What happened was totally unexpected end of peg against the euro @ 1.2CHF regime that Swiss central bank aborted. See some articles about it. As far as I know nobody in the markets knew, there was no ...

Historical only has the advantage of being easily computable, that's pretty much it. It only makes sense if you have lots and lots of observations as you observed basically everything (you hope) that ...

I reckon it's the $\frac{log(ASK)+log(BID)}{2}$, just simple arithmetic average as it makes sense, also considering logarithmic returns, when you can only take a differences from log prices. Also, ...

Have you solved it yet? For example in the drift parameter, the dt needs to be vector of time from 0 to 1 by dt. My code is: GBM<-apply(BM,2,function(x) 100*exp((cumsum((r-0.5*sigma*sigma)*...

I haven't heard of a method to do it your way. Usually, you start with covariance matrix and do Cholesky in order to be able to generate random draws from the multinomial normal distribution with ...

That's quite odd, I have no explanation, so I'd suggest look at different source of the data. Bloomberg if you got access to it, or Finance Google. FG shows same data and the CSV looks alright - same ...

The paper explains it quite well I think: There are three cases: (1) $\omega>1$ the price is either exponentially increasing or decreasing and $P_{0}$ gives the base line of the exponential ...

firstly you need to calculate logarithmic returns and then depends on the software you use. For instance R has everything already coded. If you want to to it yourself (recommended if you say ...

Assuming you are minimising variance/standard deviation of the portfolio, then you are trying to allocate more weights towards less risky assets. You can try this if you create the covariance matrix ...

The prices need to be recalculated for 'Adjusted Close'. You basically split or the prices backwards so they correspond to the same number of shares...also when computing a covariance matrix, you need ...

It is the lowest price recorded in previous n days, as of last (yesterday's) close. So it in fact does not get disrupted, it is rolling with the dates. Say 3 day low (over weekend and Monday) would ...

You will need a 'pseudo' random number generator - most stats programming languages have them (Matlab, R, Python...). But GBM is defined with Normal increments $N(0,\sigma^{2}(T-t))$ so I dont think ...

If I understand your question correctly; the (expected) return always depends on the weights that the respective factor has in the portfolio, regardless of the risk. You are trying to find the ...

Have you come across Ito lemma / Ito calculus?. As Gordon suggests: divide the top equation by St, so you get $\frac{dS_t}{S_t}$ and we look at 'by intuition' what does $dln(S_t)$ look like in the Ito ...