Future Market Prices Research Articles

Asymmetric spot‐futures prices adjustments in Quebec grain markets

AbstractAsymmetric price transmission has been the subject of many studies in agricultural economics, but few has been said on Quebec grain market. This study uses threshold cointegration models to examine the dynamic relationship between Quebec spot and futures prices. Estimation results with daily, weekly, and monthly data from September 1994 to May 2022 are three‐fold. First, the main point to keep from cointegration tests is that futures prices and Quebec local grain prices are integrated but estimations with daily data are consistent to highlight a nonlinear long‐run relationship. Second, results indicate overall positive asymmetric for daily and weekly prices transmissions from futures to spot market. Third, the positive asymmetric prices transmissions shed light on the oligopoly structure of Quebec grain market and inefficiencies in the pricing mechanism which favor local grain sellers. Results with different data frequencies show that aggregated monthly prices data compared to more disaggregated data such as daily and weekly prices may lead to different results. Results imply that policymakers and market stakeholders could facilitate increased grain market competition. Grain users like Quebec hog producers need to better understand additional tools for their management of price risk like futures market and real time prices movement monitoring.

Techno-economic study of chimneyless electric arc furnace plants for the coproduction of steel and of electricity, hydrogen, or methanol

Electric arc furnace (EAF) is the most common technology for steel production from steel scrap. Although the input energy is mostly constituted by electricity, significant amounts of carbon dioxide are emitted with the exhaust gases, most of which are classifiable as process-related. The main goal of this study is to perform a techno-economic analysis of chimneyless electric arc furnace plants, fed by either scrap or direct reduced iron (DRI), and able to coproduce steel as well as electricity, hydrogen, or methanol. Several plant configurations are investigated, featuring different combinations of oxy-postcombustion, carbon capture, carbon monoxide-rich gas recovery, hydrogen or syngas production by high-temperature electrolysis or coelectrolysis, and methanol synthesis. These configurations are also characterized by decreased false air leakage and by heat recovery for steam production. Results show that all cases allow achieving a substantial reduction of direct carbon dioxide emissions, close to 99% compared to the unabated conditions. From an economic perspective, in a long-term scenario, the internal rate of return is always above 8%, and up to 73% for the DRI-fed case. However, in a short-term scenario, only cases with sole power production are economically viable. Hydrogen and methanol are competitive with market prices only for low electricity costs. In a higher electricity cost scenario, the case of carbon capture and storage is more competitive than the case of carbon capture and utilization. With an electricity cost of 100 €/MWh, a steel premium of 10–40 €/t allows to reach economic feasibility if methanol or hydrogen selling prices are in line with current market conditions. In general, the configurations with DRI-fed furnaces obtain more favorable economic performance than scrap-fed ones. The competitiveness of sole electricity, hydrogen or methanol production configurations depends on the case study and on the future market prices.

Surrogate-based modeling and sensitivity analysis of future European electricity spot market prices

Europe is transitioning towards a sustainable energy economy. Planning these future power and energy systems is multifaceted and complex, requiring long-term projections of future electricity market dynamics. The prognostication of future prices is crucial for power system planning, as these prices will drive investments and impact incentives to build and improve new systems. The price of electricity in different regions impacts transmission investment. Energy systems models have high-dimensional output and are expensive to evaluate, leading to unique challenges in managing the uncertainty in model assumptions. This study presents a novel methodology for determining which cost assumptions are most impactful on the output of a detailed model of the European energy system. Because this model is computationally intensive, only a small number of samples are available to us. A multiple-output support vector regression is used as a surrogate of predicted electricity prices timeseries. We find that solar and wind costs are the primary drivers of electricity prices, but natural gas costs generally drive peak electricity prices and those far in the future.

Imagine and Imitate: Cost-Effective Bidding under Partially Observable Price Landscapes

Real-time bidding has become a major means for online advertisement exchange. The goal of a real-time bidding strategy is to maximize the benefits for stakeholders, e.g., click-through rates or conversion rates. However, in practise, the optimal bidding strategy for real-time bidding is constrained by at least three aspects: cost-effectiveness, the dynamic nature of market prices, and the issue of missing bidding values. To address these challenges, we propose Imagine and Imitate Bidding (IIBidder), which includes Strategy Imitation and Imagination modules, to generate cost-effective bidding strategies under partially observable price landscapes. Experimental results on the iPinYou and YOYI datasets demonstrate that IIBidder reduces investment costs, optimizes bidding strategies, and improves future market price predictions.

