Stochastic ordering of systemic risk in commodity markets

The contribution of commodity risks to the systemic risk is assessed in this paper through a novel approach that relies on the stochastic property of concordance ordering of CoVaR. Considering the period that spans from 2005 to 2022 and the VIX as the proxy for the stability of the financial system, we build the stochastic ordering of systemic risk for 35 commodities belonging to four sectors: Agriculture, Energy, Industrial Metals, and Precious Metals. The estimates of the ΔCoVaR signal that contagion effects from commodity markets to the financial system have been stronger during the years 2017–2019. Backtests validate CoVaR as a more resilient risk measure than the VaR, especially during periods of market turmoils. The stochastic ordering of CoVaR shows that severe losses (downside risk) in commodity markets tend to exacerbate systemic financial distress more than gains (upside risk). Commodity risks arising from WTI and EUA are threatening triggers for systemic risk. In contrast, the financial system is less vulnerable to a broader range of scenarios arising from fluctuations in Gold prices. As top contributors to the systemic risk, among the sectors we find Energy and Precious Metals with respect to upside risk and downside risk. The Covid-19 crisis has deeply amplified the systemic influence arising from the downside risk of WTI, Gasoline, and Natural Gas UK and has confirmed the safe-haven role of Gold.

The network of commodity risk

In this paper, we investigate the interconnections among and within the Energy, Agricultural, and Metal commodities, operating in a risk management framework with a twofold goal. First, we estimate the Value-at-Risk (VaR) employing GARCH and Markov-switching GARCH models with different error term distributions. The use of such models allows us to take into account well-known stylized facts shown in the time series of commodities as well as possible regime changes in their conditional variance dynamics. We rely on backtesting procedures to select the best model for each commodity. Second, we estimate the sparse Gaussian Graphical model of commodities exploiting the Graphical LASSO (GLASSO) methodology to detect the most relevant conditional dependence structure among and within the sectors. A novel feature of our framework is that GLASSO estimation is achieved exploring the precision matrix of the multivariate Gaussian distribution obtained using a Gaussian copula with marginals given by the residuals of the aforementioned selected models. We apply our approach to the sample of twenty-four series of commodity futures prices over the years 2005–2022. We find that Soybean Oil, Cotton, and Coffee represent the major sources of propagation of financial distress in commodity markets while Gold, Natural Gas UK, and Heating Oil are depicted as safe-haven commodities. The impact of Covid-19 is reflected in increased heterogeneity, as captured by the strongest relationships between commodities belonging to the same commodity sector and by weakened inter-sectorial connections. This finding suggests that connectedness does not always increase in response to crisis events.

Option Pricing, Zero Lower Bound, and COVID-19

This paper provides a quantitative assessment of equity options priced at the Zero Lower Bound, i.e., when interest rates are set essentially to zero. We obtain closed form formulas for American options when the Zero Lower Bound policy holds. We perform numerical implementation of American put options written on the stock Federal National Mortgage Association (FNMA) and of related bounds for the optimal exercise. The results show similarities with the corresponding European options priced at the Zero Lower Bound during the COVID-19 crisis.

Multivariate Analysis of Energy Commodities during the COVID-19 Pandemic: Evidence from a Mixed-Frequency Approach

This paper shows the effects of the COVID-19 pandemic on energy markets. We estimate daily volatilities and correlations among energy commodities relying on a mixed-frequency approach that exploits information from the number of weekly deaths related to COVID-19 in the United States. The mixed-frequency approach takes advantage of the MIxing-Data Sampling (MIDAS) methods. We compare our results to those obtained by employing two well-known models that do not account for the COVID-19 low-frequency variable, namely the Dynamic EquiCorrelation (DECO) and corrected Dynamic Conditional Correlation (cDCC). Moreover, we consider four possible specifications of the volatility: GARCH, GJR, GARCH-MIDAS, and Double-Asymmetric GARCH-MIDAS. The empirical results show that our approach is statistically superior to other models and represents a valuable methodology that can be used for risk managers, investors, and policy makers to assess the effects of the pandemic on spillovers effects in energy markets.