Futures Contracts Research Articles

Dynamic screening with liquidity constraints

AbstractWe consider a dynamic screening model with serially independent types where the agent is short-term liquidity constrained. We model a liquidity constraint as a hard constraint that forces the agent to renege whenever he would suffer a loss from fulfilling the contract terms in a given period. In particular, the violation of a liquidity constraint is a verifiable event that future contract terms can condition on. This verifiability leads to less stringent truth-telling constraints than those considered in the existing literature. We show that the weaker constraints do not affect optimal contracting, however. Moreover, we develop a novel method to study private values settings with continuous types and show that a regularity condition that has analogues in the literature on multi-dimensional screening ensures that the optimal contract is deterministic.

Scientific production on risks and financial instruments for commodity management

Introduction: Commodity management benefits from a variety of financial instruments and tools that allow companies to mitigate risks and optimize their operations. Among the most used are futures contracts, contracts for difference, risk management tools such as insurance and investment funds. The objective of this article is to analyze the scientific production on risks and financial instruments for the management of raw materials. Methodology: The research paradigm is mixed, through the combination of qualitative and quantitative methods. A bibliometric analysis was carried out which was complemented with a documentary review. The study was synthesized in three stages and was carried out in the Google Scholar, Scielo and SCOPUS databases, during the period from 1991 to 2024, without limitations in language. Results: The literature review shows an increase in the adoption of digital technologies to improve internal communication and employee engagement, with Brazil as a leader in the Latin American region. In addition, endomarketing is identified as a key element for attracting and retaining talent, especially in sectors with high competition and a shortage of specific skills. Likewise, its contribution to organizational sustainability is highlighted, focusing on the social dimension by promoting the well-being and development of employees. Conclusion: These findings reflect how endomarketing has been integrated into business strategies to align corporate objectives with the values ​​of social responsibility and job satisfaction, consolidating itself as an essential tool in improving productivity and long-term commitment.

Machine Learning Approaches for Modelling Water Futures

Modelling water futures is challenging due to the dynamics of several variables and non-linearity. The traditional models are often inefficient to capture such hidden patterns. Thus, utilizing datasets that include daily price and volume information of water futures, this study evaluates the performance of SVM, Random Forest Regressor, LSTM, GBM, and XGBoost models. The findings indicate that XGBoost outperforms other models in accuracy; however, all models tested perform well and provide accurate predictions. Consequently, it makes valuable contributions to the field of modelling financial products based on water-adjacent entities. Furthermore, it underscores the potential for machine learning in enhancing market predictions for emerging and niche sectors that have thus far been overlooked by market participants but will increasingly become more important as climate change progresses. Lastly, as futures markets for water as a commodity mature (with higher trading volume of futures contracts), this study will serve as a benchmark for better modelling of water futures.

Analyzing the dynamics between crude oil spot prices and futures prices by maturity terms: Deep learning approaches to futures-based forecasting

Predicting crude oil prices is critical due to their significant impact on the global economy. While numerous studies have focused on time series forecasting and machine learning methods to predict crude oil spot prices, the use of futures prices segmented by the maturity terms as predictors is comparatively underexplored. This study explored a deep learning approach to investigate using futures prices across different maturities to predict spot prices. Specifically, this paper analyzes the predictive power of one-, two-, three-, six-, and 12-month crude oil futures contracts. This study employs multiple deep learning algorithms, including Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Temporal Convolutional Neural Network (TCN), to forecast crude oil spot prices. This research investigates the performance of these machine learning models when exploring the relationship between futures and spot prices of crude oil. In addition, this research incorporates extensive hyperparameter tuning to enhance the interpretability of the machine learning models when forecasting spot prices using futures prices, thereby contributing to the field of Explainable Artificial Intelligence (XAI) with an optimal set of hyperparameters. In summary, this research systematically shows results demonstrating predictive power in terms of XAI between spot prices and futures prices with different maturities, and machine learning algorithms.

Local agricultural markets and external shocks – the case of Poland

Abstract We quantify the role of individual factors in shaping the domestic prices of selected grains and oilseeds in Poland and test the hypothesis of whether a favorable supply situation can isolate the domestic market from external shocks. We carried out estimations using error correction and local projection econometric models. The results lead to the following conclusions. First, futures contracts have a significant impact on domestic prices of wheat, corn, and rapeseed. Second, an important factor that determines the prices of wheat, corn, and rapeseed is the EUR/PLN exchange rate. However, the strength of this influence and its time horizon differs strongly depending on the analyzed commodity. Third, the influence of other factors is limited. The obtained results indicate that Poland’s relatively high degree of self-sufficiency in the production of wheat, corn, and rapeseed does not isolate the prices of these agricultural commodities from the impact of external factors.

