VRM 3 Flashcards

1
Q

What does 𝑉𝐴𝑅 represent?

A

Maximum potential loss in value of a portfolio of financial instruments with a given probability over a certain time horizon.

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2
Q

What is the implication of a 95% daily VAR of $100 million?

A

95% of the time, daily loss will be less than or equal to $100 million.

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3
Q

What is the formula for calculating Dollar π‘‰π‘Žπ‘…x%?

A

Dollar $π‘‰π‘Žπ‘…x% = 𝑧x% Γ— 𝜎 Γ— Portfolio Value.

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4
Q

What are the two main types of distributions discussed?

A

Conditional and unconditional distributions.

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5
Q

What does the term β€˜fat tails’ refer to in return distributions?

A

Return distributions that exhibit more extreme outcomes than a normal distribution would predict.

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6
Q

Which model is used to estimate long horizon volatility?

A

𝐺𝐴𝑅𝐢𝐻(1,1) model.

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7
Q

True or False: Conditional volatility can be calculated using both parametric and non-parametric approaches.

A

True.

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8
Q

What is the impact of mean reversion on volatility estimation?

A

Mean reversion affects long horizon conditional volatility estimation.

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9
Q

What is the Exponentially Weighted Moving Average (πΈπ‘Šπ‘€π΄) approach used for?

A

To estimate volatility.

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10
Q

What is the implication of regime switching on quantifying volatility?

A

Regime switching presents additional challenges for risk managers due to unanticipated changes.

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11
Q

Fill in the blank: 𝑉𝐴𝑅 is a measure of potential loss with a certain _______.

A

[probability]

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12
Q

What are the two approaches for estimating π‘‰π‘Žπ‘… mentioned?

A
  • Historical based approach
  • Implied volatility based approach
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13
Q

What does the parametric approach assume about asset returns?

A

Asset returns are normally or lognormally distributed with time-varying volatility.

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14
Q

What happens to the return distribution during periods of market stress?

A

Volatility tends to increase.

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15
Q

What is a common method for estimating current volatility?

A

Using recent historical data.

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16
Q

What can lead to an unreliable estimate of current volatility?

A

Using data from too long ago or including outliers.

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17
Q

What are the two types of changes in volatility described?

A
  • Slow changes
  • Regime switching
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18
Q

True or False: A mixture of two normal distributions can create fat tails.

A

True.

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19
Q

What is the formula for calculating standard deviation from sample data?

A

𝜎 = √(1/m Ξ£ (π‘Ÿn–i)Β²)

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20
Q

What does the term β€˜unconditional normality’ refer to?

A

Probability distribution of the return each day has the same normal distribution.

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21
Q

What does a conditionally normal model improve upon?

A

Assuming returns are constantly normal.

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22
Q

What is the relationship between volatility and asset return distribution during high volatility?

A

Daily return is normal with a high standard deviation.

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23
Q

Fill in the blank: The historical simulation is a type of ______ approach for estimating π‘‰π‘Žπ‘….

A

[Non-Parametric]

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24
Q

What is the significance of the correlation coefficient in a two-asset portfolio?

A

It affects the combined π‘‰π‘Žπ‘… calculation.

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25
Q

What does EWMA stand for?

A

Exponentially Weighted Moving Average

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26
Q

What is the primary purpose of using EWMA?

A

To overcome problems of estimating volatilities of market variables.

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27
Q

In EWMA, how are the weights applied to squared returns determined?

A

Weights are determined by a persistence factor (Ξ») multiplied by the weight applied to the previous squared return.

28
Q

What is the value of Ξ» that Risk Metrics found to work well in the 1990s?

29
Q

True or False: In EWMA, the weights given to all squared returns must be equal.

30
Q

What is the effect of a higher Ξ» in EWMA?

A

Gives more weight to older data.

31
Q

What is the weight given to the latest squared return in EWMA called?

A

Reactive factor

32
Q

How does the new estimate of the variance rate on day n get calculated in EWMA?

A

It is a weighted average of the previous day’s variance estimate and the most recent squared return.

33
Q

What does the term β€˜mean reversion’ refer to in the context of GARCH models?

A

The tendency of volatility to revert toward the long-run average mean.

