Volatility Forecasting

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. ...

TL;DR for operators NGAT is useful because it attacks a real modelling mismatch in financial AI: companies do not absorb market information in the same way, yet many graph neural networks treat them as if they do. The paper’s answer is a node-level graph attention layer, where each company learns its own attention mechanism for reading signals from related companies. ...