Generic framework for a coherent integration of experience and exposure rating in reinsurance

Abstract This article introduces a comprehensive framework that effectively combines experience rating and exposure rating approaches in reinsurance for both short-tail and long-tail businesses. The generic framework applies to all nonlife lines of business and products emphasizing nonproportional treaty business. The approach is based on three pillars that enable a coherent usage of all available information. The first pillar comprises an exposure-based generative model that emulates the generative process leading to the observed claims experience. The second pillar encompasses a standardized reduction procedure that maps each high-dimensional claim object to a few weakly coupled reduced random variables. The third pillar comprises calibrating the generative model with retrospective Bayesian inference. The derived calibration parameters are fed back into the generative model, and the reinsurance contracts covering future cover periods are rated by projecting the calibrated generative model to the cover period and applying the future contract terms.

Climate tracking by mountain bumblebees across a century: Distribution retreats, small refugia and elevational shifts

Cold-adapted species endangered by global change are crucial cases for understanding range dynamics and its interface with conservation and environmental decision-making. However, the study of their distribution change is either neglected or conducted at coarse resolutions at continental level, thus having little indicator value for regional and local conservation strategies. In view of climate change, it can be expected that cold-adapted insects should reduce distribution ranges by mainly concentric retreats and moving uphill. To test these hypotheses, we targeted four threatened, high-altitude bumblebees differing in subgenera and elevation ranges, and covering the main central and south European mountains. We performed species distribution models including climate and habitat at a 1 km-resolution, and we estimated elevation uphill and the year of elevation change with broken-line regressions. Results indicate that climate change will likely cause severe future range contractions across large areas, more in the Apennines (80 %–85 % ca) than the Alps and Pyrenees (24–56 % ca), with mostly concentric retreats as future extents will nearly entirely be included in the present ones. Remarkably, since the ‘80 s elevation uplift has started by about 325–535 m, a period coinciding with the beginning of the main warming, and will continue. The size and distribution of climate refugia will challenge conservation: they will be small (2–60 % of current areas) and even vary regionally, but while in the Apennines and Pyrenees they will be nearly entirely within Protected Areas, only a third will be so for the Alps. Such impressive distribution and elevation changes demonstrate that cold-adapted bumblebees can track climate change, reasons for it to be found in the specialist niches mainly requiring narrow temperature ranges and glacier presence. Overall, the distribution changes of cold specialist bumblebees driven by climate change demonstrate that conservation and policy makers should act upon the time dynamics and regional responses because future range contraction, the little availability of new areas and the movement uphill emerge as consistent patterns.

Covenant Strictness Measures

Covenants are crucial components of loan agreements, designed to protect lenders by imposing various restrictions and obligations on borrowers. Despite extensive empirical research on covenant strictness, a universally accepted measure of covenant strictness remains elusive. Existing metrics tend to include only a limited selection of covenants from the comprehensive contract. This study utilizes loan-level credit risk data to evaluate and rank the effectiveness of several widely used covenant strictness measures. Credit risk at the loan level is assessed through two primary methods: the difference between issue ratings and issuer ratings, and the loan credit spread while controlling for fixed effects at the firm level. This approach allows for a more nuanced comparison and provides insights into which measures most accurately reflect covenant strictness and its impact on credit risk. The findings aim to contribute to a better understanding of covenant effectiveness, offering potential improvements for future loan contract structuring and risk assessment practices.

