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发表于 201242 13:35
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Sheila Myers, CFA, has recently been promoted from analyst to Senior Vice President of Risk Management at Treetop Investment Inc. Myers recently attained her CFA charter. While studying for the exams, she became very interested in risk measurement and management. Previously, the focus of her career was on fundamental equity analysis.
Myers recently attended a conference on risk measurement techniques including the concept of value at risk (VAR). She learned that many managers and finance professionals are using VAR as a measure of asset, project, and portfolio risk. Rick Bishop, the key presenter at the conference on topics related to VAR, defined VAR as “the minimum amount of money that a firm could expect to lose with a given probability over a specific period of time.” One participant asked “I thought VAR was the maximum loss the firm could expect. Am I incorrect in this assumption?” Bishop replied that in its most basic form, VAR is defined as the largest potential portfolio loss over a given period of time with a certain level of probability. He went on to explain that a portfolio manager might compute the value at risk for his portfolio over the next 3 months at $5 million with 1 percent probability. What this means is that over the next 3 months, there is a 1 percent probability that the portfolio will lose $5 million or more. Alternatively, it can be said that over the next three months there is a 99 percent chance that the most the portfolio will lose is $5 million.
Sarah George asked Bishop “Is VAR comparable across various asset classes managed by the firm?” A second participant, Ben Cooper, says that he has heard that VAR is “relatively incomparable across managers”.
Myers attended a session on the use of VAR to evaluate credit risk. The session leader, Justin Banks, said that while it is possible to use VAR in credit risk analysis, the interpretation is somewhat different. He said, “Credit risk increases as the value of positions held increases.” Myers then replied “I see what you’re implying. We must thus focus on the lower tail of the distributions of gains on positions held when using VAR to evaluate credit risk.”
Blake Smith held a panel session on stress testing. He indicated that the best use of stress testing in VAR analysis is to “vary the inputs to the VAR estimation process a little bit and analyze the impact of this movement on the computed VAR.” Georgia Burns said that it is “stress testing the return generating process used to develop the scenarios or paths in Monte Carlo analysis”.
An entire session was devoted to estimating VAR. There are several methods that may be used including the historical method, the Monte Carlo simulation method, and the variancecovariance method. Session panel members were asked to discuss the advantages and disadvantages of each method of estimation. Jane Blatt said “the key disadvantage of the historical method is that we have to assume normally distributed returns.” Jim McAdams said “a key advantage of the Monte Carlo simulation method is that it can accommodate the required assumptions for complex relationships.” Finally, Beth Berry said “the key disadvantage of the variancecovariance method is that it assumes that past performance is representative of what can occur in the future.”
After the seminar, Myers was intrigued by the power of VAR but was apprehensive about actually adopting VAR as a risk measurement tool. She asked Bishop to identify the most fundamental problem with estimating VAR.Bishop, in response to George’s question regarding comparability across asset classes, is most likely to respond that VAR: A)
 does not measure risk comparably across asset classes. 
 B)
 measures risk comparably across asset classes that have normal distributions (i.e., there are no embedded options). 
 C)
 measures risk comparably across asset classes. 

VAR measures risk comparably across asset classes. The result is that with VAR, the risk of a bond portfolio can be compared against the risk of an equity portfolio. It is quite versatile in a portfolio management context. This is one of VAR’s key strengths. (Study Session 14, LOS 34.g)
In response to Cooper’s statement regarding VAR’s incomparability across managers, Myers is most likely to: A)
 agree and add that it is because of the complexity of the calculations involved. 
 B)
 disagree and add that the characteristics of a competitor's portfolio can be estimated through VAR modeling techniques. 
 C)
 agree and add that this is due to its inherent model risk. 

VAR is relatively incomparable across managers due to its inherent model risk. For example, two people can be given an assignment to compute the VAR for the same underlying asset and the results will likely be different due to the use of different methodologies and model assumptions. Neither answer is necessarily wrong. The bottom line here is that peer group evaluation using VAR is not very useful unless one can be sure that the same VAR techniques and assumptions are used to evaluate all portfolios. (Study Session 14, LOS 34.g)
With respect to the use of stress testing in VAR analysis, Burns and Smith are, respectively:
Burns is incorrect and Smith is incorrect. A particular VAR estimate is based on a given model and its parameters. In stress testing (or scenario analysis), the analyst varies the inputs to the VAR estimation process sometimes to the extreme and analyzes the impact of this movement on the computed VAR. Stress testing is "what if" analysis, and its main contribution is that it shows how reliable a particular VAR estimate is. (Study Session 14, LOS 34.h)
In response to Myers’ question about the most fundamental problem associated with estimating VAR, Bishop is most likely to reply that the main problem is: A)
 the lack of available data to compute VAR. 
 B)
 the inability to accurately derive the "true" probability distribution for the asset or portfolio under evaluation. 
 C)
 that VAR calculations depend on symmetrical payout profiles. 

The fundamental problem with VAR analysis is that the analyst must estimate the "true" probability distribution for the asset or portfolio under evaluation. This means that in order to give the analyst reliable results, the quantitative model must accurately describe the price process of the asset. (Study Session 14, LOS 34.g)
Regarding credit risk and VAR, Banks and Myers are, respectively:
Banks is correct but Myers’ conclusion is incorrect. Since credit risk increases when the value of the position held increases, we should focus on the upper not lower tail of the distributions of gains on positions held. (Study Session 14, LOS 34.g)
McAdams, Blatt and Berry are, respectively: A)
 correct; correct; incorrect. 
 B)
 correct; incorrect; incorrect. 
 C)
 incorrect; correct; incorrect. 

A key advantage of Monte Carlo simulation is the ability to deal with the assumptions required to handle complex relationships. McAdams’ statement is correct. The key advantage of the historical method is that you do not have to assume a particular distribution. Therefore, Blatt is incorrect. A major disadvantage of the historical method is that we have to assume that past performance is representative of future performance; it is not a disadvantage of the variancecovariance method. Therefore, Berry is also incorrect. (Study Session 14, LOS 34.f) 
