14-26 Risk Management

Question 0

Contrast decentralized and centralized risk governance systems

Answer 0

Decentralized - places responsibility for execution within each unit of the firm
-risk mgmt is in the hands of those closest to the risk

Centralized - aka enterprise risk mgmt - places responsibility for execution within one central unit of the firm

• better view of overall risk borne by firm and considers correlations of risk between units within firm
• offers economies of scale

Question 1

4 Qualities of high quality Risk Governance

Answer 1

1. transparent
2. establish clear accountability
3. cost efficient in use of resources
4. effective in achieving desired outcomes

Question 2

3 types of financial risks

Explain

Answer 2

1. market risk - usually largest component of risk - related to changes in i rates, exchange rates, equity prices, commodity prices, etc (are tied to supply and demand)
2. credit risk - second largest risk component - risk of loss caused by counterparty’s default
3. liquidity risk - risk of loss caused by inability to liquidate a position quickly at fair price (can be estimated by bid-ask spread - narrow vs wide - and average trade volume)

Question 3

7 types of non-financial risks

Explain

Answer 3

1. operational risk - risk of loss caused by firm’s systems failure or disaster events like terrorism (internal or external events)
2. settlement risk - aka Herstatt risk - is risk that I paid my promised amount (e.g. in a swap) while counterparty defaults and doesn’t pay his amount.
   - risk is reduced by using exchanges (netting and reduce credit risk) vs OTC transactions or continuously linked settlements (CLS) where settlements are made in defined window of time
3. model risk - risk that poor quality of inputs and assumptions give poor quality results (think option pricing models)
4. sovereign risk - is a form of credit risk - that depends on willingness and ability of government to pay
5. regulatory risk - risk that regulations adversely change or uncertainty of how transaction will be regulated
6. accounting and legal risk - uncertainty of how rules can change and adversely impact transactions (think derivatives which are hugely subject to changing rules)
7. Political risk - changes in government that lead to any of the above risks

Question 4

3 types of other risks

Answer 4

1. ESG (environmental, social and governance) risk - is risk that company harms the env’t, human resources or corporate governance which leads to decline in firm value
2. performance netting risk
3. settlement netting risk

Question 5

How is market risk measured?

Answer 5

standard deviation for any asset price or asset’s excess returns (aka active risk)

beta for stocks

duration for bonds

delta for options

Question 6

What is VAR?

5% VAR of $1,000

Answer 6

measure of minimum expected loss or maximum expected loss (dual interpretation)

5% probability that expected minimum loss is $1,000 (no less than)

95% probability that expected maximum loss is $1,000 (no greater than)

Think 5% on the left tail of return distribution

Question 7

3 methods of computing VAR?

Answer 7

• analytical method aka variance-covariance method
• historical method
• monte carlo method

Question 8

4 advantages and disadvantages of analytical VAR

Answer 8

advantages

• standard dev and correlations needed are readily available from standard sources i.e. Ibbotson
• easy to compute unlike MC, so don’t need a lot of computer power
• allows modeling the correlations of risk
• can be applied to shorter or longer time periods

disadvantages

• need to assume normal distribution of returns so can’t perfectly model leptokurtotic returns (fat tails) OR skewed returns from options (modification can be done by delta-normal method but results are less than satisfying)
• with large portfolios with many assets, hard to estimate standard deviations and need a LOT of correlation pairs
• problem with standard sources is that they provide LT inputs which can’t be easily adjusted to calc daily or weekly VAR
• still working with historical data which is not forward looking, e.g. correlations can change quickly
• can’t easily use for assets with non-linear risks

Question 9

assumptions behind analytical method

Answer 9

1. returns are normally distributed
2. returns are serially independent
3. return distributions can be calculated using modern portfolio theory (market is in equilibrium, risk averse investors, borrow and lend at Rf, etc)

Question 10

4 advantages and disadvantages of historical VAR method

Answer 10

advantage

• easy to calculate and explain to clients
• does not assume normal distribution
• can be applied to different time periods
• no need to assume serial independence, unlike analytical method
• no need to assume modern portfolio theory holds, unlike analytical method

disadvantage

• assumes that historical pattern will repeat in future (not forward looking). This can be huge mistake for assets that change characteristics quickly overtime, such as options and bonds
• requires huge database of historical returns data which is costly to maintain
• not flexible enough to take portfolio changes into account on a real-time basis
• doesn’t allow sensitivity tests to determine how VAR is impacted by assumed changes in st dev, correlations, asset weights, etc.

