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Free CFA-Level-III Exam Braindumps (page: 46)

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Paul Dennon is senior manager at Apple Markets Associates, an investment advisory firm. Dennon has been examining portfolio risk using traditional methods such as the portfolio variance and beta. He has ranked portfolios from least risky to most risky using traditional methods.
Recently, Dennon has become more interested in employing value at risk (VAR) to determine the amount of money clients could potentially lose under various scenarios. To examine VAR, Paul selects a fund run solely for Apple's largest client, the Jude Fund. The client has $100 million invested in the portfolio. Using the variance-covariance method, the mean return on the portfolio is expected to be 10% and the standard deviation is expected to be 10%. Over the past 100 days, daily losses to the Jude Fund on its 10 worst days were (in millions): 20, 18, 16, 15, 12, 11, 10, 9, 6, and 5. Dennon also ran a Monte Carlo simulation (over 10,000 scenarios). The following table provides the results of the simulation:

Figure 1: Monte Carlo Simulation Data

The top row (Percentile) of the table reports the percentage of simulations that had returns below those reported in the second row (Return). For example, 95% of the simulations provided a return of 15% or less, and 97.5% of the simulations provided a return of 20% or less.
Dennon's supervisor, Peggy Lane, has become concerned that Dennon's use of VAR in his portfolio management practice is inappropriate and has called for a meeting with him. Lane begins by asking Dennon to justify his use of VAR methodology and explain why the estimated VAR varies depending on the method used to calculate it. Dennon presents Lane with the following table detailing VAR estimates for another Apple client, the York Pension Plan.

To round out the analytical process. Lane suggests that Dennon also incorporate a system for evaluating portfolio performance. Dennon agrees to the suggestion and computes several performance ratios on the York Pension Plan portfolio to discuss with Lane. The performance figures are included in the following table. Note that the minimum acceptable return is the risk-free rate.
Figure 3: Performance Ratios for the York Pension Plan


Using the historical data over the past 100 days, the 1-day, 5% VAR for the Jude Fund is closest to:

  1. $11 million.
  2. $12 million.
  3. $15 million.

Answer(s): B

Explanation:

The number that is the 5th worst day for the Fund out of the 100-day period is considered the 5% VAR (95% of returns were better). The ten worst returns over the last 100 days were -20, -18, -16, -15, -12, -II, -10, -9, -6, and -5. Counting up from the very worst return (-20), we see 12 is fifth from the bottom, so $12,000,000 is the 5%, 1-day VAR. (Study Session 14, LOS 40.e, 0



Paul Dennon is senior manager at Apple Markets Associates, an investment advisory firm. Dennon has been examining portfolio risk using traditional methods such as the portfolio variance and beta. He has ranked portfolios from least risky to most risky using traditional methods.
Recently, Dennon has become more interested in employing value at risk (VAR) to determine the amount of money clients could potentially lose under various scenarios. To examine VAR, Paul selects a fund run solely for Apple's largest client, the Jude Fund. The client has $100 million invested in the portfolio. Using the variance-covariance method, the mean return on the portfolio is expected to be 10% and the standard deviation is expected to be 10%. Over the past 100 days, daily losses to the Jude Fund on its 10 worst days were (in millions): 20, 18, 16, 15, 12, 11, 10, 9, 6, and 5. Dennon also ran a Monte Carlo simulation (over 10,000 scenarios). The following table provides the results of the simulation:

Figure 1: Monte Carlo Simulation Data

The top row (Percentile) of the table reports the percentage of simulations that had returns below those reported in the second row (Return). For example, 95% of the simulations provided a return of 15% or less, and 97.5% of the simulations provided a return of 20% or less.
Dennon's supervisor, Peggy Lane, has become concerned that Dennon's use of VAR in his portfolio management practice is inappropriate and has called for a meeting with him. Lane begins by asking Dennon to justify his use of VAR methodology and explain why the estimated VAR varies depending on the method used to calculate it. Dennon presents Lane with the following table detailing VAR estimates for another Apple client, the York Pension Plan.

To round out the analytical process. Lane suggests that Dennon also incorporate a system for evaluating portfolio performance. Dennon agrees to the suggestion and computes several performance ratios on the York Pension Plan portfolio to discuss with Lane. The performance figures are included in the following table. Note that the minimum acceptable return is the risk-free rate.
Figure 3: Performance Ratios for the York Pension Plan


Using the Monte Carlo simulation method, the 1-day, 5% VAR for the Jude Fund is closest to:

  1. $15 million.
  2. $18 million.
  3. $20 million.

Answer(s): B

Explanation:

The 5th percentile return for the Fund from the Monte Carlo Simulation was -18%, indicating that 95% of the time, the Funds return would have exceeded -18% and 5% of the time, the Fund's return would have fallen below-18%. Thus, the VAR using a 95% probability (5% significance) is $100 million times 18% = $18 million. (Study Session 14, LOS 40.c,f)



Paul Dennon is senior manager at Apple Markets Associates, an investment advisory firm. Dennon has been examining portfolio risk using traditional methods such as the portfolio variance and beta. He has ranked portfolios from least risky to most risky using traditional methods.
Recently, Dennon has become more interested in employing value at risk (VAR) to determine the amount of money clients could potentially lose under various scenarios. To examine VAR, Paul selects a fund run solely for Apple's largest client, the Jude Fund. The client has $100 million invested in the portfolio. Using the variance-covariance method, the mean return on the portfolio is expected to be 10% and the standard deviation is expected to be 10%. Over the past 100 days, daily losses to the Jude Fund on its 10 worst days were (in millions): 20, 18, 16, 15, 12, 11, 10, 9, 6, and 5. Dennon also ran a Monte Carlo simulation (over 10,000 scenarios). The following table provides the results of the simulation:

