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4

Other than the binomial expansion, what is the relationship between $B$ and $w$? As I understand correctly, there is no immediate relationship between the lag operator $B$ and the weights $w$. However, going from the fractional differencing operator, $(1-B)^d$ to $\hat{X}_t$ can be done as follows: \begin{align} (1-B)^d X_t &= \sum_{k=0}^{\infty} \begin{...


0

daily contributions cannot be chain linked they need to be added to reach monthly. same with effects. daily calcs using daily data and then collating this to reach monthly is the most correct way


1

Forget the matrix ;-) It makes it look like the regression problem from hell! Given each term in the matrix is just a function of E123 (which are all random and independent of each other, and independent of Fhat), each term can be (tediously) calculated on its own.


2

The awful truth is that we assume that returns that are optically quite close to (log)normal are indeed (log)normal, because it makes the associated mathematics of solving almost ANY subsequent practical problem associated with returns so much much much simpler. Not to mention the small matter of replacing these with "something better". Newton's ...


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