A multiscale Quantile-on-Quantile analysis based on Hermite wavelets: Evidence from Bitcoin and macroeconomic indicators

This study proposes a novel Hermite wavelet–based Quantile-on-Quantile Regression (HW–QQR) framework to investigate the multiscale and distributional dependence between Bitcoin returns and inflation. The proposed framework integrates Hermite wavelet decomposition with quantile-on-quantile regression to capture nonlinear, asymmetric, and tail-dependent relationships across different time horizons and market conditions. The empirical results reveal significant scale-dependent dynamics in the Bitcoin–inflation relationship, indicating that the inflation-hedging properties of Bitcoin vary substantially across both return quantiles and time scales. These findings demonstrate that the relationship between Bitcoin and inflation is not uniform across market states or investment horizons and therefore cannot be adequately characterized by conventional mean-based approaches. The results provide important implications for investors and portfolio managers in designing inflation-hedging and diversification strategies, as well as for financial risk management under different market conditions.