TL;DR for operators A portfolio can look diversified on a holdings report and still behave like one very crowded trade. The paper behind this article proposes a way to detect that crowding more rigorously: build a signed, weighted stock correlation network using statistical validation, then extract the largest module where every pair is strongly connected and every signed triangle is structurally balanced.1 ...
TL;DR for operators Crypto risk systems often watch the obvious giants. Bitcoin first. Ethereum second. Everything else somewhere in the dashboard’s lower intestine, where altcoins go to become colourful noise. This paper argues that this is not enough.1 In volatility forecasting, the asset that matters is not always the asset with the largest market capitalisation. It may be the asset whose volatility is transmitting stress into Bitcoin under the current market state. That is a different question, and it produces a different monitoring system. ...