Question 11

5% and 1% VAR is how many standard deviations below the mean?

Answer 11

1.645 and 2.33

Question 12

How to transfigure monthly VAR into annual VAR?

weekly VAR and daily VAR into annual VAR?

annual VAR into monthly and weekly and daily VAR?

Answer 12

E(R)

• multiply 12 or divide 12 (monthly - annual)
• multiply 52 or divide 52 (weekly - annual)
• multiply 250 or divide 250 (trading days - annual)
• multiply 22 or divide 22 (trading days - month)

standard deviation
-multiply or divide by sq root

Question 13

formula for VAR

Answer 13

Z = [ R(var) - E(R) ] / standard dev
Solve for R(var) which is in %VAR
Then multiply by portfolio value to get $ VAR

Z = standard deviations below mean, based upon alpha provided. e.g. 1.645 for 5% alpha

Question 14

3 advantages and disadvantages of Monte Carlo VAR method

Answer 14

advantages

• does not need to assume normal distribution of returns so can analyze non-linear risk and options with skewed returns
• more flexible than other methods bc analyst can specify inputs and probability distributions
• more likely to generate outlier possibilities than historical method (potential disadvantage is analyst may ignore them thinking they are unlikely to happen)

disadvantages

• output is only as good as quality of inputs. e.g. analysts get different VARs on same portfolio
• requires a great deal of mathematical modeling capability
• more variables mean more simulations that have to be run

Question 15

Why would two analysts get different VAR estimates on a portfolio using Monte Carlo VAR method?

Answer 15

using different inputs and assumptions

• 5% vs 1% alpha (or significance level)
• different time horizon (daily VAR vs monthly VAR)
• etc

Question 16

4 overall advantage and disadvantage of VAR

Answer 16

advantages

• industry standard for risk management and required by many regulators
• easy to understand because it is one number
• can be applied to capital allocation decisions
• can compare operating performance of different assets with different risk characteristics

disadvantages
-some VAR methods are difficult and costly
-doesn’t show maximum possible loss so can give false sense of security
-looks at the absolute $ risk in portfolio but doesn’t measure trade-off between return and risk so not useful to rank investment alternatives where E(R) and risk rankings are different (can only rank investments whose E(R) are equal)
-no one VAR method and different methods give different VARs for same portfolio
-can’t capture non-market risk
-sensitive to significance level chosen to represent worse case return
-can’t easily determine VAR if using a lot of structured products that are not actively traded and whose price histories aren’t known
generally more tedious to calculate as number of assets in portfolio increase

Question 17

other measures that complement VAR

Answer 17

• Incremental VAR
• cash flow at risk
• earnings at risk
• tail value at risk
• credit VAR
• stress testing
• back testing (comparing actual results across time with projections)
• sharpe ratio

Question 18

define stress testing and two forms of it

Answer 18

complement to VAR which measures impact of unusual events that might not be reflected in VAR
-2 types are scenario analysis and stressing models

Question 19

List types of scenario analysis and stressing models

Contrast scenario analysis and stressing models

Answer 19

scenario analysis types: stylized scenarios, actual extreme events and hypothetical events

stressing model types: factor push analysis, maximum loss optimization, worst-case scenario

Contrast:

• scenario analysis measures impact of simultaneous movements in multiple inputs (or factor) on portfolio value
• stressing model is an extension of scenario analysis that focus on adverse outcomes
• suffers from same problems like incorrect inputs, inability to accurately measure by-products of major factor movements, and user bias

Question 20

define the 3 types of stressing models

Answer 20

factor push analysis - simple stress test where analyst pushes factors to worst combination of possible factors and measures impact on portfolio

maximum loss optimization - uses more sophisticated mathematical and computer modeling to find this worst combination of factors

worst-case scenario - is the worst case the analyst thinks is likely to occur (i.e., worst outcome of a combination of factors)