Study on the profitability of agricultural enterprises in Ukraine during the russian military invasion of Ukraine

This paper examines the effectiveness of grouping agricultural enterprises according to the wheat harvested area and assesses their profitability. We have developed linear and non-linear regression equations to predict the income for said groups of enterprises. The methodology is designed for cases when future market prices are probabilistic in nature. With the help of the developed methodology, it is possible to calculate the necessary production volumes in the conditions of price fluctuations. We have used the Goldfeld–Quandt parametric test to test the model for heteroscedasticity. Calculations show that agricultural holdings are indeed inefficient, and preference should be given to enterprises with medium crop areas. Application of the Lagrange multipliers method when solving the problem of agricultural enterprise optimization makes it possible to increase profitability. The case of price risk, when future market prices are not deterministic, is considered. Therefore, it is necessary to be guided by two criteria when making managerial decisions: to maximize the expected total net income and to minimize the variance of the total net income.

Electricity price forecasting using quantile regression averaging with nonconvex regularization

AbstractElectricity price forecasting (EPF) is an emergent research domain that focuses on forecasting the future electricity market price both deterministically and probabilistically. EPF has attracted enormous interest from both practitioners and scholars since the deregulation of the power market and wide applications of renewable energy sources, such as wind and solar energy. However, forecasting the electricity price accurately and efficiently is an extremely challenging task because of its high volatility, randomness, and fluctuation. Although quantile regression averaging (QRA) has been demonstrated to be efficacious in probabilistic EPF since the global energy forecasting competition in 2014 (GEFCom2014), it is sensitive to nuisance variables especially when the number of variables is large. The forecasting accuracy will be negatively affected by these nuisance variables. To address these challenges, this study investigates a nonconvex regularized QRA in probabilistic forecasting. Two types of nonconvex regularized QRA select the important inputs obtained from point forecasting to obtain more accurate forecasting outcomes. To demonstrate the effectiveness of the proposed EPF model, two real datasets from the European power market are considered.

Price discovery in Chinese PVC futures and spot markets: Impacts of COVID-19 and benchmark analysis

Despite being a minor futures category, Polyvinyl chloride (PVC) futures have emerging as a vital element in energy and chemical futures domain. Employing three benchmark models component share (CS), information share (IS), and information leadership share (ILS), this study explores the price discovery function of Chinese PVC futures and spot markets. It assesses whether PVC futures have matured into an effective hedging tool and reference point for spot markets, and also examines the impact of the COVID-19 pandemic on this price discovery relationship. Empirical analysis reveals that the futures market has become the primary site for price discovery in the Chinese PVC market. All the models consistently demonstrate a mature price discovery function in PVC futures, providing risk mitigation tools for industry players. However, post-pandemic dynamics indicate that price discovery in PVC markets primarily occurs within the spot market. This suggests that compared to the futures market, the PVC spot market is able to respond more quickly to the strong signals of industrial recovery after the end of the pandemic. The feedback and pricing efficiency of the PVC futures market in response to new market information are also influenced. Furthermore, our study offers better anticipation of future market prices.

Do spot market auction data help price discovery?

This paper contributes to the price discovery literature by establishing, for the first time, the role of commodity spot market auction data. Using the New Zealand whole milk powder market as an example, we show that auction-level data explain the price discovery dynamics above and beyond determinants previously identified as being relevant to spot and futures market price formation. In particular, the price discovery of the futures market rises with the volume of dairy products traded at the auction, signaling that the volume auctioned induces a change in the trading strategies of futures market participants. The whole milk powder discovery process is found to primarily take place in the spot market, which aligns well with the auction predating the introduction of the futures market, its higher volume, and lower trading costs.

AGROPEDIA - One Stop for all Agricultural Needs

In India, more than half of the workforce is employed in agriculture and related sectors, which account for around 17 percent of the country's Gross Value Added (GVA). In India, 60–70% of the workforce is employed in agriculture directly or indirectly. Given the importance of food to human existence, it is always difficult to produce food profitably, efficiently, and with minimum land usage. The project's approach offers farmers a one-stop shop for all of their agricultural requirements. It uses a crop forecast system, a weather analysis model, information about government programmes, a system for renting equipment, and a system for analysing market prices. The system for renting out tools that has been developed here gives users the option of lending out their unused agricultural equipment in order to generate passive money from it. The farmer may make a claim for crop insurance or during crop losses with the aid of proper understanding of these programmes. The farmers are informed by our model, which is now in use, about potential future market price patterns.

