Abstract Using confidential daily data, we examine the Bank of Israel's foreign exchange interventions from 2013 to 2019. We find that a 1 billion U.S. dollars (USD) purchase leads to a 0.82% depreciation of the Israeli Shekel (ILS)–a strong effect compared to other studies. We show that this effectiveness can partially be attributed to the limited risk‐taking capacity of global banks. The interventions also widen the negative deviation from covered interest parity and influence the higher‐order moments of risk‐neutral expectations derived from options prices. We find that USD purchases shift the USD/ILS distribution upward and reduce crash risk. Moreover, the options market anticipates and prices in upcoming interventions.

Design of trading mode and clearing mechanism for energy storage based on flexible energy blocks participating in spot market

Electricity spot market reform and overcapacity in fossil-fired power firms: A quasi-natural experiment based on China power market

Deep Reinforcement Learning for Scheduling of a Steel Plant in the Electricity Spot Market

Abstract: This paper examines the economic sustainability of some critical agricultural commodities in India (Guar Seeds, Guar Gum, Castor Seeds) during the period between 2015 and 2025. The study analyzes the pattern of prices, structural stability, volatility behavior, and market interactions using daily price data with a view to evaluating market resilience and efficiency. First, stationarity of the series of returns is established through the Augmented Dickey-Fuller (ADF) test, which controls the appropriateness of time series models. The Bai-Perron multiple break test shows structural breaks, and the CUSUM test ensures that segments of stability exist in the data. In the tests, these periods are identified as showing a dramatic change in market behavior and are included in the modeling that follows. Their studies are done in the form of trends and autocorrelation of the movements of prices of all commodities. The GARCH (1, 1) model has been employed in the study to capture both volatility clustering and persistence. It has been demonstrated that the effects of shocks on market volatility are likely to recur, indicating that markets are somewhat resilient against these shocks. Moreover, Granger causality between futures and spot market returns is performed for each commodity, and it can be shown that there is a bidirectional causation in the majority of the commodities. This shows that the two markets are efficient in the discovery of price and the existence of a good flow of information between them. In general, the results have great significance for the necessity to take structural breaks and the persistence of volatility in the analysis of commodity markets. The findings are beneficial to policymakers, traders, and farmers in informing them on how they can enhance risk management practices and assist in the sustainable growth of the market. Such detailed analysis leads to a better understanding of the dynamic behavior of agricultural commodity markets in India over ten years. Keywords: Economic Sustainability, Agricultural Commodity Markets, Market Trends, Price Patterns, Market Resilience

ABSTRACT Periodicity in spot Bitcoin and Ethereum trading activity is present at the one-second frequency. Trade count rises sharply during the first and last seconds of the minute. Rapid trades executed around the turn of the minute are done in smaller sizes, reflect the presence of crossing order flows and do not contribute a meaningful share to price discovery. The pattern of recurring sharp increases in transactions over a short time that do not distort the market dynamics support the idea that such trading activity is execution-driven rather than speculative. Bitcoin and Ethereum perpetual futures lack the pattern of surges in trading activity at the turn of the minute prominently displayed in their spot trading segment. Trading activity in the spot market for Binance Coin, Solana and Ripple does not exhibit spikes in trade count at the intersection of the minute either. The combined evidence discounts or rejects the set of possible explanations of the observed phenomenon that include proprietary trading, crypto wash trading, exchange’s matching engine and retail bots, and is indicative of agency algorithms. The findings imply heterogeneity in the level of institutional adoption within the cryptocurrency market, with Bitcoin and Ethereum exhibiting the highest degree of institutional engagement.

ABSTRACT This study offers a comprehensive assessment of spillovers across four FX market segments and 20 currencies, covering 90% of global FX turnover. By incorporating emerging market currencies and a broad spectrum of FX instruments, it extends the existing research and offers a more complete perspective on transmission mechanisms. The findings reveal a highly structured spillover system: the FX spot market absorbs the most cross-segment stress, the FX swap market closely mirrors its dynamics, and the basis swap market stands out due to its role in domestic spillovers. The analysis demonstrates that financial stress propagates not only across currencies but also systematically between distinct FX market segments. Notably, the study also documents a spillover puzzle, whereby emerging market currencies generate a disproportionately large share of spillovers across all segments relative to their market turnover.

Probability Density Forecasting of Electricity Price Difference in Spot Market

Virtual power plants (VPPs) can aggregate distributed resources across various nodes to participate and collaborate in electricity market trading. Unlike traditional standalone generators or load aggregators, this study leverages the dual role of VPPs as producers and consumers. It introduces a natural risk–hedging mechanism and proposes a price‐acceptance bidding strategy for VPPs in the day‐ahead spot market, which primarily relies on electricity price forecasts. This strategy is compared with scenarios where distributed resources or traditional generators/load aggregators bid independently. The analysis focuses on the success rate of market participation and the actual financial returns. The findings indicate that the proposed strategy based on the natural risk–hedging mechanism substantially enhances the resilience of VPPs in managing market risks and effectively mitigates the negative impacts of price volatility and forecasting errors on their economic benefits.