Figure 1: Monte Carlo Simulation Data

The top row (Percentile) of the table reports the percentage of simulations that had returns below those reported in the second row (Return). For example, 95% of the simulations provided a return of 15% or less, and 97.5% of the simulations provided a return of 20% or less.
Dennon's supervisor, Peggy Lane, has become concerned that Dennon's use of VAR in his portfolio management practice is inappropriate and has called for a meeting with him. Lane begins by asking Dennon to justify his use of VAR methodology and explain why the estimated VAR varies depending on the method used to calculate it. Dennon presents Lane with the following table detailing VAR estimates for another Apple client, the York Pension Plan.

To round out the analytical process. Lane suggests that Dennon also incorporate a system for evaluating portfolio performance. Dennon agrees to the suggestion and computes several performance ratios on the York Pension Plan portfolio to discuss with Lane. The performance figures are included in the following table. Note that the minimum acceptable return is the risk-free rate.
Figure 3: Performance Ratios for the York Pension Plan


Evaluate Dennon's comments regarding the VAR estimates provided by the historical and analytical VAR methods. Dcnnon is:

  1. correct only with respect to historical VAR.
  2. correct only with respect to analytical VAR.
  3. correct with respect to both historical and analytical VAR.

Answer(s): A

Explanation:

The historical method does not make any assumptions about the underlying distribution of returns (i.e., normal, skewed, etc.) but does assume that future returns will be distributed similarly to past returns. This may or may not be a reasonable assumption. If future volatility is expected to be greater than past volatility, the distribution of returns will be wider, and the value at risk should increase. Thus, Dennon's comment regarding the historical method is correct.
The analytical method (also called the variance-covariance method) assumes that the distribution of returns on the portfolio is normal. Once again this may or may not be a true reflection of the actual distribution of portfolio returns. If the distribution of returns is negatively skewed (i.e., long left tail with large losses), the VAR estimate using the analytical method will underestimate the probability of a very large loss. The VAR estimate would be biased downward (i.e., would be too low) rather than upward as stated by Dennon. Thus, Dennon's comment regarding the analytical method is incorrect. (Study Session 14, LOS 40.f)



Paul Dennon is senior manager at Apple Markets Associates, an investment advisory firm. Dennon has been examining portfolio risk using traditional methods such as the portfolio variance and beta. He has ranked portfolios from least risky to most risky using traditional methods.
Recently, Dennon has become more interested in employing value at risk (VAR) to determine the amount of money clients could potentially lose under various scenarios. To examine VAR, Paul selects a fund run solely for Apple's largest client, the Jude Fund. The client has $100 million invested in the portfolio. Using the variance-covariance method, the mean return on the portfolio is expected to be 10% and the standard deviation is expected to be 10%. Over the past 100 days, daily losses to the Jude Fund on its 10 worst days were (in millions): 20, 18, 16, 15, 12, 11, 10, 9, 6, and 5. Dennon also ran a Monte Carlo simulation (over 10,000 scenarios). The following table provides the results of the simulation:

Figure 1: Monte Carlo Simulation Data

The top row (Percentile) of the table reports the percentage of simulations that had returns below those reported in the second row (Return). For example, 95% of the simulations provided a return of 15% or less, and 97.5% of the simulations provided a return of 20% or less.
Dennon's supervisor, Peggy Lane, has become concerned that Dennon's use of VAR in his portfolio management practice is inappropriate and has called for a meeting with him. Lane begins by asking Dennon to justify his use of VAR methodology and explain why the estimated VAR varies depending on the method used to calculate it. Dennon presents Lane with the following table detailing VAR estimates for another Apple client, the York Pension Plan.

To round out the analytical process. Lane suggests that Dennon also incorporate a system for evaluating portfolio performance. Dennon agrees to the suggestion and computes several performance ratios on the York Pension Plan portfolio to discuss with Lane. The performance figures are included in the following table. Note that the minimum acceptable return is the risk-free rate.
Figure 3: Performance Ratios for the York Pension Plan


Which of the following correctly assesses Dennon's comment regarding the Monte Carlo Simulation method of estimating VAR?

  1. Monte Carlo VAR analysis is limited to normal or near-normal distribution assumptions for input variables.
  2. The convergence value for the Monte Carlo model should have been stated as $26 million rather than $19 million.
  3. Outputs from the Monte Carlo model are not forward looking since the major assumptions used in the model are based on historical data.

Answer(s): B

Explanation:

The Monte Carlo Simulation Method allows a great deal of flexibility in calculating VAR. The model allows the specification of any distributional assumptions that are appropriate and generally uses expectations data to generate a random series of forward-looking simulations that arc then compiled and analyzed to determine the VAR estimate at a given probability level. If a normal distribution was assumed to hold for all relevant variables, then over a very large number of simulations, the VAR estimate would converge to that determined by the analytical method (which also assumes a normal distribution), in this case a value of $26 million. (Study Session 14, LOS 40.f)






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