R40: Measuring and Managing Market Risk Flashcards
Value at Risk (VaR)
- the minimum loss that would be expected a certain percentage of the time over a certain time period, given assumed market conditions
- meant to capture market risk for equity prices, interest rates, exchange rates, and commodity prices.
VaR at 1%
2.33 sd
VaR at 1sd
16% VaR
VaR at 5%
1.65 sd
parametric method
- generally assumes distribution of returns on risk factors is normal
- simple & straightforward
- VaR sensitive to E(R) and sd
- difficult to use if the portfolio contains options as that threatens normality
historical simulation method process
- for certain time period (ie 2 years), weight the portfolio every day, calculate a daily return for each day
- 500 hundred observations, calculate the day to day losses and rank them
- then take percentiles (5% VaR), that’s the VaR
historical simulation method notes
- what if you a bond today that didn’t exist 2 years ago,
- a lot of modifications, use of proxies
- not constrained by assumption of normality
- estimation of VaR using what actually happened (while keeping in mind that the past may not repeat itself)
monte carlo simulation
- same method of ranking losses
- not constrained by any distribution
- avoids complexity of the parametric method when the portfolio has many risk factors
Conditional VaR
- relies on a particular VaR
- it is the average loss if the VaR loss is exceeded
- best derived using historical simulation and monte carlo
Incremental VaR
- how a VaR will change if a position size is changed relative to the remaining position
- before vs after calculation
Marginal VaR
- conceptually the same as incremental VaR, but for very small change in position
- change in VaR given a $1 or 1% change
Relative VaR
- ex-ante tracking error
- the degree to which the performance of a given portfolio might deviate from its benchmark
- (portfolio holdings - benchmark holdings): active positions
Sensitivity Risk Measures
- examines how performance responds to a single change in an underlying risk factor
- equities: factor sensitivities
- fixed income: duration and convexity
- options: delta, gamma, and vega
Scenario risk measures
- estimates the portfolio return that would result from a hypothetical change in markets or a repeat of a historical event.
- multiple factor movements (i.e vega, delta is at a point in time)
Historical Scenario Measures
- portfolio values are re-measured
- equities using historical prices to model behavior
- fixed income- re-priced based on conditions that pre-vailed at the time, using the inputs to a valuation model that existed at that time (interest rates, credit spreads, etc)
- scenario is run as if total price action across the scenario period happened instantaneously.
Output to historical scenario measures
- total return
- total return vs benchmark
- extra collateral/cash requirements (effects of margin calls and the cascading effects)
Modified approach to hist. scenario measures
-staggered scenario which allows expected management action
Hypothetical scenarios
- imagined scenarios
- those that have never been experienced historically
- justification: the past doesn’t repeat itself exactly
- reverse stress test: start with exposure, and then determine an event that causes exposure
- design rare, but not impossible scenario (earthquake, war)
- goal is to understand risk, not eliminate them
Limits placed on risk measurements
- risk budgeting
- position limits
- scenario limits
- stop-loss limits
Risk budgeting
- total risk allocated to sub-activities
- VaR 4 million: budget 2m to market risk, 1.5m to credit risk, 0.5m to operational risk
Position limits
-control on over-concentration
Scenarion limits
-a limit on the loss for a given scenario
stop loss limit
-if a certain threshold for losses is breached during a specific period, liquidate or reduce position