Commodity Price Uncertainty Research Articles

Commodity currencies revisited: The role of global commodity price uncertainty

Exchange rates of commodity exporting countries, generally known as commodity currencies, are often considered to be driven by some specific commodity prices. In this paper, we show that the uncertainty common to a basket of commodity prices is also a significant driver of exchange rate dynamics for a panel of commodity exporting countries. In particular, an increase in global commodity price uncertainty leads to a short-run depreciation of the effective exchange rate in commodity currency countries, followed by a medium-term rebound. We document that this pattern is specific to commodity currencies and is not visible on benchmark currencies like the euro or the U.S. dollar, the latter acting as a typical safe haven currency. We refer to this pattern as the “commodity uncertainty currency” property.

Integrating stochastic mine planning model with ARDL commodity price forecasting

Commodity price is of paramount importance to mine planning among other stochastic factors such as mining cost and geological uncertainties. Commodity price uncertainty is critical in the determining block values, and it has been incorporated into mine planning by simulation-based methodologies to deal with the effects of price uncertainty during the mining process. In this article, stochastic optimisation was undertaken using parametric minimum cut network flow algorithm for ultimate pit and pushback design, while stochasticity of commodity prices was incorporated into the planning using Autoregressive Distributed Lag (ARDL) simulation model. The push back design and cashflows in two scenarios, namely when the prices are going up and when the prices are going down, were investigated. Moreover, for each scenario, three types of price inputs were considered: (a) actual prices; (b) ARDL forecasted prices; and (c) stochastic prices generated from ARDL forecasted prices. The results of cashflows indicated that when commodity price moving up scenario, the ARDL forecasted prices underestimate the NPV by about 17% and the stochastic minimum cut underestimates the NPV by 72.27% compared to actual price. Similarly, for the price declining scenario, the ARDL forecasted prices underestimated the NPV by about 3% compared to the actual, while the stochastic minimum cut has an NPV value that is 277% of the actual value. Lastly the value of stochastic programming (VSP) was evaluated. The VSP was about 9.2 million dollars when the prices were going up, while the VSP was about 27.1 million dollars, when price was declining. Moreover, declining commodity price produced negative cashflows which indicate need for risk management with a recourse action to evert losses.

Commodity price uncertainty and international trade

Abstract We empirically investigate the impact of commodity price uncertainty on US and Euro Area (EA) trade flows. Our results indicate that the response of US and EA trade flows to commodity uncertainty shocks is larger, in magnitude and persistence, when compared with the respective impact of commodity supply and demand shocks. Moreover, our analysis shows that a one-standard deviation shock in commodity price volatility has a higher (in magnitude) and more persistent effect on trade when compared with the respective shocks in exchange rates and commodity prices. Finally, an uncertainty shock in various agricultural and metals markets has a similar negative impact on trade flows to that of energy uncertainty shocks.

Does commodity price uncertainty matter for the cost of credit? Evidence from developing and advanced economies

In this study, we focus on the effect of commodity price uncertainty on the cost of bank credit for a broad sample of loans traced to firms operating in developing and advanced economies. Using loan-level data for the 1990–2019 period, we find novel evidence that commodity price uncertainty, as estimated by a Bayesian Dynamic Factor Model, increases the cost of bank loans particularly for commodity dependent firms operating in developing countries vis-à-vis commodity dependent firms operating in advanced economies. In a further analysis, when examining the effect of group specific commodity price uncertainty on cost of borrowing, we find that agricultural price uncertainty significantly increases the cost of credit of commodity dependent firms operating in developing economies. We also find that commodity price uncertainty rises the cost of bank credit more for loans traced to firms locating in bank-based countries as compared to loans granted to firms operating in market-based economies, suggesting that the financial structure of a country could play an important role in passing through the borrowing costs to firms. Lastly, we also find that the effect of commodity price uncertainty is more pronounced for smaller firms operating in developing countries as opposed to smaller firms operating in developed countries. All in all, the above evidence provides useful policy implications, particularly, for the longevity of corporate sector operating in developing countries whereby the local financial structure poses serious threats to firms’ future earnings.