The Transmission Effect Test of China’s Rotation Mechanism on the Cotton Reserve Market

As an important strategic material in China, cotton policy has positive significance in stabilizing the safety of domestic cotton, ensuring cotton income, and stabilizing cotton output. At the same time, affected by the future market price, the cotton industry supply chain cost management is particularly important. Based on the data of China’s cotton price, consumption, yield, and wheeling-in and wheeling-out from 1978 to 2016, the simultaneous equation model is used to empirically analyze the transmission effect of the rotation mechanism of reserve cotton (RMRC) on cotton supply and demand. Studies have shown that China’s cotton reserve policy and the RMRC play a positive role in balancing the supply and demand of the domestic cotton market and preventing excessive fluctuations in cotton prices. The transmission effect of the RMRC on domestic cotton yield and consumption is significant and there are certain differences. The transmission effect of domestic cotton yield is stronger than the transmission effect on domestic cotton consumption. The difference is owing to the transfer path of the RMRC being different. We should improve the regulation and control effect of the RMRC on cotton demand, realize the normalization of the RMRC, and clarify the policy objectives of the RMRC.

Multidimensional Risk-Based Real Options Valuation for Low-Carbon Cogeneration Pathways

Energy price fluctuations pose a significant risk and uncertainty to financial investments for new developments in conventional power and freshwater cogeneration facilities. This study attempts to address the problem of making robust valuation for low-carbon energy project investments subject to multi-dimensional price risk, particularly looking at some key research questions: (a) how does the correlation structure, or independence, between the price risks affect the project value; and (b) does adding flexibility in investment enhance or worsen the project valuation, given (a). This study identified three price factors with significant fluctuations that impact conventional power generation, namely: wholesale electricity spot price, natural gas spot price, and CO2 market price. The price factors were used to construct a multidimensional risk model and evaluate investment decisions for cogeneration project expansion in the future based on a low-carbon energy mix. To this end, five cogeneration configurations using combined-cycle gas turbine (CCGT) integrated with solar photovoltaics (PV) and carbon capture and storage (CCS) technologies were assessed. A combined price risk was initially estimated by transforming the given price factors representing maximum covariance using principal component analysis (PCA). The trend and volatilities in the major principal component scores (the combined price risk indicator) were modelled using the geometric Brownian motion stochastic process, whose parameters were determined and then used to perform time-series simulation and generate multiple realisations of the principal component. A back transformation was then applied to obtain the simulated values representing future uncertainties in the price factors. The effect of price risk and uncertainties were subsequently evaluated using a recombining binomial lattice model for real options analysis (ROA). There were financial gains when PV was mixed with conventional natural gas-fired technology. Investment in cogeneration configurations with (a) 25% PV share provided a 53% gain in the extended net present value (e–NPV); and (b) 50% PV share provided a 124% e–NPV gain when compared to the baseline cogeneration system with no PV shares. The analyses demonstrate that PV technology is a better hedging option than CCS against future market uncertainty and price volatility.

Influence of Increasing Renewable Power Penetration on the Long-Term Iberian Electricity Market Prices

In recent years, there has been a significant increase in investment in renewable energy sources, leading to the decarbonization of the electricity sector. Accordingly, a key concern is the influence of this process on future electricity market prices, which are expected to decrease with the increasing generation of renewable power. This is important for both current and future investors, as it can affect profitability. To address these concerns, a long-term analysis is proposed here to examine the influence of the future electricity mix on Iberian electricity prices in 2030. In this study, we employed artificial intelligence forecasting models that incorporated the main electricity price-driven components of MIBEL, providing accurate predictions for the real operation of the market. These can be extrapolated into the future to predict electricity prices in a scenario with high renewable power penetration. The results, obtained considering a framework featuring an increase in the penetration of renewables into MIBEL of up to 80% in 2030, showed that electricity prices are expected to decrease by around 50% in 2030 when compared to 2019, and there will be a new pattern of electricity prices throughout the year due to the uneven distribution of renewable electricity. The study’s findings are relevant for ongoing research on the unique challenges of energy markets with high levels of renewable generation.

Remote sensing monitoring of planting area of Panax notoginseng: A case study of Kaiyuan City in Honghe Prefecture

