I am working on the comparison of different volatility timing/target strategies on portfolios starting from different conditions (data, asset classes, calculation of realized volatility, different exposure assumptions, etc...).
I was thinking that it would be interesting to see what happens if I simulate volatility exceptional behaviors throughout the data in the backtests, but I'm not sure how to implement that.
I tought of defining a stochastic process (maybe a point process?) ad hoc capable of that, but I don't know where to start to look for something theoretically solid.
Any advice to put me in the right direction is well appreciated.
P.S. If it can be useful, I am working on Python, mainly numpy, pandas, and the usual related packages.