Uncertainty diffusion across commodity markets

ABSTRACT While numerous studies investigate volatility transmission across commodity markets, particularly oil and agricultural markets, uncertainty diffusion across commodity markets remains absent from the literature. This circumstance is primarily related to the lack of appropriate measures of commodity price uncertainty, which differs from volatility. This study focuses on measuring commodity price uncertainty and how it is transferred from one commodity market to another. Our contributions are twofold. (i) We construct an aggregate predictability-based measure of uncertainty for each group of commodity markets and different maturities, and (ii) we analyse uncertainty diffusion across different commodity markets using a vector autoregressive model. Our findings clearly demonstrate a bi-causal uncertainty transfer between agriculture, energy, and industrial markets, excluding precious metals markets. Additionally, the industrial commodity market is assumed to be the transmission channel of commodity uncertainty spread, given its close link with global economic activity. Notably, we validate the efficacy of using industrial uncertainty as a proxy for macroeconomic uncertainty. Finally, our confirmation of precious metals’ insensitivity to other markets’ shocks reinforces its nature as a safe haven.

Commodity price uncertainty as a leading indicator of economic activity

AbstractThis article examines the impact of commodity price uncertainty on the U.S. economic activity. Our analysis indicates that uncertainty in agricultural, energy and metals markets depresses economic activity in the United States. Uncertainty shocks in agricultural and metals markets have a more long‐lasting dampening effect on economic activity and its components, when compared to the effect of oil uncertainty shocks. Finally, we show that when accounting for the effects of macroeconomic and monetary factors, the negative dynamic response of economic activity to agricultural and metals uncertainty shocks remains unaltered, while the respective macroeconomic response to energy uncertainty shocks is significantly reduced.

Commodity Price Uncertainty Comovement: Does it Matter for Global Economic Growth?

Global economic activity is surrounded by increasing uncertainties from various sources. In this paper, we focus on commodity prices and estimate a global commodity uncer- tainty factor by capturing comovement in volatilities of major agricultural, metals and energy commodity markets through a group-specific Dynamic Factor Model. Then, by computing impulse response functions estimated using a Structural VAR model, we find that an increase in the common commodity price uncertainty results in a substantial and persistent drop in investment and trade for a set of emerging and advanced economies. We show that a global commodity uncertainty shock is more detrimental for economic growth than usual financial and economic policy uncertainty shocks. Last, our method- ology turns out to be a way to disentangle the macroeconomic effects of good and bad oil uncertainty: when an oil uncertainty shock is common to all commodities, then the macroeconomic effect is likely to be negative, but when this shock is specific to the oil market, the effect tends to be positive in the short run.

Does Commodity Price Uncertainty Matter for the Cost of Credit? Evidence from Developing and Advanced Economies

Does Commodity Price Uncertainty Matter for the Cost of Credit? Evidence from Developing and Advanced Economies

Commodity Price Uncertainty Comovement: Does It Matter for Global Economic Growth?

Commodity Price Uncertainty Comovement: Does It Matter for Global Economic Growth?

Drivers of Commodity Price Uncertainty: Does the Economic Policy Uncertainty Matter?

Drivers of Commodity Price Uncertainty: Does the Economic Policy Uncertainty Matter?

The Dynamic Spillover Effects of Macroeconomic and Financial Uncertainty on Commodity Markets Uncertainties

The present paper has two main objectives: first, to accurately estimate commodity price uncertainty; and second to analyze the uncertainty connectedness among commodity markets and the macroeconomic uncertainty, using the time-varying vector-autoregressive (TVP-VAR) model. We use eight main commodity markets, namely energy, fats and oils, beverages, grains, other foods, raw materials, industrial meals, and precious metals. The sample covers the period from January 1960 to June 2020. The estimated commodity price uncertainties are proven to be leading indicators of uncertainty rather than volatility in commodity markets. In addition, the time-varying connectedness analysis indicates that the macroeconomic uncertainty has persistent spillover effects on the commodity uncertainty, especially during the recent COVID-19 pandemic period. It has also found that the energy uncertainty shocks are the main drivers of connectedness among commodity markets, and that fats and oils uncertainty is the influence driver of uncertainty spillovers among agriculture commodities. The achieved results are of important significance to policymakers, firms, and investors to build accurate forecasts of commodity price uncertainties.