Aiming to address the problems of knowledge conflict and insufficient physical constraints caused by the heterogeneity of boundary data in the electricity spot market, this paper presents a novel deep learning-based method for boundary data state estimation. Specifically, we propose a Power Estimation and Correction Engine (PEACE), which integrates three key components: a Power Data Multimodal Encoder (PDME), a Power System Mixture of Experts Module (PSMoE), and a Physical Constraint Guidance Module (PCGM). The PDME employs heterogeneous graph representation learning to unify multimodal data. The PSMoE uses an expert network to adaptively fuse information from different modalities based on the characteristics of the input data. The PCGM ensures that the model’s output adheres to the fundamental physical constraints of the power system. The proposed method effectively addresses knowledge conflicts caused by data heterogeneity and ensures the physical feasibility of the estimated states. Experimental results demonstrate the effectiveness of PEACE in providing accurate and reliable boundary data state estimates for the power spot market.

With the heightening financial globalization, the market risk has increased strongly especially at times of geopolitical uncertainty. Gold that has traditionally been considered a safe-haven asset is highly volatile when there is an escalation of geopolitical tensions. Nonetheless, the variability that occurs to the spot and futures markets has not been fully investigated. The paper determines the factor of Geopolitical Risk (GPR) on conditional volatility of gold futures and spot returns by the GARCH-X(1,1) model that incorporates the Student-t innovations. A standardized GPR index is introduced as an exogenous variable in the equation of variance using monthly data between the year 1980 and 2023. We find that, lagged GPR has a positive effect on futures volatility, but that effect is somewhat significant, indicating that futures markets that are more vulnerable to geopolitical shocks are speculative and leveraged derivatives markets. By comparison, the spot market volatility does not exhibit any important effect of GPR, meaning that it is self-enhanced by past shocks. Ljung-Box diagnostics indicate some residual autocorrelation in the futures model, indicating that there is some possibility to refine the model by using alternative GARCH specification, or adding more terms to the mean equation. GARKH-X(t) model is highly suitable in terms of volatility dynamics in the futures, whereas spot volatility is more stable. The study has significant implications on risk management, portfolio hedging policies, and regulatory controls on derivatives markets in times of geopolitical crises.

Abstract Public interest in the pork sector renewed in the wake of COVID-19 disruptions. Policy proposals addressed a range of issues – real and perceived – in meatpacking and the pork sector. Evaluating these policies credibly requires accurately representing the industry structure. One feature of the pork supply chain is the use of alternative marketing arrangements to procure hogs. This study seeks to augment existing literature by fully accounting for heterogeneity across alternative marketing arrangements. A structural econometric model links hog supply and pork demand through a representative packer’s optimal procurement decision of hogs on the spot (negotiated) market. Prices are then discovered for alternative marketing arrangements according to price rules. A market power conjecture allows for testing the source and extent of the representative packer’s market power. Results for the period from 2013 to 2024 indicate a lower degree of market power than found in the existing literature and differential contributions from each type of alternative marketing arrangement. The share of hogs procured via methods from which packers derive market power has consistently grown in for the past two decades, however, so the degree of market power may increase if procurement trends continue.

Market power remains a persistent challenge in liberalized electricity spot markets, where generators can manipulate bids to distort prices and extract rents. Traditional monitoring approaches—such as structural indices or simulation-based models—offer partial insights but fail to capture the nonlinear, spatially correlated propagation of strategic behavior across transmission-constrained networks. This paper develops a diffusion neural learning framework for market power risk assessment that integrates welfare optimization, nodal pricing dynamics, and graph-based deep learning. Specifically, a Graph Diffusion Network (GDN) is trained on simulated spot market scenarios to learn how localized strategic deviations spread through the network, distort locational marginal prices, and alter system welfare. The modeling framework combines a system-wide welfare maximization objective with multi-constraint market clearing, while the GDN embeds network topology into predictive learning. Results from a case study on an IEEE 118-bus system demonstrate that the proposed method achieves an R2 of 0.91 in predicting market power indices, outperforming multilayer perceptrons, recurrent neural networks, and Transformer baselines. Welfare analysis reveals that distributionally robust optimization safeguards up to 3.3 million USD in adverse scenarios compared with baseline stochastic approaches. Further, congestion mapping highlights that strategic bidding concentrates distortions at specific nodes, amplifying rents by up to 40 percent. The proposed approach thus offers both predictive accuracy and interpretability, enabling regulators to detect emerging risks and design targeted mitigation strategies. Overall, this work establishes diffusion-based learning as a novel and effective paradigm for electricity market power assessment under high uncertainty and renewable penetration.

