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First of all, it is not conceivable to do all that work by hand! You are crazy to have just thought it! Second, if you want to repeat your work with different datasets, I suggest you to use R, since, once you have written a script, you can use it all the times you want. But, there's a 'but': you cannot think we are going to write some code for you (you ...


Kalman filter (or similar methods) are quite well suited to deal with observations that are of different sampling frequencies and/or asynchronous.


For a normal random variable $\xi$ with mean 0, then \begin{align*} P(\xi < 0) = P(\xi > 0) = 50\,\%. \end{align*} For a normal random variable $\eta$ with mean (i.e., realized mean return) $\mu=3\,\%$ and risk (i.e., standard deviation) $\sigma = 3\,\%$, then \begin{align*} P(\eta < 0) &= P\left(\frac{\eta - \mu}{\sigma} < \frac{ - ...

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