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We provide a new way of hedging a commodity exposure which eliminates downside risk without sacrificing upside potential. The tool used is a variant on the equity passport option and can be used with both futures and forwards contracts as the underlying hedge instrument. Results are given for popular commodity price models such as Gibson-Schwartz and Black with convenience yield. Two different scenarios are considered, one where the producer places his usual hedge and undertakes additional trading, and the other where the usual hedge is not held. In addition, a comparison result is derived showing that one scenario is always more expensive than the other. The cost of these methods are compared to buying a put option on the commodity.

In this paper, we present a new method for computing the first-order approximation of the price of derivatives on futures in the context of multiscale stochastic volatility studied in Fouque et al. (2011). It provides an alternative method to the singular perturbation technique presented in Hikspoors & Jaimungal (2008). The main features of our method are twofold: firstly, it does not rely on any additional hypothesis on the regularity of the payoff function, and secondly, it allows an effective and straightforward calibration procedure of the group market parameters to implied volatilities. These features were not achieved in previous works. Moreover, the central argument of our method could be applied to interest rate derivatives and compound derivatives. The only pre-requisite of our approach is the first-order approximation of the underlying derivative. Furthermore, the model proposed here is well-suited for commodities since it incorporates mean reversion of the spot price and multiscale stochastic volatility. Indeed, the model was validated by calibrating the group market parameters to options on crude-oil futures, and it displays a very good fit of the implied volatility.

In this paper, we investigate the applicability of the comonotonicity approach in the context of various benchmark models for equities and commodities. Instead of classical Lévy models as in Albrecher et al. we focus on the Heston stochastic volatility model, the constant elasticity of variance (CEV) model and Schwartz’ 1997 stochastic convenience yield model. We show how the technical difficulties of inverting the distribution function of the sum of the comonotonic random vector can be overcome and that the method delivers rather tight upper bounds for the prices of Asian Options in these models, at least for strikes which are not too large. As a by-product the method delivers super-hedging strategies which can be easily implemented.

In the past two decades, China has substantially increased its economic presence in Latin America. The impressive rate of economic growth in China has resulted in a voracious appetite for Latin American commodities and energy sources. China has also become a major investor in the region, and has loaned billions of dollars to Latin American countries. This paper evaluates how aware Latin American citizens are of this increased economic presence of China, and also studies citizens’ attitudes toward the rising influence of China in Latin America. Public attitudes toward the Chinese economic and political model, and evaluations of the Chinese popular culture are also presented and discussed. The evidence suggests that the image of China is improving in Latin America as a result of its new economic role in the region. However, Chinese soft power faces several limitations in the region. The Chinese political and economic models, and the Chinese popular culture are still not very attractive in Latin America.

We analyze whether metrics of climate risks, as captured primarily by changes in temperature anomaly and its stochastic volatility (SV), can predict returns and volatility of 25 commodities, covering the overall historical period of 1258 to 2021. To this end, we apply a higher-order nonparametric causality-in-quantiles test to not only uncover potential in-sample predictability in the entire conditional distribution of commodity returns and volatility but also to account for nonlinearity and structural breaks which exist between commodity returns and the metrics of climate risks. We find that, unlike in the misspecified linear Granger causality tests, climate risks do predict commodity returns and volatility, though the impact on the latter is stronger, in terms of the coverage of the conditional distribution. Insights from our findings can benefit academics, investors, and policymakers in their decision-making.

The large inflow of investment capital in critical periods sparked a debate about the extent to which these speculative bubbles affect asset volatility and how (and what extent) these volatilities are transmitted between them. In periods of greater uncertainty, commodity futures markets may receive and/or send two types of volatility spillovers: intergroup of assets and/or intragroup of assets. We tested for the period from March 3, 2000 to May 4, 2017, which of the two effects prevailed and in which group of assets was more intense. We concluded that the most relevant volatility transmission effects (measured by Diebold–Yilmaz indices) occurred intragroup of assets — corn, wheat, soy, oats and rice. These assets make up the main cluster of a commodities complex network. Thus, we detected and measured using network approach that the most significant effects was over the years of the Great Recession (2007–2009) and the peak of the European Sovereign Debt Crisis (2010–2012).

This paper examines the main drivers of the returns of gold miner stocks and ETFs during 2006–2017. We solve a combined optimal control and stopping problem to demonstrate that gold miner equities behave like real options on gold. Inspired by our proposed model, we construct a method to dynamically replicate gold miner stocks using two factors: the spot gold ETF and market equity portfolio. Furthermore, through each firm’s factor loadings on the replicating portfolio, we dynamically infer the firm’s implied leverage parameters of our model using the Kalman Filter. We find that our approach can explain a significant portion of the drivers of firm implied gold leverage. We posit that gold miner companies hold additional real options which help mitigate firm downside volatility, but these real options contribute to lower returns relative to the replicating portfolio when gold returns are positive.

The financialization of commodities and their inclusion in financial portfolios as part of an investment strategy may result in higher correlations and volatility spillovers between commodity and equity markets. In this paper, we estimate the correlation between equity markets and commodities using the dynamic conditional correlation (DCC) model, while emphasizing the differences between emerging and developed markets co-movements with commodities. The results reveal that certain emerging markets, especially those in Asia, show a much lower level of co-movement with commodities than developed markets do, while Latin American equities exhibit a higher level of integration with commodities. Furthermore, it is found that both agricultural and precious metals commodities offer better diversification possibilities in the less developed markets. We also find that increases in the CBOE Volatility Index (VIX) are related to higher agriculture commodities-equities correlations, while commodity net index investment has limited explanatory power in our study.

The main purpose of this article is to empirically demonstrate the effects of temporal aggregation when applying reverse regression models hypothesizing that spot prices today help predict forward rates in the future. This paper essentially reviews results from earlier research indicating that time-series aggregation will most certainly influence standard errors on parameter estimates. Standard errors are likely to increase with aggregation. The relationships between futures prices and spot oil prices are analyzed along with the importance of the effects of temporal aggregation and alternative model specification for understanding empirical relationships between the markets. Model specification and time-series aggregation over daily, weekly, and monthly aggregations confirm evidence that estimated standard errors are likely to increase with aggregation and t-ratios change as well. While goodness-of-fit measures might increase with aggregation, forecast accuracy with macrolevel aggregation might deteriorate owing to information loss due to the averaging of observations associated with underlying microstructures.