Introduction This paper investigates the effect of a major institutional reform on the Shanghai Futures Exchange: the change in the exercise style of its gold futures option contracts from European to American (“Americanization” reform). We aims to examine the reform’s impact on the efficiency of the gold futures market (the underlying of gold futures options) and the complexity of the cross correlation between gold futures and spot markets. Methods The Multifractal Detrended Moving-Average Cross-Correlation Analysis (MF-X-DMA) method is employed in this study. Additionally, the nonlinear Granger test is used to assess the directional predictive relationship between the gold futures and spot markets. Results The MF-X-DMA results indicate that the “Americanization” reform improves the efficiency of the gold futures market but increases the complexity of the cross correlation between gold futures and spot markets. Moreover, the nonlinear Granger test shows that the unidirectional predictive lead from the futures to the spot market diminishes after the reform. Discussion Our findings suggest that the price discovery function transitions from a simple leader-follower model to a more sophisticated, synchronized system. This study contributes to related literature by providing empirical evidence on the market effects of different option exercise styles from the multifractal perspective.

With increasing renewable energy integration, electricity spot markets exhibit high volatility and uncertainty, making it difficult to balance profit and risk. To address this challenge, this paper proposes Joint (Version 1.0), a multi-agent closed-loop framework that integrates forecasting, strategy, and feedback for coordinated decision-making. The Prediction Agent learns statistical patterns of price spreads to generate distributional forecasts, directional probabilities, and extreme-value indicators; the Strategy Agent adaptively maps these signals into executable bidding ratios through a hybrid mechanism; and the Feedback Agent incorporates settlement results for performance evaluation, CVaR-based risk control, and preference-driven optimization. These agents form a dynamic “forecast–strategy–feedback” loop enabling self-improving trading. Experimental results show that Joint achieves a monthly profit of 146,933.46 CNY with strong classification performance (Precision = 53.25%, Recall = 40.45%, AA = 56.05%, SWA = 57.36%), and the complete model in ablation experiments reaches 157,746.64 CNY, demonstrating the indispensable contributions of each component and confirming its robustness and practical value in volatile electricity spot markets.

In Gabon, cassava sticks are a popular food for the population. In some cases, it is sold via an informal contract between producers and restaurants. But there is little or no studies on this business. The objectives of this research were to compare the performance of women cassava sticks producers (WCSPs) and those of meat products braisers (MPBs) who exchange cassava sticks via informal sales contracts (ISCs), to describe these ISCs and to find the determinants to participate in ISCs. A sample of 157 WCSPs and 41 MPBs was surveyed in four localities in the Haut Ogooué province of Gabon. The sampling method was non-probabilistic, with the imperative of representativeness of each district and activity. Statistical tests of independence and comparison of variables as well as a probit logistic model were used. Results show that the spot market dominates these exchanges. Nevertheless 27% of WCSPs and 43.9% of MPBs were doing business through ISCs. A higher proportion of WCSPs with ISCs are heads of households. Furthermore, ISCs allow WCSPs to increase their monthly revenue by an average of 160%, thinks in particular to significantly higher production. Above-average weekly production and being the head of one’s household were the determinants of WCSPs participation in ISCs. These women producers of cassava sticks with ISCs were also the ones who prepared the most appreciated type of cassava sticks according to the city of residence. The activity of MPBs was highly dependent on cassava sticks. Actors of that activity who had an ISC have higher monthly revenues and created more jobs. They mostly braised imported chicken cuts. Regarding the description of these ISCs, they are all unwritten. In 53.8% of cases, it was the WCSP that approached the MPB. With ISCs in operation, each WCSP delivers on average 3 times per week to its MPB client. In perspective, the life quality of households that benefit from those ISCs should be studied. Key words: Local food, Contractualization, Informal economy, Women, Collective catering, Single parenthood

Bidding strategy for photovoltaic storage station in the electricity spot market based on photovoltaic power prediction

Bounded rational bidding strategy of GenCo in electricity spot market based on prospect theory and distributional reinforcement learning

Investigating empirical bidding curves in the electricity spot market: Expected patterns vs anomalies?

Market-wide position limits (MWPL) and bans on F&O trading in stocks have been enforced in Indian markets since 2004. However, despite the rapid growth in derivative trading volumes in recent years, questions about the optimal position limits and their impact on market quality remain largely underexplored. During the 2019 COVID pandemic, the Securities and Exchange Board of India (SEBI) reduced the MWPL thresholds to 50% of the pre-COVID level to counter systemic risks and extreme market volatility. This regulatory change provides a natural setting to evaluate the impact of changes to MWPL and F&O on market quality in the Indian derivatives market. We find that the changes to MWPL resulted in reduced liquidity and volatility in the spot and futures markets compared to the pre-COVID levels, which declined further during the post-COVID period. However, the volatility in the future markets, particularly the overnight volatility, was greater than the spot market during the ban period. The stocks under repeated bans demonstrated significantly higher overnight volatility in futures, while other volatility measures were higher in the spot market. This analysis offers valuable insights into the evolution of liquidity and volatility in the Indian derivative markets during various pandemic phases.