Arbitrage outcome between conventional and Islamic finance of yield and forward rates in bond market

Purpose In this research, arbitrage opportunity is tested between the yield rates computed by the NSS model, and the computed forward rates between conventional and Islamic finance to see any arbitrage opportunity. The research questions are the conventional and Islamic finance yields at the same level and equal to each other to avoid arbitrage? Whether conventional and Islamic forward rates differ significantly and thus create any arbitrage opportunity. This study aims to find the presence or absence of arbitrage between conventional and Islamic finance yield rates. Design/methodology/approach The NSS model is the latest model in calculating yield and forward rates. In the method the error level is minimized so expected yield rate and given yield rate both converged (Vahidin and Anastasios, 2020). When they converged it gives the researchers all six months’ yield rates. For the Nelson Siegal method, all the six months’ yield rates are available and these yield rates can be used to compute the forward rates. Findings The authors concluded there is a significant difference between the conventional yield rate and the Islamic yield rate. It suggests that because there are significant differences, its suggest arbitrage is possible. So anyone interested in making a guaranteed profit. The conventional yield rates are lower; hence, anyone can borrow from the conventional finance system and invest the money in the Islamic financial system because investments are getting higher rates of income in the form of yield rate in Islamic Finance. So, one can make money because of this difference. Statistically, it is possible to make money, but practically, the authors observed the difference, however it is very meager. The arbitrage opportunity between Islamic finance and conventional finance will not affect the economy because the significant difference is too small. The disturbance in the arbitrage opportunity due to the values is very meager and insignificant. Research limitations/implications This research does not address the derivative contracts’ role in risk management; future researchers could take up this as another research. Practical implications This research will be beneficial for financial institutions, especially institutional investors. Besides, this research will help the regulators and investment bankers in assisting where and future losses especially bond portfolios in conventional finance and Islamic finance. This study will also contribute and help the asset manager of mutual funds in the mutual fund industries. Social implications In effect, this research will strengthen the financial system, capital market and bond market, derivative contracts such as options contracts, futures contracts, swap contracts and forward contracts will use computed forward rates for assessing future losses (value at risk [VaR]) and to hedge them (Balakrishnan, 2020). Originality/value As this topic is rarely studied it will increase the literature present in this domain.

Dynamic linkages and contagion effects: Analyzing the linkages between crude oil prices, US market sector indices and energy markets

This paper aims to assess the dynamic linkages between crude oil futures contracts, renewable energy indices, carbon credit futures indices and several US sector market indices by applying the DECO-GARCH model and the connectedness index of Diebold and Yilmaz (2012). The analysis is conducted on a daily data sample from August 2014 to February 2024 and performed at both the aggregated and disaggregated levels. The disaggregated analysis revealed that the correlation between the variables was lower before periods of stress, such as the COVID-19 crisis and the Russian–Ukrainian conflict as well as a complex correlation structure with a diverse mix of positive and negative values between different pairs of variables. Moreover, the static connectedness results in terms of returns underscore a significant degree of interconnection and transmission of shocks between oil prices and sector markets, renewable energy, and carbon credit futures indices. In addition, the results highlight the responsiveness of the clean energy and carbon credit sectors to global circumstances and economic conditions. The study concludes with a dynamic connectedness analysis, highlighting once again the intricate connections and interactions between all the variables, which are exacerbated during periods of market instability and key events. The net interconnectedness analysis demonstrated that changes in crude oil prices have a significant impact on most of the variables analyzed. The findings of this study have clear implications for a wide range of market participants, policy-makers, and individuals managing portfolios, particularly in terms of diversification opportunities.

Optimizing Genetic Algorithm With Momentum Strategy for Technical Trading Rules: Evidence From Futures Markets

ABSTRACTThis paper introduces an innovative genetically optimized dynamic composite strategy for achieving profitability in futures markets. Utilizing daily data from 35 actively traded futures contracts (1984–2022), we highlight the potential advantages of integrating the momentum effect into dynamic moving average strategies. This enhancement can boost the strategy's capability to capture and capitalize on market trends, ensuring consistent and stable returns. The developed dynamically composite technical trading strategy aspires to be a valuable reference for investors and the finance academic community, contributing to advancements in the field.

Pelaksanaan Perjanjian Kerja Waktu Tertentu Terhadap Pekerja PT BTU

Competition for jobs makes workers accept jobs without having to pay attention to the employment relationship system, so that in the future contract workers do not receive legal certainty. Contract workers, hereinafter referred to as fixed-term work agreements (PKWT), in their implementation give rise to multiple interpretations which tend to be violated by employers. The case example that I took occurred at PT BTU which incidentally employed its employees for 5 to 7 years continuously without paying attention to the labor regulations that apply in Indonesia. The research method used in this research is a normative legal research method, which aims to explain the aspects studied in a complete, detailed, clear and systematic manner in legislation. This research uses a statutory approach, where the research process is carried out by examining all laws and regulations related to the legal issue being researched. From the research results, it can be proven that PT BTU violates labor regulations in Indonesia.

