Platinum–Cryptocurrency Connectedness at Quantile Frequencies: Evidence from Industrial Metal Markets

Abstract

This paper aims to examine the quantile-based frequency connectedness between cryptocurrencies and platinum returns across different market conditions and investment horizons. In this paper, it is also aimed to evaluate the asymmetric and time-varying spillover effects between cryptocurrency markets and platinum returns within short-, medium-, and long-term horizons. The study discussed the Heterogeneous Market Hypothesis together with the concepts of cryptocurrencies and platinum as part of the theoretical and conceptual foundations underpinning the analysis. The study adopted the positivist research paradigm, quantitative research approach, and explanatory research design. We employed a hybrid analytical framework combining Quantile Regression (QR), Quantile-on-Quantile Regression (QQR), and Ensemble Empirical Mode Decomposition (EEMD) to capture the non-linear and frequency-dependent dynamics between the selected variables. A dataset comprising daily prices of Cardano (ADA), Binance Coin (BNB), Bitcoin (BTC), Ethereum (ETH), TRON (TRX), Tether (USDT), Dogecoin (DOGE), Ripple (XRP), and platinum returns sourced from CoinMarketCap and the Bloomberg L.P. Terminal was analysed. The main findings of the study demonstrate that (i) the connectedness between cryptocurrencies and platinum returns is generally weak and predominantly inverse in the short term, indicating minimal immediate spillover effects between the two asset classes, (ii) medium-term relationships become significantly stronger with positive dependencies dominating across most quantiles, suggesting increasing integration driven by speculative investment flows and evolving investor sentiment, and finally (iii) long-run connectedness is highly asymmetric and predominantly positive, particularly from cryptocurrencies to platinum returns, although occasional negative dependencies at lower quantiles indicate limited hedging and safe-haven properties during extreme market downturns.