We aim to estimate the systemic risk present in commodity and stock markets. We will focus on crucial commodity market segments and financial assets, including agricultural products, metals, energy resources, stock markets, exchange rates, and treasury bonds. In the realm of agricultural commodities, our analysis will encompass a wide range of items: London wheat, US wheat, oats, rough rice, US corn, US soybean meal, US soybean oil, US soybeans, London cocoa, London coffee, London sugar, lumber, orange juice, US cocoa, US coffee C, US cotton #2, US sugar #11, feeder cattle, lean hogs, and live cattle. For metals, we will evaluate aluminum, copper, gold, lead, nickel, palladium, platinum, silver, tin, and zinc. The energy component includes Brent oil, WTI crude oil, gasoline RBOB, heating oil, London gas oil, natural gas, and CO2 emissions. Our stock market analysis will also involve indices such as the S&P 500, STOXX 600 Europe, and the Shanghai SE Q Share Index (SSEA). The exchange rates we utilize are EUR/CNY, USD/CNY, and  EUR/USD will we also use the 10-year bond yield USA, 10-year bond yield in China, 10-year bond yield in Germany, 10-year bond yield in France, and 10-year bond yield in the UK for our investigation. We also include the CBOE Volatility Index.

Systemic risk refers to the overall risk inherent in the entire market, and to investigate this, we will utilize the high-dimensional time series factor copula model developed by Oh and Patton (2017). First, we will calculate the Joint Probability of Anomalies Changes (JPAC) derived from this model. The interconnectedness of the commodity and stock markets comes from their responsiveness to upstream and downstream trends, where price fluctuations can significantly affect economic conditions. The JPAC will provide valuable insights into the likelihood of considerable price shifts within both markets, reflecting systemic risk and informing us about major economic events.

Furthermore, we will examine price variations in response to substantial occurrences such as the COVID-19 pandemic and the conflict in Ukraine. Next, we will assess the heterogeneous dependence between commodity and stock prices when a disturbance occurs in one commodity, utilizing the Expected Proportion in Distress (EPD) measure proposed by Oh and Patton (2017). The EPD quantifies how different commodities respond to significant shocks, thus identifying the commodities and securities with the greatest influence within the market. A higher EPD value for a given commodity indicates greater systemic relevance, while lower values suggest a lesser impact.

While considering the existing literature, we found that only the research by Ouyang et al. (2022) is pertinent to our study. However, our research builds upon their findings by incorporating stock market dynamics into the analysis and examining various macroeconomic indicators. This approach aims to provide a more comprehensive understanding of systemic risk in the commodity and stock markets, which will prove valuable to regulators and market participants for developing proactive policies.