Granger Causality

Causality is rarely one-size-fits-all—especially in the dynamic world of time series data. Whether you’re analyzing brainwaves, financial markets, or industrial processes, the timing of influence and the frequency at which it occurs both matter. Traditional Granger causality assumes a fixed temporal lag, while Variable-Lag Granger Causality (VLGC) brings some flexibility by allowing dynamic time alignment. But even VLGC falls short of capturing frequency-specific causal dynamics, which are ubiquitous in complex systems. ...

In the noisy world of algorithmic trading, volatility is often treated as something to manage or hedge against. But what if it could be a signal generator? Ivan Letteri’s recent paper proposes a novel trading framework that does just that: it treats mid-range volatility not as a nuisance, but as the key to unlocking directional causality between assets. From Volatility to Causality: The 4-Step Pipeline This is not your standard volatility arbitrage. The author introduces a four-stage pipeline that transforms volatility clusters into trading signals: ...