P1.T4.326. Non-parametric volatility estimation approaches

Nicole Seaman

Director of CFA & FRM Operations
Staff member
Subscriber
AIM: Compare, contrast and calculate parametric and non-parametric approaches for estimating conditional volatility, including: ... Historic simulation; Multivariate density estimation; Hybrid methods

Questions:

326.1. Below are the ordered daily returns for the worst ten (10) returns among a window of one hundred returns (n = 100). Under Linda Allen's hybrid approach, the weights are determined by assuming a lambda parameter of 0.940.
P1.T4.326.Non-parametric_volatility_estimation_approaches.png

Which is NEAREST to 95.0% value at risk (VaR) under the hybrid approach?

a. 2.10%
b. 2.28%
c. 2.35%
d. 2.40%

326.2. The multivariate density estimation (MDE) method estimates conditional volatility according to the following function:

P1.T4.326.Non-parametric_volatility_estimation_approaches_02.png


Each of the following is TRUE about this multivariate density estimation (MDE) approach to volatility EXCEPT which is false?

a. MDE is similar to GARCH because it assigns different (i.e., non-constant) weights to a series of of historical (squared) returns
b. MDE is similar to GARCH because distant observations receive less weight than recent observations
c. MDE is different than GARCH because the weighting (kernel) function is not a function of time
d. MDE is flexible because user can specify economic state variables, but it is thusly data intensive

326.3. Analyst Scott is employing a hybrid approach (i.e., hybrid of HS and EWMA) in order to estimate volatility. His historical window includes 100 return observations. What is the effect on the hybrid 95.0% value at risk (VaR) if he reduces the lambda parameter from 0.94 to 0.88?

a. The VaR decreases; i.e., the interpolated loss return shifts from more negative to less negative
b. No impact on the VaR
c. The VaR increases; i.e., the interpolated loss return from less negative to more negative
d. Unclear, depends on dataset

Answers:
 

Keshav

Member
Hi David, could you explain the solution to the first question? For 95% var, should we not look at the 5th & 6th lowest returns instead of the 6th & 7th?
 

David Harper CFA FRM

David Harper CFA FRM
Subscriber
Hi @Keshav If this were a simple historical simulation, which assumes that each sorted return carries equal weight regardless of when it occurred, the midpoint between the 5th and 6th (i.e., -2.35%) is a smart choice for 95.0% VaR; this would be a technical choice that assumes the 1.0% weight of each return "straddles" the return, as a random variable, such that -2.40% is actually a midpoint between -2.35% at 5.0% cumulative and -2.45% at 4.0% cumulative (but, again, under the assumption of simple historical simulation, which is not even displayed in the question!).

The hybrid approach assigns weights based on recency (periods ago; i.e., days ago). The 6th worst return of -2.30% only corresponds to a cumulative weight of 4.85% such that the 5.0% is not reached. Consequently, the answer here is between the 6th and 7th because 5.0% is located between 4.85% and 6.05%. I hope that helps!
 

Nicole Seaman

Director of CFA & FRM Operations
Staff member
Subscriber
Hello @Keshav

I just wanted to make sure that you were able to click on the "Answers here in forum" link to get to the answers and their explanations. If you were not able to post your question on the answer page, please let me know because this can indicate that your forum permissions need to be reset. ;)

Thank you,

Nicole
 
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