Scrutinizing commodity markets by quantile spillovers: A case study of the Australian economy

Notwithstanding Australia plays the lead role in exporting strategic commodities such as crude oil, natural gas, coal, Liquid Natural Gas (LNG), and iron ore, a scattering of researchers attempts to investigate the effects of exported commodities on stock and currency in Australia. In this academic research, our objective is to delve deeply into volatility connectedness among Brent oil, natural gas, coal, iron ore, LNG, stock, and currency markets in Australia. To that end, we adopt Ando et al. (2018) and Baruník and Křehlík (2018) techniques which are built upon Diebold and Yilmaz's (2012) framework for utilizing quantile- and frequency-based spillover analysis respectively. Finally, we used a recent approach developed by Chatziantoniou et al. (2022) for robustness purposes which combines quantile-spillover (due to Ando et al., 2018) and frequency-spillover (due to Baruník and Křehlík, 2018). This approach enables us not only to overcome the methodological shortcomings of prior studies but also to provide an insightful analysis regarding interrelationships across pivotal commodities. Our findings reveal that lower and upper quantiles show more satisfactory performance compared to the conditional mean. This implies that the usage of mean-based connectedness measures does not provide accurate results. More precisely, even though it provides a practical framework for gauging extreme spillovers, it overlooks certain quantiles. The framework, accordingly, undervalues the real effects of spillovers across the markets. What is more, the mentioned principal approach deploys Ordinary Least Square (OLS) framework to gauge the VAR. As a dire consequence, this framework prevents improving the efficiency of the model. We discern Brent oil, natural gas, and Australian stock are the most consequential net transmitters of shocks in this system. Plus, the total spillover index reveals a high level of strength in the return spillovers amid the assets. In terms of policy implications, we expect our outcomes to aid market participants in expanding their perceptions concerning price volatility shocks in these crucial markets. For investors, they are able to measure related risks during extreme positive or extreme negative conditions, leading investors to stabilize their portfolios during extreme conditions and hedging, and risk-pricing, at that. From the results of BK (2018), we comprehend total connectedness does not properly perform in long-horizon. This suggests investors should not hold the markets in short-horizon by reason of high volatility.