The driving forces behind cryptoassets’price dynamics are often perceived as being dominated by speculative factors and inherent bubble-bust episodes.Fundamental components are believed to have a weak,if any,role in ...The driving forces behind cryptoassets’price dynamics are often perceived as being dominated by speculative factors and inherent bubble-bust episodes.Fundamental components are believed to have a weak,if any,role in the price-formation process.This study examines five cryptoassets with different backgrounds,namely Bitcoin,Ethereum,Litecoin,XRP,and Dogecoin between 2016 and 2022.It utilizes the cusp catastrophe model to connect the fundamental and speculative drivers with possible price bifurcation characteristics of market collapse events.The findings show that the price and return dynamics of all the studied assets,except for Dogecoin,emerge from complex interactions between fundamental and speculative components,includ-ing episodes of price bifurcations.Bitcoin shows the strongest fundamentals,with on-chain activity and economic factors driving the fundamental part of the dynam-ics.Investor attention and off-chain activity drive the speculative component for all studied assets.Among the fundamental drivers,the analyzed cryptoassets present their coin-specific factors,which can be tracked to their protocol specifics and are economi-cally sound.展开更多
The out-of-sample R^(2) is designed to measure forecasting performance without look-ahead bias.However,researchers can hack this performance metric even without multiple tests by constructing a prediction model using ...The out-of-sample R^(2) is designed to measure forecasting performance without look-ahead bias.However,researchers can hack this performance metric even without multiple tests by constructing a prediction model using the intuition derived from empirical properties that appear only in the test sample.Using ensemble machine learning techniques,we create a virtual environment that prevents researchers from peeking into the intuition in advance when performing out-of-sample prediction simulations.We apply this approach to robust monitoring,exploiting a dynamic shrink-age effect by switching between a proposed forecast and a benchmark.Considering stock return forecasting as an example,we show that the resulting robust monitoring forecast improves the average performance of the proposed forecast by 15%(in terms of mean-squared-error)and reduces the variance of its relative performance by 46%while avoiding the out-of-sample R^(2)-hacking problem.Our approach,as a final touch,can further enhance the performance and stability of forecasts from any models and methods.展开更多
基金financial support from the Czech Science Foundation under the 20-17295S“Cryptoassets:Pricing,Interconnectedness,Mining,and their Interactions”project and from the Charles University PRIMUS program(project PRIMUS/19/HUM/17)Jiri Kukacka gratefully acknowledges financial support from the Charles University UNCE program(project UNCE/HUM/035)supported by the Cooperatio Program at Charles University,research area Economics.
文摘The driving forces behind cryptoassets’price dynamics are often perceived as being dominated by speculative factors and inherent bubble-bust episodes.Fundamental components are believed to have a weak,if any,role in the price-formation process.This study examines five cryptoassets with different backgrounds,namely Bitcoin,Ethereum,Litecoin,XRP,and Dogecoin between 2016 and 2022.It utilizes the cusp catastrophe model to connect the fundamental and speculative drivers with possible price bifurcation characteristics of market collapse events.The findings show that the price and return dynamics of all the studied assets,except for Dogecoin,emerge from complex interactions between fundamental and speculative components,includ-ing episodes of price bifurcations.Bitcoin shows the strongest fundamentals,with on-chain activity and economic factors driving the fundamental part of the dynam-ics.Investor attention and off-chain activity drive the speculative component for all studied assets.Among the fundamental drivers,the analyzed cryptoassets present their coin-specific factors,which can be tracked to their protocol specifics and are economi-cally sound.
文摘The out-of-sample R^(2) is designed to measure forecasting performance without look-ahead bias.However,researchers can hack this performance metric even without multiple tests by constructing a prediction model using the intuition derived from empirical properties that appear only in the test sample.Using ensemble machine learning techniques,we create a virtual environment that prevents researchers from peeking into the intuition in advance when performing out-of-sample prediction simulations.We apply this approach to robust monitoring,exploiting a dynamic shrink-age effect by switching between a proposed forecast and a benchmark.Considering stock return forecasting as an example,we show that the resulting robust monitoring forecast improves the average performance of the proposed forecast by 15%(in terms of mean-squared-error)and reduces the variance of its relative performance by 46%while avoiding the out-of-sample R^(2)-hacking problem.Our approach,as a final touch,can further enhance the performance and stability of forecasts from any models and methods.
