Good post.

A question on the equity turbulence – did you use the S&P 500 constituents in calculation of the turbulence? Because the turbulence index requires cross-mean and covariance matrix.

Thanks! ]]>

Prices are always greater than zero. p>0. Hence, modelling them with geometric stochastic motion is appealing.

dp = p*r*dt+p*b*dW, guarantees p > 0 (if p_0 > 0), and the solution is log-normal prices.

If instead we chose dp = r*dt + b*dW, p can be anywhere on the reals, which isn’t realistic.

There are other choices that guarantee positive definiteness too, and which are perhaps better suited to modelling prices.

]]>From my understanding I calculated the logs of returns of month of the year-end, which here is my formula: Ln(monthly return index) of month at time t divided by Ln(monthly return index) of month at time t-1 and then subtract 1. However, Im not really confident about my results.

Could anyone here help me to clarify my calculation above?

I would really appreciate your help. Thank you.

Also, would love to be on your bloglist. Thanks.

]]>1.-http://www.math.upenn.edu/~ghrist/notes.html

2.-http://graphics.stanford.edu/courses/cs468-09-fall/#id3