How Do Futures Contracts on Agricultural Commodities Respond to El Nino Weather Events?

This study examines how futures contracts on wheat, soybeans, corn and sugarcane respond to El Nino weather events over the period March 2010 to April 2024. We use the monthly ENSO forecast data from the Climate Prediction Center of the National Oceanic and Atmospheric Administration to benchmark the start of an El Nino forecast, which we define as the first reporting month in which a current or forecasted El Nino probability exceeds 50%. We examine two periods around each event: the six months leading up to the El Nino forecast, and the six months following the event date. We find that El Nino weather events have significant effects on commodity futures prices. Wheat, soybean and corn futures prices (expressed in dollars) generally decline after an El Nino forecast, consistent with improved harvests due to an increase in rain. We also find that El Nino events are associated with increased volatility in commodity futures markets. Our study can inform hedgers and speculators who participate in the agricultural derivatives markets, as well as policymakers who may be interested in initiating or adjusting any subsidies granted to farmers or food consumers, as compensation for price pressures.

PRICE DISCOVERY EFFICIENCY IN BIST LIQUIDITY BANK INDEX USING DEFERRED FUTURES: A MULTILAYER PERCEPTRON NEURAL NETWORK APPROACH

The literature indicates that the process of price discovery in spot and futures can be bidirectional. This study novelty lies in its analysis of the spot data of the BIST liquid bank index, a relatively new index in Turkey, using futures contracts of different maturities with a multi-layer perceptron (MLP) artificial neural network model. The efficacy of the models is evaluated by examining the capacity of futures prices to inform spot price discovery. The effectiveness of the MLP models is measured by low mean squared error (MSE) ratios relative to the out-of-samples test series results. The findings indicate that the one- and two-next futures contracts of the liquid bank index are more effective than the nearest futures contracts in explaining spot prices. Additionally, the nearest expiry contracts are observed to exhibit higher variances than the others. The most efficient pricing model including both spot and futures as explaining variables, is autoregression with three lags for spot and two lags for the two next futures contracts. These results must be considered when implementing risk management strategies for individuals engaged in spot and futures transactions.

Legal Risks in Future Housing Purchase and Sale Contracts: A Case Research in Vietnam

A future housing purchase contract stipulates a predetermined sale price between a home buyer and seller, with the transaction set for a future date. This type of contract offers sellers the advantage of price security and continued property use until the sale, mitigating risks associated with market fluctuations and buyer scarcity. Nevertheless, these contracts also present several challenges, particularly the need for clear, transparent terms and careful legal risk management to safeguard the interests of all parties involved. This article uses the method pf analyzing the written law, the method of analyzing the situation trough the researching on secondary sources to analyze anticipated legal challenges in future housing purchase contracts and reviews relevant Vietnamese legal frameworks. It proposes essential contract terms and preventive measures aimed at minimizing risks and protecting the rights of the parties, focusing particularly on the buyer. These insights are intended to guide stakeholders in crafting more secure and equitable future housing purchase agreements.

Risk Management and Different Financial Approaches to Reduce Business Risk

This article examines three different risk management methods: asset hedging by shorting stocks, cash flow hedging using forward contracts, and futures contracts. The article explains each method's rationale, application, advantages, and disadvantages. By presenting and explaining these methods, this study aims to help readers gain insights into their effectiveness in managing risk in different types of transactions. The content of the article supports the readers to have a better understanding and gives some strategies for risk management, and provides guidance for decision-makers to mitigate potential risks in their financial operations. Asset hedging by shorting stocks involves selling borrowed stocks in anticipation of a decline in their price, thereby offsetting the potential loss of a declining asset. This strategy allows an investor to profit from a decline in stock prices. Cash flow hedging through forward contracts involves entering into an agreement to buy or sell an asset at a pre-determined future price, thereby reducing the risk of adverse price movements and ensuring a stable cash flow. On the other hand, futures contracts are standardized agreements to buy or sell an asset at a future date, are traded on public markets, provide legal protection for both parties through financial intermediaries, and allow investors to lock in prices. While these risk management methods have obvious advantages, such as potential gains to offset the risk of asset depreciation during market downturns, protection against adverse price movements, and price stabilization, they also have inherent risks and limitations that need to be carefully considered.

