CRNG
CORENERGY INFRSTRCTURE TR
stock OTC

I read through the code and tried to independently benchmark it and I don’t really think the conclusions you’ve made are justified by the results. In my own benchmark, CRNG produced heavy fails but it introduced noticeable positive skew and showed only mild volatility clustering relative to a standard GARCH-t baseline. It was also significantly slower than most of the other baseline methodologies. It seems like it can generate outliers but that is a much weaker claim than “universal catastrophe mechanism” or even a useful tail risk model.
A high KS p-value is not evidence that two systems share the same generating process, and matching gap distributions after trying multiple kurtosis thresholds is not strong support for cross-domain universality. Earthquakes / natural disasters and financial crashes aren’t interchangeable event streams simply because some summary statistics generally line up.
My impression after reviewing is that the project is a creative heuristic generator with too mix narrative wrapped around it. Good experiment but the results don’t justify the claims being made
Also, more broadly, some of the implementation itself makes me even less confident in the framing. For example, parts of the API are labeled in ways that imply standard distributions, but they are not actually implementing those distributions in any conventional sense.

KS test: the gap distribution of CRNG extreme events is statistically indistinguishable from real earthquakes (p=0.990), crashes (p=0.665), and natural disasters (p=0.979)

I read through the code and tried to independently benchmark it and I don’t really think the conclusions you’ve made are justified by the results. In my own benchmark, CRNG produced heavy fails but it introduced noticeable positive skew and showed only mild volatility clustering relative to a standard GARCH-t baseline. It was also significantly slower than most of the other baseline methodologies. It seems like it can generate outliers but that is a much weaker claim than “universal catastrophe mechanism” or even a useful tail risk model.
A high KS p-value is not evidence that two systems share the same generating process, and matching gap distributions after trying multiple kurtosis thresholds is not strong support for cross-domain universality. Earthquakes / natural disasters and financial crashes aren’t interchangeable event streams simply because some summary statistics generally line up.
My impression after reviewing is that the project is a creative heuristic generator with too mix narrative wrapped around it. Good experiment but the results don’t justify the claims being made
Also, more broadly, some of the implementation itself makes me even less confident in the framing. For example, parts of the API are labeled in ways that imply standard distributions, but they are not actually implementing those distributions in any conventional sense.

KS test: the gap distribution of CRNG extreme events is statistically indistinguishable from real earthquakes (p=0.990), crashes (p=0.665), and natural disasters (p=0.979)