The Courselet presents a paper that proposes a new model for analyzing cryptocurrency prices that considers past behaviors like herding and momentum trading, in addition to rational expectations of future prices. This model integrates asset-flow approach and quantity equations of money, allowing for the analysis of key variables in the quantity theory, accounting for the inflexible token supply of cryptocurrencies. The model is easy to interpret and apply both empirically and in simulations. Simulations reveal that increased tokens held by investors lead to greater price instability in response to shocks, concluding that price stabilization is unlikely without low levels of tokens held by investors.
Most models derive current cryptocurrency prices over rational expectations of future ones. We complement this perspective with a backward-looking view accounting for herding and momentum trading observed in cryptocurrency markets. Our approach merges an asset-flow approach modeling rational and irrational speculation around an asset’s fundamental value into the transactions-form of the quantity equations of money. Our approach allows us to analyze the inter-temporal relation of the quantity theory’s key variables for cryptocurrencies with their inflexible token supply. Due to the intuitive component models, our model is simple to interpret and to apply empirically and in simulations. We demonstrate its usefulness via simulations of how cryptocurrency prices adjust after changes in fundamental values. We find that a higher fraction of tokens held by investors (rather than circulated by users) leads to disproportionate increases in the instability of the price process in response to shocks. We conclude that stabilization of cryptocurrency prices is unlikely unless accompanied by low levels of tokens held by investors.
I pursue cryptocurrency research from an economic angle.