Panax notoginseng is second only to ginseng Panax notoginseng in the Chinese traditional medicine industry. It has extremely high economic value and medicinal value. However, due to the unsustainable planting characteristics of P. notoginseng. The original planting area of P. notoginseng has been exhausted, so the planting area of P. notoginseng has gradually expanded to other areas, and the planting area of P. notoginseng is also changing. Based on Sentinel-2 and Landsat 8 OLI remote sensing image data from 2018 to 2021, the study used Google Earth engine and Random Forest classification to analyze the spatial distribution information of P. notoginseng planting areas in Kaiyuan City, Honghe Prefecture. The results show that, based on Sentinel-2 data, Landsat 8 OLI data, and other multisource data, comprehensively considering the image spectral characteristics and habitat characteristics of P. notoginseng, and applying the Random Forest classification method. The spatial distribution information of P. notoginseng planting areas can be quickly extracted. After calculation, the transfer matrix indicates that the accuracy of the results is high and can meet the requirements of research and application. According to the analysis of the results, the cultivated area of P. notoginseng showed no significant change in 2018 and 2019. It shows an increasing trend during this period. In 2020, the cultivated area of P. notoginseng decreased slightly. While in 2021, the cultivated area of P. notoginseng increased sharply, and the cultivated area of P. notoginseng was more than twice that of the previous year. However, the price of P. notoginseng has shown a trend of decline year by year. By monitoring the planting area of P. notoginseng and analyzing the relationship between the planting area and the price of P. notoginseng, we can predict the future market price of P. notoginseng and plan the rational planting of P. notoginseng.

Predicting the unpredictable: New experimental evidence on forecasting random walks

We investigate how individuals use measures of apparent predictability from price charts to predict future market prices. Subjects in our experiment predict both random walk times series, as in the seminal work by Bloomfield and Hales (2002) (BH), and stock price time series. We successfully replicate the experimental findings in BH that subjects are less trend-chasing when there are more reversals in random walk times series. We do not find evidence that subjects overreact less to the trend when there are more reversals in the stock price prediction task. Our subjects also appear to use other variables such as autocorrelation coefficient, amplitude and volatility as measures of predictability. However, as random walk theory predicts, relying on apparent patterns in past data does not improve their prediction accuracy.

Power-to-heat for the industrial sector: Techno-economic assessment of a molten salt-based solution

The industrial sector is a major source of wealth, producing about one-quarter of the global gross product. However, industry is also a major emitter of CO2 and it represents a key challenge towards achieving the worldwide CO2 emission reduction targets. Nowadays, about 22 % of the overall energy demand is heating for the industrial sector, generating about 40 % of the global CO2 emissions. Solutions to decarbonize the industrial sector are needed. This work presents the techno-economic assessment of a molten salts based power-to-heat solution aiming at decarbonizing the industrial sector, requiring medium temperature heat (150–450 °C). The system is studied under different electric markets considering electricity prices of 2021, future electricity market price modifications are assessed via sensitivity analyses. Dispatch strategies and system sizing are identified to ensure optimal techno-economic performance. The main performance indicators investigated are the levelized cost of heat, the operational expenditure, and the attainable savings with respect to traditional solutions. The system scalability and sizing criteria are investigated accounting for different typical industrial load profile curves and nominal thermal power demands. The results highlight that nominal levelized cost of heat as low as 55 €/MWhth are attainable in cheap electricity markets (i.e. Denmark) and 80 €/MWhth in more expensive scenarios (i.e. United Kingdom). The proposed system can provide operational cost savings between 0.5 and 3 M€/y against traditional non-flexible electric boilers and even wider benefits if fossil fuels-based boilers are considered. Future electricity markets, subjected to higher renewable penetration causing cheaper average electricity prices and higher price volatility, could lead to further reductions of the levelized cost of heat of more than 20 % achieving values below 45 €/MWhth in markets such as Denmark. This study sets the ground for further power-to-heat techno-economic investigations addressing different industrial sectors and identifies main system design strategies.

Research on the Application of Time Convolution Series in Futures Price Forecasting

Aiming at the problem of anomaly detection of time series data with unbalanced distribution among classes, a detection method based on depth convolution neural network is proposed. With the increasing trading scale of the futures market, it covers more and more economic and financial fields, and the volatility of the futures market is more and more intense, which constantly presents many complex phenomena that cannot be explained by other classical financial theories. Investors predict stock prices based on statistical analysis or simple machine learning methods, but because the stock market is a complex nonlinear dynamic system, these methods have huge limitations. By using the existing data and the chart obtained by the game master software for prediction analysis, the advantages of several technical indicators have been effectively complemented, and the accuracy of prediction has been improved. On the basis of the concept of attribute set and attribute measure, aiming at the problem of pattern recognition, this paper proposes a recognition method, establishes an attribute mathematical model, and combines it with other methods, which has been gradually applied to artificial intelligence, neural network and other fields. It has been successfully applied in the prediction of futures price trend. Building a futures price forecasting model to reveal the inherent law implied in futures market price index and show its evolution mechanism can improve the control ability to deal with economic and financial risks, and provide an objective and rigorous basis for regulatory authorities to formulate relevant policies, which is also the significance of this study.

