Dynamic Risk Spillovers between Stablecoins and Major Cryptocurrencies
Description
This study investigates the dynamic risk spillovers between major cryptocurrencies (Bitcoin, Ethereum, Binance Coin) and two prominent fiat-backed stablecoins (USDT and USDC) using daily return data from January 2020 to January 2026. The analysis focuses on how these assets transmit risk across the cryptocurrency market, especially during major market shocks such as the Terra collapse, FTX bankruptcy, and Silvergate Bank liquidation. The data, sourced from Yahoo Finance, is transformed into log return series to ensure stationarity and is analyzed using a rolling VAR model combined with Generalized Forecast Error Variance Decomposition (GFEVD). The rolling window of 250 trading days allows for capturing time-varying risk spillover dynamics across the assets. Key findings reveal that risk spillover in the cryptocurrency market is largely event-driven. The total spillover index (TSI) shows significant increases during critical events, indicating that systemic risk is not consistently high but peaks during major shocks. Furthermore, USDT and USDC display distinct roles in risk transmission. USDT is a significant transmitter of risk, particularly during liquidity shocks, while USDC tends to absorb risk due to its more stable liquidity structure. These results emphasize the important role of stablecoins in cryptocurrency market dynamics and provide valuable insights for understanding systemic risk transmission. The findings suggest that regulators should focus on the structural differences between stablecoins, as their market roles significantly influence their capacity to propagate risk.
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Steps to reproduce
Data Source: The daily adjusted closing prices of Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Tether (USDT), and USD Coin (USDC) were sourced from Yahoo Finance for the period between January 1, 2020, and January 14, 2026. Data Download: The data was downloaded in Excel format for each asset. Data Processing: The raw price data was cleaned and merged using R. Each asset's closing price was renamed and converted into log returns to ensure stationarity for time series analysis.