Coin impact on Cross-Crypto Realized Volatility and Dynamic Cryptocurrency Volatility Connectedness

TY - JOUR

T1 - Coin impact on Cross-Crypto Realized Volatility and Dynamic Cryptocurrency Volatility Connectedness

AU - Korkusuz , Burak

AU - Sahiner, Mehmet

N1 - © The Author(s) 2025.

PY - 2025/11/28

Y1 - 2025/11/28

N2 - This study evaluates the predictive accuracy of traditional time series (TS) models versus machine learning (ML) methods in forecasting realized volatility across major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), and Ripple (XRP). Employing high-frequency data, we analyze cross-cryptocurrency volatility dynamics through two complementary approaches: volatility forecasting and connectedness analysis. Our findings reveal three key insights: (i) TS models, particularly the heterogeneous autoregressive (HAR) model, exhibit superior predictive performance over their ML counterparts, with the long short-term memory (LSTM) model providing competitive yet inconsistent results due to overfitting and short-term volatility challenges; (ii) including lagged realized volatility of large-cap coins improves predictive accuracy for mid-cap coins, especially XRP, whereas forecasts for large-cap coins remain stable, indicating more resilient volatility patterns; and (iii) volatility connectedness analysis reveals substantial spillover effects, particularly pronounced during market turmoil, with large-cap assets (BTC and ETH) acting as primary volatility transmitters and mid-cap assets (XRP and LTC) serving as volatility receivers. These results contribute to the understanding of volatility forecasting and risk management in cryptocurrency markets, offering implications for investors and policymakers in managing market risk and interdependencies in digital asset portfolios.

AB - This study evaluates the predictive accuracy of traditional time series (TS) models versus machine learning (ML) methods in forecasting realized volatility across major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), and Ripple (XRP). Employing high-frequency data, we analyze cross-cryptocurrency volatility dynamics through two complementary approaches: volatility forecasting and connectedness analysis. Our findings reveal three key insights: (i) TS models, particularly the heterogeneous autoregressive (HAR) model, exhibit superior predictive performance over their ML counterparts, with the long short-term memory (LSTM) model providing competitive yet inconsistent results due to overfitting and short-term volatility challenges; (ii) including lagged realized volatility of large-cap coins improves predictive accuracy for mid-cap coins, especially XRP, whereas forecasts for large-cap coins remain stable, indicating more resilient volatility patterns; and (iii) volatility connectedness analysis reveals substantial spillover effects, particularly pronounced during market turmoil, with large-cap assets (BTC and ETH) acting as primary volatility transmitters and mid-cap assets (XRP and LTC) serving as volatility receivers. These results contribute to the understanding of volatility forecasting and risk management in cryptocurrency markets, offering implications for investors and policymakers in managing market risk and interdependencies in digital asset portfolios.

KW - Volatility Forecasting

KW - Realized Volatility

KW - Bitcoin

KW - Cross-Cryptocurrency impact

KW - Dynamic Connectedness

KW - Machine Learning

KW - Network Analysis

KW - Econometric Models

U2 - 10.1186/S40854-025-00881-X

DO - 10.1186/S40854-025-00881-X

M3 - Article

SN - 2199-4730

VL - 11

JO - Financial Innovation

JF - Financial Innovation

M1 - 129

ER -