Tag Info

New answers tagged

0

The fact that Implied Vol rises has absolutely nothing to do with riskaversion. If market expects volatility before an upcoming uncertain earnings report, put option prices rise naturally. This is due to the asymmetric payoff profile of options, which always gain from volatility because the downside losses are capped but upside potentially unlimited.


2

It might help to look at the solutions of the SDEs that you have there. In the first case $$ S_t/S_0 = \exp(-\sigma^2/2 t + \sigma B_t) \quad \quad (1) $$ Thus if you take the log then $\sigma$ is the volatility of the log-returns (assume that $t=1$ time step),. In the second case $$ S_t = S_0 + \sigma B_t \rightarrow S_t - S_0 = \sigma B_t \quad \quad(2) ...


0

If you want to take the EWMA approach have a look at cov.wt() from the stats package. It will give you an EWMA volatility, which you can then, given the normal distribution assumption, easily transform into VaR.


1

If you believe the process $Y_t$ to be stationary, you can try to profit from it via a mean-reversion strategy or any other way that exploits the stationarity. It doesn't matter whether $Y_t$ is obtained as a cointegrational combination of a few non-stationary processes, or as a linear combination of some processes that are stationary themselves. In the ...


0

If you calculated MA by hand without actually fitting a MA(q) time series model, then you are out of luck. I suggest you use R, like in this example that shows how to construct a prediction interval, among other things.


1

The cumulative return over the entire path is the sum of the returns on the individual periods: $$X = X_1 + X_2 + \ldots + X_N.$$ Two potential definitions of the volatility of this process would be $Std(X) / \sqrt{N}$ (which is exactly your "cross-section" volatility) or $Std(X_i)$ (assuming each $X_i$ has the same unconditional distribution). If the $X_i$ ...


0

I've analysed numerous strategies, and have never encountered problem similar to yours. Your approach of volatility measurement may be a bit deviating from the conventional thinking. Essentially, why would you measure volatility of overlapping returns? It's not sensible. No matter what rolling or walk-forward schemes you are adopting, one can always derive ...


0

Concidering 22 days of trading per month you have approximatly 132 days of trading. I highly doubt that this will be sufficient for any forecasting. The sample might be too small. Have a look here: http://research.stlouisfed.org/wp/2012/2012-008.pdf Erdemlioglu, Laurent and Neely used the data of ~10 years to conduct their survey.



Top 50 recent answers are included