The Macroeconomic Effects of Commodity Price Uncertainty

This paper studies the macroeconomic effects of commodity price uncertainty (CPU) shocks. Using Australia as a case study, an econometric-based CPU index is proposed to reveal that Australia has experienced an unprecedented increase in uncertainty from the commodity market recently. Evidence from a VAR model shows that CPU shocks have a larger recessionary impact than other relevant uncertainty shocks such as financial, economic and trade policy uncertainty. The empirical results are then interpreted in a non-linear multisector DSGE model of the Australian economy by estimating key parameters in the DSGE model to match its responses to the VAR responses. CPU shocks in the DSGE model, via foreign commodity export demand with price rigidity, trigger a precautionary response and cause a decline in real economic activity.

Effects of diamond price volatility on stock returns: Evidence from a developing economy

AbstractHigh diamond price volatility can have significant impact on Botswana's diamond‐driven economy. The global economic crisis of 2008–2009 saw the local economy characterised by heightened commodity price uncertainty, falling stock prices and dwindling international demand for diamonds. In this paper we employ a number of techniques to analyse and assess the effect of diamond price volatility on stock returns in Botswana. Firstly, estimation of a Markov Switching model reveals that high volatility regimes in diamond prices have become more frequent and persistent since the recession. Secondly, a bivariate GARCH‐in‐Mean VAR model is estimated and the results recognize that diamond price volatility has a positive and significant influence on stock returns in Botswana.

Overinvestment and macroeconomic uncertainty: Evidence from renewable and non-renewable resource firms

Investment is an inherent component of business activities. However, what if firms invest more than they should? This study examines this phenomenon, which is dubbed as overinvestment, among resource firms as induced by the business cycle and macroeconomic uncertainties. The analysis is conducted using unbalanced panel data drawn from 584 resource companies across 32 countries covering 1986 to 2017 in four resource sectors: (1) alternative energy, (2) forestry and paper, (3) mining, and (4) oil and gas producers. The results indicate that the forestry and paper sector overinvests relative to the standard investment level predicted by the investment function regardless of the sample period, while the alternative energy sector tends to underinvest. Also, many emerging economies, including Brazil, China, India, Indonesia, Russia, and South Korea, are found to have overinvested over the last three decades or so. In addition, the results suggest that commodity price inflation plays a more important role in inducing firms’ overinvestment than commodity price uncertainty. It is also found that the home country’s business cycle significantly affects overinvestment, with the sign alternating from negative to positive after the global financial crisis. Furthermore, the finding also shows no significant relationship between global geopolitical risk and overinvestment but a significantly positive relationship is found between global economic and country-level governance policy uncertainties and overinvestment. Lastly, the results suggest that the effect of overinvestment on firm performance after three years is positive, especially for firms in the mining sector.

Practical Applications of Can Commodity Price Uncertainty Indexes Be Improved by Capturing Media Information? The Case of Oil Price Uncertainty

Practical Applications In Can Commodity Price Uncertainty Indexes Be Improved by Capturing Media Information? The Case of Oil Price Uncertainty from the Spring 2020 issue of The Journal of Alternative Investments, authors Yosef Bonaparte (of the J.P. Morgan Center for Commodities at the University of Colorado-Denver), Frank J. Fabozzi (of the EDHEC Business School in Nice, France), and David Koslowsky (of the University of Wisconsin-Superior) present a new and improved method of predicting how much oil prices will fluctuate in the future. Traditional oil price volatility indexes, such as the OVX index, only use historical pricing data on oil options and reflect the market’s expectation about future price volatility. The authors find that the OVX by itself tends to underpredict actual oil price volatility, and note that the changes in oil price volatility have correlated with changes in the frequency of news media reports on oil price uncertainty. They therefore create a new index, the OPUX, that includes data on the frequency of news media reports and social media posts on oil price uncertainty. They find that OPUX predicts actual oil price volatility more accurately than OVX, and they recommend using indexes that incorporate the frequency of news media reports for purposes of predicting future price volatilities. TOPICS:Commodities, mutual funds/passive investing/indexing, information providers/credit ratings

Can Commodity Price Uncertainty Indexes Be Improved by Capturing Media Information? The Case of Oil Price Uncertainty

