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#251




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#252




It had been suggested to me to use the idea of an efficient frontier to pick an asset class mix. I understand how I would use that to choose the mix but I’m not seeing how you could really do a sensitivity analysis using this? I guess you would just have to plot the new return and SD on the same play as the initial scenarios and see Where it lies but there’s really no quantitative measure of that change.

#253




Can someone please explain what using the CTE 95 metric for determining the cost per employee would tell us in the context of the CDEF situation? Let's say that using the CTE 95 metric would result in a cost per employee of 800. So a tax amount of 800 would sufficiently cover future liabilities with 95% certainty? or is that what Var 95 tells us?

#254




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#255




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I'd think the other way around was correct because: Mean / CTE = expected return rate over risk. Given 2 portfolios with the same risk value (CTE in the denominator), the one with higher mean (expected rate of return) would have better risk/return trade off. Thanks! 
#256




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Worth repeating though that pretty much all feedback received is to not use a ratio. 
#259




i understand that but can't you also look at it both ways? its measuring how risky the asset portfolio is because its calculating the mean tax contribution that is needed to offset the level of risk of the portfolio. i feel like thats why the Q refers to the metrics as risk/return metrics and not risk and return metrics

#260




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Theoretically, let's say your asset portfolio is 1/3 each in treasury, bonds, and equities. You simulate annual returns from each asset, and somehow each of your 1 million simulations tells you that you get the exact same return every time. If I were to ask you what you expect your return to be on the next simulation, I imagine you'd be pretty sure it's the same as the last million, as in, low risk of having adverse outcome. In this question, it doesn't matter how much your returns are, because I'm asking about the risk of it being adverse. In other words, there is 0 volatility. Now let's say none of your 1 million simulations are the same. Some say you lose all your money, some say you make 1000% return some years, etc etcit's all over the place. Averaging these simulations up, you find your "average" return. Now if I ask you what you think the exact return will be on your next simulation, you'd probably say there's no way to know, it varies too much. You could tell me the average, which gives me a ballpark of where your distribution might be centered, but you wouldn't be confident the next simulation would be close to the average. Therefore, high risk, high volatility. ETA: The average you do find in scenario 2 still tells you something about your return, just not the level of risk associated with it. Easy to picture a bell curve, and this average tells you where your distribution is centered, but doesn't tell you the weight of the tails. 
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cdef, fa task 2, final assesment 
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