Risk Modeling

TL;DR for operators Bitcoin is not simply “more volatile” than traditional assets. That is the easy answer, and therefore the suspicious one. A recent paper compares Bitcoin, GBP/USD, gold, and natural gas using two complexity tools: Refined Composite Multiscale Sample Entropy (RCMSE) and Multifractal Detrended Fluctuation Analysis (MF-DFA).1 The result is more interesting than the usual crypto-volatility sermon. Bitcoin has the highest summed multiscale entropy complexity, at 74.66, and the widest multifractal spectrum, at 0.62. Natural gas, despite showing high volatility in the return distribution, has the lowest values on both measures: 51.48 for summed RCMSE complexity and 0.21 for spectrum width. ...

TL;DR for operators Diversification is not wrong. That would be too easy, and therefore suspicious. The sharper point in Léonard Vincent’s paper is that diversification can fail in a very specific, very strong way when losses are extremely heavy-tailed and may have infinite mean.1 The paper compares two portfolios built from the same risks and the same weights. One spreads exposure across all risks as a weighted average. The other puts the whole exposure into exactly one randomly selected risk, using the same weights as selection probabilities. Under the paper’s conditions, the diversified portfolio has higher exceedance probability at every threshold. ...

TL;DR for operators Pricing desks usually ask an exotic-option model for one number: the expected discounted payoff. The paper behind this article asks for the whole conditional payoff distribution instead.1 That sounds like a small statistical upgrade. It is not. It changes what the model is trying to learn, what risk information becomes available after training, and where the engineering fragility enters. ...