Uncertainty indexes are used in economics to assess the uncertainty regarding economic policy, and in finance to quantify the uncertainty associated with the price volatility of financial instruments and commodities. Existing uncertainty indexes are typically constructed using realized (historical) price volatility, implied volatility when options are available, and conditional volatility using various econometric models. In this article, the authors present an improved commodity price uncertainty index, an oil price uncertainty index that includes information from news articles and social media. They demonstrate the superior performance of their new index compared with existing indexes that do not include news and social media information. They also create an oil price volatility forecasting model based on media coverage and use it to trade an oil volatility portfolio, obtaining a return that exceeds the average monthly growth of oil price volatility. Altogether, media coverage is a powerful predictor of future price volatility in the oil market. TOPICS:Volatility measures, performance measurement Key Findings • Media information from newspapers and Twitter about oil price uncertainty is combined with the existing OVX oil ETF volatility index to create a new oil price uncertainty index (OPUX). • The OPUX model gives a significantly better prediction of 1-month forward oil price volatility, as measured by an increase in R-squared from 12.1% for OVX to 33.0% for OPUX. • A volatility trading model based on OPUX exceeds the average oil price volatility by 0.29% per month.

A dynamic stochastic programming approach for open-pit mine planning with geological and commodity price uncertainty

Over the last decade, geological uncertainty and its effects on long-term or strategic mine planning and methods for related risk management have been studied. However, the combined effect of geological and commodity price uncertainty has received relatively less attention in the technical literature. A research experiment that addresses both these sources of uncertainty is presented herein and accounts for their differences. In particular, while the current commodity price is known at the beginning of every new mining period, the geology, including the mineral grades, metal content, material types and so on, remain uncertain, even when additional information becomes available. The proposed method first uses a two-stage model to manage the geological uncertainty that leads to a scenario-independent extraction sequence. Based on different metal production targets, a pool of subsets of mining blocks is also precomputed for every period. Then, a stochastic dynamic programming algorithm is developed and employed to define the best policy in terms of metal production targets to follow, depending on the evolution of the related commodity price. This policy follows the scenario tree of the commodity price, as it is scenario-dependent (price only) with non-anticipativity constraints, which is similar to an operator that adapts to a fluctuating market. This new approach is tested through a case study that reveals the counter-intuitive combined effects of both sources of uncertainty. For instance, based on the previous evolution of the commodity price, the obtained policy suggests adaptations of the metal production target that go against common practices of mining operators.

Real option valuation of open pit mines with two processing methods

This paper proposes a real option model for valuing open pit mines with two processing methods under commodity price uncertainty. The model considers the flexibility that open pit mines have in order to decide if the ore being mined will be processed or sent to the waste dump. In addition, it enables to maximize the net present value of the mine through the use of the mining sequence and the extraction rate as decision variables. A numerical example is used to illustrate the model and the effect that price uncertainty has in the design of a mining project. The main result obtained is that an open pit mine can significantly enhance its value under a design that considers two processing methods instead of one.

Pricing options on investment project expansions under commodity price uncertainty

<p style='text-indent:20px;'>In this work we develop PDE-based mathematical models for valuing real options on investment project expansions when the underlying commodity price follows a geometric Brownian motion. The models developed are of a similar form as the Black-Scholes model for pricing conventional European call options. However, unlike the Black-Scholes' model, the payoff conditions of the current models are determined by a PDE system. An upwind finite difference scheme is used for solving the models. Numerical experiments have been performed using two examples of pricing project expansion options in the mining industry to demonstrate that our models are able to produce financially meaningful numerical results for the two non-trivial test problems.

Considerations and potential economic advantages for the in-situ recovery of gold from deep, hard-rock deposits

The combination of commodity prices and price uncertainty, coupled with ever-increasing capital and operating costs of mining and metal production create significant challenges for the metals mining industry. With current (“conventional”) technologies, it may not be feasible to extract and process many known deposits economically. This is because of declining grades, challenging mineralogies and increasingly greater depths, amongst other factors. To overcome these issues, it is appropriate to reconsider the wider application of a step-change technology, such as in-situ recovery (ISR), which reduces or eliminates mining and comminution costs significantly. Although a number of attempts have been made towards gold ISR processing, no commercial applications of this technology exist currently. In this paper, the suitability of various gold-bearing deposit types is discussed with reference to their potential for ISR processing. Solution chemistry-related issues and potential lixiviants for use in ISR are described and the challenges for gold ISR are highlighted. Despite the technical challenges presented, a preliminary techno-economic evaluation indicates the potential profitability of such a process.