Extant linkages between Shanghai crude oil and US energy futures: Insights from spillovers of higher-order moments

This study is epicentral to analyzing the impact of futures volatility on portfolio and risk management, as extant literature indicates the challenges of using economic variables that fall short of forecasting volatility beyond lagged values. Further, higher moments may be better adaptive to signaling distress during market upheavals. This paper sources data from Bloomberg from March 26, 2018–April 28, 2023, to examine the dynamic spillovers of higher moments among Shanghai International Energy Exchange and US energy futures contracts by constructing realized skewness and kurtosis. Using nonlinear techniques of mutual information and time-varying vector autoregression (TVP-VAR), we show that realized skewness and kurtosis offer significant information on spillover transmission between the two futures markets, primarily through the crises of COVID-19 and the Russia and Ukraine war. Further, we identify that the risks embedded in these future contracts have increased significantly. Our results have important implications for policymakers, investors, and risk managers.

Carbon Emissions Pricing: Linkages Between EU ETS Spot and Future Prices and Completeness of EU ETS Market

The study examines the relationship and linkages between spot and future prices of European Union Emission Trading Systems (EU-ETS) during 2019–2021. Dynamic conditional correlation specification of multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) model was employed to examine the time-varying correlation between spot and future prices. Also, vector autoregression mean model and MGARCH-Baba-Engle-Kroner-Kraft volatility model was jointly estimated to model the spillover between EU ETS spot and future prices in the first and second moments. Lastly, we utilize the variance-covariance matrix of joint mean-variance model estimation to derive the optimal conditional hedge ratio as well as the hedge effectiveness of EU ETS future contracts. Our findings reveal a high conditional correlation and significant spillover between carbon spot and future markets in EU. Further, our study uncovers a high degree of hedge effectiveness for EU ETS future contracts. This is possibly the first study that examines the linkages between EU ETS spot and future prices pertaining to the recent transition stage of phase III and the initial stage of ongoing phase IV of the ETS market. Our findings pinpoint to ETS markets becoming more complete and in turn offering optimal hedging avenues. JEL Codes: G15, C58, Q38

Is Customers’ Financial Reporting Quality Associated with Suppliers’ Decision to Contract?

ABSTRACT Using a unique hand-collected dataset of purchase obligations, we find that customers’ financial reporting quality is positively associated with future supply contracts, indicating that suppliers are more willing to contract with customers who provide them with better information. Further, the association between customers’ financial reporting quality and future supply contracts is stronger for customers with strong bargaining power, which is consistent with suppliers relying more on financial reports when they have less access to customers’ private information. Collectively, our results suggest that customers’ financial reporting quality plays an important role in influencing suppliers’ decisions to contract with customers for the future supply of goods and services. Data Availability: Data for dependent variable are manually collected and data for all other variables are available from public sources cited in the text. JEL Classifications: D80; M41.

Efficient likelihood estimation of Heston model for novel climate-related financial contracts valuation

We propose novel Bitcoin-denominated derivatives contracts on carbon bonds. We consider a futures contract on carbon bonds where its price is expressed in terms of bitcoins. Then, we put forward options on a futures contract of the former type. Governments can use such contracts to hedge climate change and influence the prices of carbon bonds and cryptocurrencies. We show how these derivatives transfer volatility to the bitcoin market without a negative effect in the carbon bonds market. Since the aforementioned options are not yet traded in the market, we price them by assuming that the underlying is driven by the well-known Heston model, where the model parameters are estimated by a novel method based on Shannon wavelets. Heston model belongs to the class of stochastic volatility (SV) models. The discrete observations from the SV model can be seen as a state-space model, that is, a stochastic model in discrete-time which contains two sets of equations, the state equation and the observation equation. While the first describes the transition of a latent process in time, the second shows how an observer measures the latent process at each time period. We infer the properties of the latent variable by means of a filtering algorithm, and we estimate the parameters of the model via maximum likelihood. The evaluation of the likelihood function is a time-consuming task that involves updating and prediction steps of the state variable, leading to the computation of complicated integrals. We calculate these integrals by means of an integration method based on Shannon wavelets, and compare the root mean square error (RMSE) of the estimation with state-of-the-art methods. The results show that the RSME is dramatically reduced in a short CPU time with the use of wavelets.