Explore the vast range of titles available.

Oil has historically been the most significant primary energy source for our daily lives and business activities. However, recent skyrocketing oil prices have been one of the greatest concerns among policymakers, business executives, and the general public due to their impacts on daily necessities, including food, clothing, and automobile transportation. As a result, fast-rising inflation on the global scale is attributed to mounting oil prices. Even though many countries have made a conscious effort to tame oil prices and the subsequent inflation, their efforts are often in vain due to some uncontrollable situations. These situations include the ongoing war between Ukraine and Russia, where Russia began weaponizing its oil resources and limiting oil supplies to its neighboring European countries. Faced with the current energy crisis, a growing number of policymakers and business executives have attempted to develop energy-induced risk mitigation strategies. With this in mind, the primary purpose of this paper is to investigate what may have caused oil price hikes and to determine how significantly oil prices influence commodity prices. This paper then proposes ways to mitigate energy-induced supply chain risks by analyzing four decades of secondary data obtained from multiple sources.

We show that a model featuring an average commodity factor, a carry factor, and a momentum factor is capable of describing the cross-sectional variation of commodity returns. More parsimonious one- and two-factor models that feature only the average and/or carry factors are rejected. To provide an economic interpretation, we show that innovations in global equity volatility can price portfolios formed on carry, while innovations in a commodity-based measure of speculative activity can price portfolios formed on momentum. Finally, we characterize the relation between the factors and the investment opportunity set. Data and the Internet appendix are available at https://doi.org/10.1287/mnsc.2017.2840 This paper was accepted by Neng Wang, finance.

As a crucial component of enterprise marketing strategy, commodity pricing and replenishment strategies often play a pivotal role in determining the profit of retailers. In pursuit of profit maximization, this work delved into the realm of fresh food supermarket commodity pricing and replenishment strategies. We classified commodities into six distinct categories and proceeded to examine the relationship between the total quantity sold in these categories and cost-plus pricing through Pearson correlation analysis. Furthermore, a Seasonal ARIMA model was established for the prediction of replenishment quantities and pricing strategies for each of the categories over a seven-day period. To ensure precise data, we extended our forecasting to individual products for a single day, employing 0–1 integer programming. To align the inquiry with real-world scenarios, we took into account various factors, including refunds, waste, discounts, and the requirement that individual products fall within specific selling ranges. The results show that the profit will be maximized when the replenishment of chili is 39.874 kg and the replenishment of edible mushrooms is 43.257 kg in the future week. We assume that the residual of the model is white noise. By testing the white noise of the model, the analysis of the residual Q statistic results shows that it is not significant in level, which can prove that the model meets the requirements and the obtained results are reliable. This research provides valuable insights into the realm of commodity pricing and replenishment strategy, offering practical guidance for implementation.

In this paper, we examine an application of Ornstein-Uhlenbeck process to commodity pricing in Thailand. Prices of Tapioca Starch, Ribbed Smoke Sheet no. 3, and Thai Hom Mali Rice are investigated. We use three parameter estimation methods: least squares estimation, maximum likelihood estimation, and jackknife estimation in order to find the best estimation for the model. Jackknife technique is the most appropriate estimation for our commodity pricing model, which provides the least sum-squared error of commodity prices.

We focus on an information-based model with noisy anticipation motivated by asset valuation problem. Precisely, the price of an asset is computed from the expectation of the totality of discounted future dividend, conditioned on the market filtration generated by (1) the current and past value of dividend, and (2) a partial information of the future cash flow stream. As a result, we obtained a new solution method to compute a generalized asset pricing formula. Moreover, under a certain condition, the formula can be reduced to a simple form, a linear combination between dividend and noisy anticipation. The approach can be applied to approximate a reasonable price of the commodities even without knowing the actual demand and supply.

