So could I be the refere ? and the winner is......................................................Aleksander with the long run volatility (not gamma). Don't forget to pay the referee guys

You will definitely get you commission but don't you thnk you need to prove it 1st I won't lose $100 so easily

I read the question like this: Garch (1:1) .... what's the long run volatility used in the model. you just have to square root the long run variance given in the information.

Yes. you are totally right,if that is the question.. but the point of contention is the question itself ... And i think you are refering to a different question altogether here.. the 1 i am talking abt is the 1 where they gace A/B/C/D and asked which 1 has max weight on long-run variance and the options were just A/B/C/D ..no numbers !! Anywas - Now I hardly see any resolution coming out of it unless GARP testifies

are we talking about different questions by any chance? I'll definitely bet a $100 that GARP will not testify and give out their answer/solution

Anyway Guys, I still remembered some questions : 1) In normal distribution what is happen if number of sample is enlarged? a . mean sample will be smaller b. standard deviation will be larger c. Mean sample close to 0 d. standard deviation close to 0 2)In Geometric Brown Motion, what is the consistent distribution to determine Future distribution? a. if return normal dis then future also normal dis b. if return log normal dis then future normal dis c. .... d. b. if return log normal dis then future also log normal dis what do you think? Thanks

2) d. but the question is stupid: No matter what the distributions of one-period returns may be, the long-term compounded values tend to be lognormally distributed if returns are independent. . This is the case for GBM.

Anyone disagree on the #1? hmm..I think I chose C. When comparing C and D, I thought that standard error close to 0, not standard deviation.

Question 1 Fact 1: If you take a large number of random samples of size n from the same population, and obtain the mean of any variable X in all the samples, these sample means will have a normal distribution around the population mean. (CTL & LLN) Fact 2) The closeness to the theoretical normal distribution will increase if you enlarge sample size or you increase the number of samples taken. Fact 3) Note that fact 1 & 2 says nothing about the distribution of variables in the population or within each sample. Fact 4) When the sample size n is large, the sample average Y_hat is normally distributed with mean μY and standard deviation σY_hat ≡ σY /sqrt(n); or equivalently, [(Y_hat − μY)/σY_hat] ∼N(0,1) Fact 5): the precision of Y_hat depends entirely on the absolute size of the sample n, not whether n is large relative to the size of the population. Fact 6): The spread of the data Y (population) will not change as the sample size grows. The standard deviation of Y_hat will shrink as the sample size grows because we become more certain about the true value of the mean as the sample size grows.

This is how I solved it: y = Rp - Rf x = Rm - Rf m = beta The eqn was of the form y = mx + c On substituting all the terms: Rp = Rf + beta (Rm - Rf) + c the first two terms together consitute the CAPM return. Since alpha is the absolute excess over the CAPM return, the answer is the constant value denoted by c. I guess , it was something like 3.x or so. I think the R2 was a reduntant piece of info..

This is how I solved it: y = Rp - Rf x = Rm - Rf m = beta The eqn was of the form y = mx + c On substituting all the terms: Rp = Rf + beta (Rm - Rf) + c the first two terms together consitute the CAPM return. Since alpha is the absolute excess over the CAPM return, the answer is the constant value denoted by c. I guess , it was something like 3.x or so. I think the R2 was a reduntant piece of info..

This is how I solved it: y = Rp - Rf x = Rm - Rf m = beta The eqn was of the form y = mx + c On substituting all the terms: Rp = Rf + beta (Rm - Rf) + c the first two terms together consitute the CAPM return. Since alpha is the absolute excess over the CAPM return, the answer is the constant value denoted by c. I guess , it was something like 3.x or so. I think the R2 was a reduntant piece of info..

Honestly, with a month gone by the exam day.. i hardly rem a single question... this soln looks plausible When are the results out ?

July 6th. I am eagerly waiting. Any idea, what could be the cut off?. I get mixed answers. Some say 60, some 67 plus....

yes 6th it is..no idea about cutoff though We have argued a lot about the possible Integer..but no1 but GARP would know that.. with the quality of paper and considering 45-50% GARP Historical Pass Rate - 60 seems befitting !! but there is a but....who knows.. so lets wait for few more days - 15 to be precise