W1 is the weighting of the first asset
• W2 is the weighting of the second asset
• Q1 is the standard deviation or volatility of the first asset
• Q2 is the standard deviation or volatility of the second asset
• P is the correlation coefficient between the two assets
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19. Value–at–Risk - Cont
• Historical Simulation Method
 Finance's VaR analysis-Procedure for predicting VaR for many portfolio
 Determined on the basis of the information about potential profit and
loss gained from the simulation scenarios.
 Avoids some of the pitfalls of the correlation method (normally
distributed returns, constant correlations, constant deltas)
• VaR (1 – ) is the estimated VaR at the confidence level 100 × (1 – )%.
• (R) is the mean of the series of simulated returns or P&Ls of the
portfolio
• R is the worst return of the series of simulated P&Ls of the portfolio or,
in other words, the return of the series of simulated P&Ls that
corresponds to the level of significance
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20. Value-at-Risk – Cont
• Monte Carlo Simulation
Analyze (complex) instrument, Portfolios and investment by simulating the
various sources of uncertainty affecting their value, and then determining their
average value over the range of resultant outcomes.
To calculate VaR using M.C. simulation we
 Value portfolio today
 Sample once from the multivariate distributions of the xi
 Use the xi to determine market variables at end of one day
 Revalue the portfolio at the end of day
 Calculate P
 Repeat many times to build up a probability distribution for P
 VaR is the appropriate fracted of the distribution times square root of N
 For example, with 1,000 trial the 1 percentile is the 10th worst case.
 Use the quadratic approximation to calculate P
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21. IV. Credit Risk Measurement
Definition
 One of the most fundamental types of risk. it represents the chance the
investor will lose his or her investment.
 The probability of loss as a result of the failure or unwillingness of a
counterparty or borrower to fulfill a financial obligation.
Issue:
 Exposure to credit risk increases with the market value of outstanding
financial instruments with other counterparties, all else being equal.
 Whenever a borrower is expecting to use future cash flows to pay a current
debt.
 Investors are compensated for assuming credit risk by way of interest
payments from the borrower or issuer of a debt obligation.
 Lender's risk that borrower will not repay or The total amount of credit
extended to a borrower by a lender.
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22. Credit Risk Measurement - Cont
• Counterparty Ratings
 The risk to each party of a contract that the counterparty will not live up to its
contractual obligations. Counterparty risk as a risk to both parties and should
be considered when evaluating a contract.
 The other party that participates in a financial transaction. Every transaction
must have a counterparty in order for the transaction to go through. More
specifically, every buyer of an asset must be paired up with a seller that is
willing to sell and vice versa.
 In most financial contracts, counterparty risk is also known as "default risk".
• Notional Exposure
 In an interest rate swap, the predetermined dollar amounts on which the
exchanged interest payments are based. Notional principal never changes
hands in the transaction, which is why it is considered notional, or theoretical.
Neither party pays or receives the notional principal amount at any time; only
interest rate payments change hands.
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23. Credit Risk Measurement - Cont
• Aggregate Exposure
 The exposure of a bank, financial institution, or any type of
major investor to foreign exchange contracts - both spot and
forward - from a single counterparty or client.
 Aggregate risk in forex may also be defined as the total
exposure of an entity to changes or fluctuations in currency
rates.
• Replacement Cost
 The cost to replace the assets of a company or a property of the
same or equal value. The replacement cost asset of a company
could be a building, stocks, accounts receivable or liens. This
cost can change depending on changes in market value.
 Also referred to as the price that will have to be paid to replace
an existing asset with a similar asset.
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24. Credit Risk Measurement - Cont
• Credit Risk Measures
 Probability of counterparty default, which is an
assessment of the likelihood of the counterparty
defaulting.
 Exposure at counterparty default, which takes into
account an organization’s exposure to a defaulting
counterparty at the time of default.
 Loss given counterparty default, which considers
recovery of amounts that reduces the loss otherwise
resulting from a default.
• Future of Credit Risk Measurement
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25. V. Operational Risk Measurement
Overview of Operational Risk
• Inadequate or failed internal processes: Marketing material can be mailed to
the wrong customers, account opening documentation can turn out not to be
robust, transactions can be processed incorrectly, etc.
• People: violation of employee health and safety rules, organized labor
activities and discrimination claims. inadequate training and management,
human error, lack of segregation, reliance on key individuals, lack of integrity,
honesty, etc.
• Systems: The growing dependence of financial institutions on IT systems is a
key source of operational risk. Data corruption problems, whether accidental
or deliberate, are regular sources of embarrassing and costly operational
mistakes.
• External events: This source of operational risk has at least two discernible
dimensions to it, firstly the extent to which a chosen business strategy pursued
by a bank may expose it to adverse external events, and secondly external
events that impact it independently, emanating from the business
environment in which it operates.
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26. Operational Risk Measurement - Cont
Some methods that have been used to measure or
indicate potential for operational risk in financial
institutions and other organizations include:
 Number of deviations from policy or stated procedure
 Comments and notes from internal or external audits
 Volume of derivatives trades (gross, not netted)
 Levels of staff turnover
 Volatility of earnings
 Unusual complaints from customers or vendors
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27. VI. Summary
• The concept of probability is the central tenet of risk, and
the business of risk measurement involves estimating the
probability of loss.
• Scenario analysis involves using a set of predetermined
changes in market prices or scenarios to test the
performance of the current portfolio or exposure.
• The most commonly used measure of market risk is value
at risk, a systematic methodology based on statistical
estimates.
• As the costs of computation decline and user sophistication
increases, the number and variety of risk management
tools has increased substantially. More rigorous
measurements of risk will likely become commonplace.
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28. Discussion
Thank You.
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