VaR Flashcards

1
Q

Which tail is used for VaR?

A

Left

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2
Q

When did computers add calculation muscle to risk?

A

1980

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3
Q

two types of financial institutions that primarily drive risk

A

private equity and hedge funds

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4
Q

5 steps of enterprise risk management

A
  1. risk transparency and insight
  2. natural ownership and risk strategy
  3. risk capacity
  4. risk-related decisions and processes
  5. risk organization and governance
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5
Q

investment bank that risks the most money

A

goldmann sachs

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6
Q

when did G10 agree to set minimum capital requirements?

A

1988

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7
Q

when were banks allowed to use their own proprietary models?

A

1995

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8
Q

VaR level with 95% confidence

A

“I am 95% confident that I will not lose more than $xx in one day”

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9
Q

what does a closed form of VaR assume?

A
  • normal distribution

- distribution with a specific mean and standard deviation

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10
Q

what does closed form of VaR explicitly determine?

A

the p/l probability distribution for a portfolio

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11
Q

what does closed form VaR implicitly determine?

A

the standard deviation and correlation of p/l

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12
Q

confidence interval recommended by Basle Committee?

A

99 percent

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13
Q

properties of a normal curve

A
  • perfectly symmetric
  • no skew
  • mean = 0
  • kurtosis = 3 x variance squared
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14
Q

what do fat tails suggest

A

a higher chance of very high or very low prices

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15
Q

when is fat tails classified

A

when kurtosis > 3

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16
Q

are options linear or non-linear

A

options are non linear , price of option doesn’t move 1 to 1 with the price of the underlying

17
Q

how are portfolios with optionality measured?

A

by using simulations/estimations of VaR

18
Q

factors effecting simulations/estimations of VaR

A

options (delta, theta, gamma)
bonds (complexity and duration)
equities (beta)

19
Q

curse of dimensionality

A

multiple risk factors create exponential layers of calculations

20
Q

models that estimate VaR and open-form equations

A

Historical and Monte Carlo

historical uses past events to predict the probability of future events

monte carlo incorporates all risk factors into complex calculations

21
Q

error that occurs with Monte Carlo VaR

A

specification error = random sampling of risk metrics doesn’t represent true distribution

convergence error = not enough sampling is done

22
Q

VaR model to use if no optionality

A

Parametric (variance or covariance) VaR

23
Q

VaR model with curse of dimensionality

A

Monte Carlo

24
Q

distribution kurtosis type for enegry markets? opposite?

A
Leptokurtotic = woody, concentrated center with fat tails
opposite = platokurtotic
25
two types of VaR model likely to give errors in energy market
parametric and historical
26
concepts to consider in overcoming VaR limitations
- use stress tests and scenario analysis | - VaR gives probability of a loss that could occur past a certain $xx, but doesn't specify how far $xx that loss will go
27
3 types of stress tests
1. historical scenarios (using past tail risk events) 2. mechanical stress tests (yield curve shifts, forward price curve shifts, changes in volatility curve) 3. hypothetical scenarios (user-designed)
28
shocks that can be used in stress tests
``` parallel= equal shocks given across the curve twist = equal shocks given to high/low end of curve but with different signs (+, -) curvature = equal shocks given to high/low end of curve but with the same sign. same shock is also applied to the center of the curve but w/ a different sign ```
29
problems with stress tests
1. subjective 2. no probability 3. information overload