Using Two Cryptocurrencies Enhances Volatility Forecasting
Researchers from the HSE Faculty of Economic Sciences have found that Bitcoin price volatility can be effectively predicted using Ethereum, the second-most popular cryptocurrency. Incorporating Ethereum into a predictive model reduces the forecast error to 23%, outperforming neural networks and other complex algorithms. The article has been published in Applied Econometrics.
Cryptocurrencies are among the most unpredictable financial instruments. Their prices can swing sharply and frequently—a characteristic known as volatility, meaning that an asset's value can change dramatically even over a short period. In contrast, stocks and bonds tend to be much more stable.
The cryptocurrency market responds sharply to external events. Its total market capitalisation—the combined value of all issued coins—has already surpassed $3 trillion. Bitcoin and Ethereum make up over 60% of this market, trading $18 billion and $8 billion respectively in a single day.
This is why the ability to predict sudden price fluctuations has become crucial for risk management and investment strategies. Until now, researchers have primarily studied the volatility of each cryptocurrency separately. Anatoly Peresetsky, Research Professor at the HSE Faculty of Economic Sciences, and Maksim Teterin, a doctoral student, have proposed a new approach that examines the interdependence between the two cryptocurrencies.
To achieve this, the authors analysed high-frequency trading data for Bitcoin and Ethereum at five-minute intervals from January 1, 2018, to June 23, 2024. After filtering out unreliable data, they obtained over 2,300 observations for each cryptocurrency, and then calculated realised volatility, which measures how much an asset's price fluctuated.
Anatoly Peresetsky
'For example, Ethereum prices show slightly higher volatility. Interestingly, both cryptocurrencies experience pronounced extreme swings, underscoring the high-risk nature of the market,' explained Prof. Peresetsky. The authors also calculated realised covariances and correlations between the assets, reflecting the degree of interdependence and the relationship between Bitcoin’s and Ethereum’s price movements.
The researchers then constructed 11 variations of econometric models, each with different parameter combinations. Each model was trained using a 300-day observation window and then used to generate day-ahead forecasts. This approach enabled them to account for structural shifts in the market. Altogether, the procedure produced over 2,000 day-ahead forecasts per model.
To evaluate the accuracy of the models’ forecasts, standard metrics were used: root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Each metric measures the extent to which the model’s predictions deviate from the actual values on the trading day.
Ultimately, the baseline model produced a MAPE of 24.65% for Bitcoin and 20.98% for Ethereum. The best-performing model, which incorporated parameters for both cryptocurrencies, reduced the error to 23.38%. These results outperform previous studies using neural networks and other advanced models, which reported a MAPE of 25.6%.
'Compared to traditional financial assets, Bitcoin and Ethereum remain the most volatile instruments in the market. This high volatility presents opportunities, but it also entails significant risks. As a result, developing more accurate volatility forecasts is of great interest to both investors and regulators,' commented Prof. Peresetsky.
The study’s findings can help optimise investment portfolios and shape financial strategies in response to cryptocurrency market volatility, while emphasising the importance of monitoring the network of interdependencies rather than focusing solely on individual tokens.
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