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I found the Part II exam to be much easier than the Part I exam, even though I thought the material in Part II to be more difficult. The calculation questions, with the exception of a few, were very simple. If you knew that the hazard rate = spread/(1-RR) you got 3-4 questions right there! There...

Ah, ok. Thanks for digging into that.

That is my assumption, actually. Take a look at the document above. The ES is 6027 for observation 199. This implies ES is calculated as (2988+3039)/1. It should be (2988+3039)/2.

Correct. Even still, in the document I posted up above. The VaR is being calculated as the 11th worst loss and ES is being calculated as the sum of losses 1-10 divided by 9 (instead of divided by 10).

I agree that for the 95% cuttoff for VaR, there are 4 observations in the tail. But those observations need to be averaged, not summed and divided by 3. I believe I may have found the answer from a while back where David recognizes an error...

Hello, In reviewing the Part 2 Formula sheet, I had a question regarding the ES calculation on page 21. It appears that ES is calculated by summing from the confidence level of interest and up and dividing by not "n" but "n-1" VaR points. Why is this not calculated by dividing by "n" points...

Hi All, I plan to start studying for FRM Part 2 (May 20th, 2017) next week and was curious if there was a certain order that people prefer to study the material. Some options I've considered: 1. Topic guide on GARP's site 2. Order of BT's study planner 3. Mix it up! In addition, I put...

Hi Nicole, Where on the study notes/question sets can we find the date the notes were last updated? Thanks, Mike

Great. Thanks for your help!

Thanks! Is this an exam requirement? I didn't see it related to an LO in the readings. Robust Standard errors come up when discussing this LO: LO: Evaluate the implications of homoskedasticity and heteroskedasticity.

Hi, I was wondering if on the exam we are required to know how to calculate standard errors assuming both homoskedasticity and heteroskedasticity for linear regression. Is there a formula for the latter? If not, I assume we just need to understand when to use robust standard errors (when...