An online algorithm for the inventory retrieval problem with an uncertain selling duration, uncertain prices, and price-dependent demands

We study a multi-period inventory retrieval problem with uncertain selling duration, uncertain future prices, and price-dependent demands. At the beginning of a finite selling horizon, a fixed amount of inventory is available for sale in future periods. In each period, the decision-maker observes the current market price and makes the retrieval decision, and only at the beginning of the last period, he knows that it is the last period. The objective is to maximize the expected total revenue and the difficulty stems from the lack of information on probability distributions of both the selling duration and future market prices, which also causes uncertainty in the random demands. Proposing an online algorithm ALG-IR to solve the problem based on competitive analysis theory, we derive its constant competitive ratio, which is optimal under certain conditions, showing the robust performance of the algorithm. We further indicate that, when applying other price-dependent demand models or considering the impact of reference prices on the current demand, the adjusted algorithm ALG-IR-M retains all the theoretical properties. When considering the holding cost of inventory, a similar algorithm ALG-IR-H is proposed to solve the problem. Through numerical studies, we show the algorithm achieves good performance in both optimality and robustness, especially when the market is not over-supplied or the selling duration will not suddenly end at an early period.

Does Accounting Measurement Influence Market Efficiency? A Laboratory Market Perspective

ABSTRACTUsing laboratory markets where accounting regimes can be directly compared with equivalent economic parameters, we test whether and how two different accounting measurement bases—historical cost (HC) and mark-to-market (MTM)—influence trader perceptions and asset mispricing. Our results show that traders perceive otherwise equivalent assets differently by regime, consistent with accounting regimes imposing differential information processing costs. In the MTM regime, traders integrate market price information to a greater extent and integrate asset fundamental information to a lesser extent. We also observe that traders in the MTM regime express prospective preferences for information about future market prices, but in HC prefer information about future dividends. These individual-level effects correspond with greater market-level mispricing/bubbles under MTM. Our results suggest that accounting regimes can, on their own, contribute to price bubbles and their subsequent collapse.Data Availability: Data are available on request.

A closed-loop system to recycle rare earth elements from industrial sludge using green leaching agents and porous β-cyclodextrin polymer composite

Industrial sludge is classified as hazardous waste as well as a resource of valuable metals and needs to be adequately treated for the sake of the environment and economy. This study reports a green approach for extracting and purifying rare earth elements (REEs) from industrial sludge using environmentally friendly washing solutions ((NH4)2SO4, N, N-Bis(carboxymethyl) glutamic acid (GLDA), tetrabutylammonium bromide ionic liquid (TBAB), and water) and a porous β-cyclodextrin polymer composite (PCDP-M-SHM) respectively. The results showed that there is no significant difference between used extracting solutions. However, the speciation study indicated that most REEs in the sludge sample were bounded to a water-soluble fraction. Therefore, for economic reasons, water was used for further studies. Furthermore, the recycling efficiencies after the purification of leached REEs with PCDP-M-SHM were in the range of 76.0 % (Gd) to 87.3% (Pr), except for Ce (8.29%).Moreover, cost evaluation and sensitivity analysis studies were performed to investigate the applicability of the reported method at the industrial level. The cost evaluation results estimated $3676 as the cost of processing 1 ton of sludge with revenue of $710/ton of sludge. Finally, the sensitivity analysis test affirmed the profitability to depend on the cost of PCDP-M-SHM. Based on the future market price of REEs, the proposed closed-loop recycling approach could be a promising green solution for environmental problems related to mining, processing, and conservation of REEs resources as well as industrial sludge disposal.

Assessing the economic profitability of Paulownia as a biomass crop in Southern Mediterranean area

The increase of renewable energy production worldwide, occurred in the last years, is also attributable to some agroforestry species cultivated according to the short rotation coppice technique. Although these species are able of enhancing abandoned or marginal land leading to numerous environmental benefits, an increasing number of farmers are introducing them in place of agricultural crops. Therefore, since for a farmer economic sustainability is one of the main factors to introduce a biomass crop, the study aimed at evaluating the profitability of Paulownia, a species that has been spreading in recent years. In particular, an economic analysis has been carried out in a Southern Italian farm in which Paulownia has recently replaced a vineyard, by adopting a discounted cash flow approach. The results show that the Paulownia for both timber and woodchip production, with an annual gross margin equal to 357.91 € ha−1, can represent a valid alternative compared to wine grape (237.41 € ha−1), while Paulownia for exclusively biomass production has almost zero profitability (4.22 € ha−1). However, the profitability depends not only on the product typology but also on the future market price fluctuations, on the subsidies or incentives, as well as on adequate choices by entrepreneurs for the creation of sustainable supply chains also from an environmental and social point of view.