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Hello David, I just finished the reading and tried this challenging question. I chose (C) because due to crashophobia the right-end of the volatility smile would tilt up, and so lognormal distribution will consistently undervalue ANY out of the money calls. Based on the diagrams you showed here...

Hello David I hope you are doing well. First, I notice that the VaR Mapping calculation methods between fixed income and outright forward are different. For fixed income we pre-and-postmultiply the correlation matrix to individual var ( CF * Z95%) and obtain a diversified VaR. in FRA we start...

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@gsarm1987 Thank you for your kind response! when F is high my calculation returns a smaller U ( negative ) - and NORMDIST return a smaller PD. when F is low my calculation returns a larger U ( positive ) and NORMDIST returns a larger PD. e.g. correlation = 0.4, PD=1.5% negative ( low ) F of...

Sorry I have a stupid question about why the text emphasise higher F = ( higher X ) would result in higher U. is it because of the relationship - Ui = aF + sqrt(1-corr) * z? if U = inverse normal of PD and PD soars because a poor economy - lower (F ), the U should increase too! so lower F and...

thank you David for your clarification! it is much clearer to me now. Sorry for asking this weird question. So as you said the F test using EES/RES is a special case of the restricted & unrestricted version. They are equivalent and they will return the same value in that special case. I am...

Hello I am having a hard time with MA process. lets say now we have a set of S&P stock return in % and we would like to model a ma(1). yes we checked the ACF and PACF and assumed it is a good fit. the model itself is: Observed Y = mean(u) + coefficient * Previous Error + Current Error...

thank you ! I mean if restriction = # of independent variables, are they the same: (ESS/df)/(RSS/df) and { (R-UNRestricted ^2 - R-Restricted ^ 2 ) /q } / ( 1- R-UNRestricted ^2 ) / (N-k-1)

does the two formulas converge assuming the multiple regression has k independent variables and our null hypotheses is ALL k slope coefficients are zero ( so q=k) ?