Forecasting cryptocurrencies in turbulent times: Evidence on parsimony versus model complexity
DOI:
https://doi.org/10.18559/ebr.2026.1.2652Keywords:
cryptocurrencies, financial forecasting, time series models, emerging markets, financial marketsAbstract
This study examines short-term return forecasting for Bitcoin, Ethereum, and Litecoin over 2020–2024, comparing autoregressive benchmarks with Kitchen Sink and VARX-type models using point and density accuracy measures supported by Diebold–Mariano and Model Confidence Set inference. The results demonstrate that the AR(1) benchmark and parsimonious specifications incorporating cryptocurrency-specific variables consistently outperform the more elaborate linear frameworks considered, while the inclusion of macro-financial predictors offers limited benefits. Findings highlight the robustness of autoregressive dynamics for short-term cryptocurrency forecasting and underscore the importance of parsimony over model complexity. These results are consistent with a market environment characterised by high structural uncertainty, sentiment-driven trading and rapidly shifting regimes, in which additional macro-financial information contributes little to forecastability beyond short-run return momentum and crypto-specific volatility.
JEL Classification
Time-Series Models • Dynamic Quantile Regressions • Dynamic Treatment Effect Models • Diffusion Processes • State Space Models (C32)
Forecasting and Prediction Methods • Simulation Methods (C53)
Financial Markets and the Macroeconomy (E44)
International Financial Markets (G15)
Financial Forecasting and Simulation (G17)
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