Exploring Tail Risk Transmission between Volatility Indices and Cryptocurrencies: Evidence from Quantile Connectedness

[Purpose] This paper examines tail spillover and quantile connectedness between implied volatility measures (VIX, OVX, GVZ, and major cryptocurrencies such as Bitcoin Cash, Ripple, Litecoin, Ethereum, and Bitcoin). The investigation examines risk transmission for the bullish, bearish, as well as normal market conditions. [Methodology] Cross-quantilogram and quantile connectedness frameworks are utilized in a quantile vector autoregressive (QVAR) framework. Using the QVAR method with the day-ahead data from June 5, 2020, to June 8, 2024, generalized forecast error variance decomposition (GFEVDs) are evaluated to account for static as well as dynamic connectedness across different regimes of market conditions. [Results] The results indicate that the TCI (Total Connectedness Index) is broadly flat under typical conditions but increases under bullish conditions as well as under bearish conditions. Cryptocurrencies, besides Ripple under certain conditions, are a net transmitter of shocks, while volatility indices are the key net receivers. Such results feature non-linear risk transmission processes and carry useful implications for hedging as well as for diversification of portfolios. [Research limitations/implications] The paper focuses on cryptocurrencies and implied indices during a particular time interval (2020-2024) that might restrict the generalizability of results extended to other economic assets or timeframes. Future research might extend the coverage by incorporating the other asset classes or machine learning-based connectivity methods. [Practical Implications] It provides actionable advice for policymakers as well as for portfolio managers to control risk transmission between cryptocurrencies as well as volatility indices. Enhancing monitoring mechanisms as well as adaptive hedging policies remains helpful in mitigating systematic risk exposure under severe market conditions. [Originality/Value] This study is original in applying the quantile connectedness methodology to simultaneously study implied volatility indexes and cryptocurrencies under different market settings. Unlike earlier studies that focus on just the mean connectedness, our evaluation reveals the tail risk behavior that causes systemic exposure and contagion. The results add value to the body of work under Decision Science by offering policymakers and investors useful insights about risk transmission between volatility indexes and cryptocurrencies, thus facilitating decision-making about portfolio diversification and hedging under a volatile market.