34
Q

In GARCH(1,1), what do the parameters Ξ±, Ξ², and Ξ³ represent?

A
  • Ξ±: weight for the most recent squared return
  • Ξ²: weight for the previous variance rate estimate
  • Ξ³: weight for the long-run average variance rate
35
Q

Fill in the blank: In the GARCH model, the unconditional variance has been normalized to _______.

36
Q

What is the implication of having a Ξ» value much higher than 0.94 in EWMA?

A

It would make EWMA relatively unresponsive to new data.

37
Q

What does the term β€˜decay factor’ refer to?

A

Ξ» in the context of EWMA

38
Q

What happens to estimates if Ξ» is set to a low value (e.g., 0.5)?

A

Estimates would overreact to new data.

39
Q

What is the relationship between EWMA and GARCH(1,1)?

A

EWMA is a particular case of GARCH(1,1) where Ξ³ = 0.

40
Q

What is the significance of the weights in GARCH models?

A

Weights must sum to one and determine the influence of past returns and variance.

41
Q

What is the expected variance rate on day t in GARCH?

A

σ² = VL + Ξ± + Ξ² σ²(n-t) - VL

42
Q

What are the three main nonparametric methods used to estimate VaR?

A
  • Historical simulation
  • Hybrid approach
  • Multivariate density estimation
43
Q

True or False: Nonparametric methods require assumptions about the entire distribution of returns.

44
Q

What is the square root rule in the context of volatility?

A

Assumes that the variance rate over T days is T times the variance rate over one day.

45
Q

What does multivariate density estimation (MDE) focus on?

A

Determining weights based on periods in the past that are most similar to the current period.

46
Q

In GARCH(1,1), what does the notation (1,1) signify?

A

Weight is given to one most recently observed squared return and one most recent variance rate estimate.

47
Q

What does EWM stand for in the context of weighting historical data?

A

EWM stands for Exponentially Weighted Moving Average.

EWM applies decreasing weights to historical data as one moves back in time.

48
Q

What is the main concept behind multivariate density estimation (MDE)?

A

MDE determines weights based on the similarity of historical periods to the current period.

Weights are assigned according to how similar past days are to the current day.

49
Q

How does volatility of interest rates behave in relation to interest rate levels?

A

Volatility tends to decrease as interest rates increase.

50
Q

What are conditioning variables in the context of MDE?

A

Conditioning variables are additional variables used to assess similarity between periods, such as GDP growth and interest rate levels.

51
Q

In the formula for similarity in MDE, what does X* represent?

A

X* represents the value of Xi today.

52
Q

What is a disadvantage of using MDE?

A

MDE may lead to over-fitting of data.

53
Q

What is another disadvantage of MDE?

A

MDE requires a large amount of data.

54
Q

What is Value at Risk (VaR)?

A

VaR is a statistical measure that estimates the potential loss in value of an asset or portfolio with a specified confidence level.

55
Q

What is the expected shortfall in risk management?

A

Expected shortfall is the average loss given that a loss is beyond the VaR threshold.

56
Q

What does implied volatility indicate?

A

Implied volatility indicates the average volatilities expected over the life of an option.

57
Q

What is the VIX index?

A

The VIX is an index of the implied volatilities of 30-day options on the S&P 500.

58
Q

What typical range does the VIX index fall within?

A

The VIX typically ranges from 10 to 20.

59
Q

True or False: Correlations should be monitored alongside volatilities in risk management.

60
Q

What is the formula used for updating covariance in EWM?

A

covn = Ξ»covn–1 + (1 - Ξ») xn–1yn–1.

61
Q

How is the correlation between two variables calculated?

A

Correlation is calculated as the covariance divided by the product of their standard deviations.

62
Q

What is the relationship between the coefficient of correlation and covariance?

A

Covariance is the coefficient of correlation multiplied by the product of the standard deviations.

63
Q

Fill in the blank: The covariances can be updated using the _______ model.

A

EWM model.

64
Q

What does GARCH stand for?

A

Generalized Autoregressive Conditional Heteroskedasticity.

65
Q

What is one complexity involved in using GARCH for updating covariances?

A

Using GARCH to update multiple covariances in a consistent way is complex.