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1

alpha + beta < 1 is the stationary condition for GARCH. If alpha and beta are low that means volatility of the stock does not have clustering behaviors. I think you can have a look at ADF and PACF of Return^2 time series first. If the first order autocorrelation is very significant but alpha is not, then perhaps you can check on the parameter calibration. ...


2

i would guess the value of volatility that makes the variance swap with strike equal to it have zero value.


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I've been heavily in the decision process of rejecting interview candidates at my firm. We have certainly come across PhD candidates who failed sheerly based on the poor quality of their presentation. The application process is an art. It's a crapshoot. Even to your recruiters. Sometimes we already have a hypothesis against the candidate's favor by the time ...


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You sound like a good candidate for many roles in finance who probably has experience with some reasonable approaches to presentation. If your intuition is that you made some sub-optimal choices in the case of this assignment then you probably did, so try to get more specific about that and identify precisely where you believe you went wrong. On the other ...


3

The answer is that you may use an approach that includes IRR, but that's not a necessary component of what I would consider a good model. I have seen commercial tools that include them and those that don't. I have also seen practitioners set the variables in packages that include this approach, so that they were not a relevant component of the resulting ...


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The formula works for total variance, not "strike specific" variance that you need to construct basket vol surface from components, because single historical correlation (or correlation matrix) just does not provide enough information to uniquely reconstruct expected distribution of basket returns (unless for a trivial case where all components are gaussian, ...


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Using a simple moving average is a trick to take the noise out of the estimated quantity. He calculates a measure of volatility that is the SMA of the estimates of historical volatilities for the past 21 days. This is a crude way to estimate volatility. If you read further you will see that the author uses a GARCH(1,1) model to estimate volatility.



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