This commentary is a response to the Hodge et al. (Miner Econ 35: 587-606, 2022) article on global mining industry corporate profiles, complexity, and change. The author states that the total number and distribution of mining companies may be weighted towards non-OECD developing countries, especially with regard to small to medium size companies. This difference may in turn influence corporate sustainability practices and performance, consequently affecting the trust deficit. This assertion is supported by a brief comparison of the Global Top 50 mining companies based on Sustainalytics data. Companies from OECD countries tend to perform much better than non-OECD country–based companies, especially the Chinese and Indian companies. The corporate profile differences and sustainability performance country by country clearly exert an influence on the trickle-down effect as well. Real and imaginary reasons for trust deficit in different countries may also vary considerably. There is clear evidence that overall adverse impacts related to mining activity may be much greater in many developing countries. This does not, however, prevent or discourage the development of strong anti-mining movements in many developed countries, even though it is the developed world which has a huge appetite for commodities, particularly to meet the demands of the highly ambitious Green Transition. This creates a sustainability dilemma, where the much-sought-after commodities are sourced mostly from jurisdictions where adverse effects on the environment and society are potentially greatest. The author proposes enhanced research and dialogue to be undertaken on these topics by all industry parties to improve understanding on the matter, which should also contribute to policy and decision-making. Eventually, the commodity pricing mechanisms should reflect sustainability performance, providing yet further incentives for the producers to continuously improve performance.

This paper presents a novel multivariate mean-reverting jump-diffusion model that incorporates correlated jumps and seasonal effects to capture the complex dynamics of commodity prices. The model also accounts for the interplay between price volatility and convenience yield, offering a comprehensive framework for commodity futures pricing. By leveraging the Feynman–Kac theorem, we derive a partial integro-differential equation for the conditional moment generating function of the log price, enabling an analytical solution for pricing commodity futures. This solution is validated against Monte Carlo simulations, demonstrating high accuracy and computational efficiency. The model is empirically applied to historical futures prices of natural rubber from the Thailand Futures Exchange. Key parameters—including commodity price dynamics, convenience yields, and seasonal factors—are estimated, revealing the critical role of jumps and seasonality in influencing market behavior. Notably, our findings show that convenience yields are negative, reflecting higher inventory costs, and tend to increase with rising spot prices. These results provide actionable insights for traders, risk managers, and policymakers in commodity markets, emphasizing the importance of correlated jumps and seasonal patterns in pricing and risk assessment.

We extend the literature on commodity pricing by incorporating a link between the spread of forward prices and spot price volatility suggested by the theory of storage. Our model has closed form solutions that are generalizations of the two-factor model of Gibson-Schwartz (1990). We estimate the model on daily copper spot and forward prices using the Kalman filter methodology. Our findings confirm the link between the forward spread and volatility, but also show that the Gibson-Schwartz (1990) model prices forward contracts almost as well. In the pricing of option contracts, however, there are significant differences between the models. [PUBLICATION ABSTRACT]

This paper generalizes a market-based indicator for financial sector surveillance using a multifactor latent structure in the determination of the default probabilities of an nth-todefault credit default swap (CDS) basket of large complex financial institutions (LCFIs). To estimate the multifactor latent structure, we link the market risk (the covariance of the LCFIs' equity) to credit risk (the default probability of the CDS basket) in a coherent manner. In addition, to analyze the response of the probabilities of default to changing macroeconomic conditions, we run a stress test by generating shocks to the latent multifactor structure. The results unveil a rich set of default probability dynamics and help in identifying the most relevant sources of risk. We anticipate that this approach could be of value to financial supervisors and risk managers alike.

Economic growth, low inflation, and financial stability are among the most important goals of policy makers, and central banks such as the Federal Reserve are key institutions for achieving these goals. In Asset Prices and Monetary Policy, leading scholars and practitioners probe the interaction of central banks, asset markets, and the general economy to forge a new understanding of the challenges facing policy makers as they manage an increasingly complex economic system. The contributors examine how central bankers determine their policy prescriptions with reference to the fluctuating housing market, the balance of debt and credit, changing beliefs of investors, the level of commodity prices, and other factors. At a time when the public has never been more involved in stocks, retirement funds, and real estate investment, this insightful book will be useful to all those concerned with the current state of the economy.