Cryptocurrency’s price volatility and adaptive learning

This paper studies a theoretical question: Why is cryptocurrency so volatile? To investigate this, I apply a New Monetary model that incorporates an adaptive learning assumption. Specifically, building on the baseline framework of Choi and Rocheteau (2021), I relax their perfect foresight assumption by replacing it with adaptive learning. I show how high volatility can emerge under this revised assumption. With a simple learning rate algorithm, I find that adaptive learning can alter the stability of steady states. For instance, with a high learning rate, the system can experience a period of doubling bifurcation, potentially leading to chaotic regimes or explosive paths. These price dynamics help explain the extreme volatility observed in cryptocurrency markets.