Search

The article "Mastering the Digital Regulatory Maze: Strategies for Marketing Success in a Complex Landscape" by Daniel Sokol discusses the pervasive nature of digital regulation, covering areas such as privacy, antitrust, AI, and fintech. Marketers must navigate different levels of regulation, including traditional government regulation, public-private regulation, and purely private regulation through contracts. The article emphasizes the importance of understanding and responding to the complex digital regulatory environment to thrive in the digital age, highlighting the need for transparency, accountability, and proactive engagement with regulators to avoid more severe government regulation. [Extracted from the article]

Introduction: This paper constructs a revenue model for an independent electrochemical energy storage (EES) power station with the aim of analyzing its full life-cycle economic benefits under the electricity spot market. Methods: The model integrates the marginal degradation cost (MDC), energy arbitrage, ancillary services, and annual operation and maintenance (O&M) costs to calculate the net profits of the EES power station. Using an iterative optimization approach, we determine the optimal MDC and analyze the economic end of life (EOL) for different types of EES power stations. Results: By examining real-world examples from the California energy market, we find that the full life-cycle benefits of an EES power station peak when its MDC is optimal, at $45/MWh-throughput. Under these conditions, the economic and physical EOL of commercial/industrial EES power station is 9 years, while the economic EOL of residential-grade EES power station is 8 years, which is shorter than their physical EOL of 9 years. Discussion: The study further indicates that the economic life of an EES power station is influenced by multiple factors, and operators need to determine the optimal economic EOL to maximize revenue based on battery degradation characteristics, market conditions and operational strategy. [ABSTRACT FROM AUTHOR]

One of the most interesting applications of blockchain is given by the automated market makers (AMMs). In the paper, we discuss how arbitrage activity between the AMMs and the other exchange nodes can affect the volumes of assets in liquidity pools of constant function AMMs. In particular, we argue that arbitrage superimposes to the constant function in determining the liquidity volumes within the same AMM and across different AMMs. Yet, despite representing an additional condition in the model, equilibrium arbitrage is typically not unique because it may depend on several elements, such as the amount of liquidity in the system and the number of exchange nodes. Hence, the paper discusses how the constant function and arbitrage jointly determine the relationship across the assets' liquidity volume in the pool but not a unique value for such volumes unless further constraints are introduced. Therefore, a platform interested in predicting the pool's liquidity volumes may face indeterminacy as to which equilibrium would prevail. Though arbitrage has been discussed in related literature, equilibrium indeterminacy does not seem to have been pointed out. [ABSTRACT FROM AUTHOR]

This study uses an artificial intelligence (AI) model to identify arbitrage opportunities in the retail marketplace. Specifically, we develop an AI model to predict the optimal purchasing point based on the price movement of products in the market. Our model is trained on a large dataset collected from an online marketplace in the United States. Our model is enhanced by incorporating user-generated content (UGC), which is empirically proven to be significantly informative. Overall, the AI model attains more than 90% precision rate, while the recall rate is higher than 80% in an out-of-sample test. In addition, we conduct a field experiment to verify the external validity of the AI model in a real-life setting. Our model identifies 293 arbitrage opportunities during a one-year field experiment and generates a profit of $7.06 per arbitrage opportunity. The result demonstrates that AI performs exceptionally well in identifying arbitrage opportunities in retail markets with tangible economic values. Our results also yield important implications regarding the role of AI in the society, both from the consumer and firm perspectives. [ABSTRACT FROM AUTHOR]

As more renewable energy sources are integrated into the United Kingdom's power grid, flexibility services are becoming integral to ensuring energy security. This has encouraged the proliferation of Lithium-ion battery storage systems, with 85 GW in development. However, battery degradation impacts both system lifespan and the economic viability of large-scale projects. With rising commodity costs and supply chain issues, maximising the value of energy storage is critical. Traditional methods of mitigating battery ageing rely on static limits based on inflexible warranties, which do not fully account for the complexity of battery degradation. This study examined an alternative, degradation-aware current derating strategy to improve system performance. Using an optimisation model simulating UK energy trading, combined with an electro-thermal and semi-empirical battery model, we assessed the impact of this approach. Interviews with industry leaders validated the modelled parameters and the relevance of the alternative strategy. Results show the degradation-aware strategy can extend battery lifetime by 5–8 years and improve net present value and internal rate of return over a 15-year period compared with traditional methods. These findings highlight the economic benefits of flexible, degradation-aware operational strategies and suggest that more adaptive warranties could accelerate renewable energy integration and lower costs for storage operators. [ABSTRACT FROM AUTHOR]

Some US-listed country exchange-traded funds (ETFs) suffer from chronic and meaningful mispricing in the form of premiums or discounts relative to their fundamental value despite the presence of the creation/redemption mechanism. This mispricing is mainly attributed to the staggered information flow due to nonoverlapping time zones between the market where the ETF is listed and its underlying home market. This study provides out-of-sample evidence on the price formation of Gulf Cooperation Council (GCC) country ETFs and gauges the impact of mispricing on their underlying home markets. The GCC context is particularly insightful because these markets have nonoverlapping time zones with the US and follow distinct trading schedules. Our sample comprises daily data from three countries' iShares that exclusively track the Qatari, Saudi, and Emirati stock markets from 17 September 2015 to 14 March 2023. The results show that GCC ETFs are driven mainly by their net asset values (NAVs), albeit imperfectly, while the S&P500 exerts a relatively mild influence on these ETFs compared to other country ETFs, as reported by prior studies. Moreover, we find that crude oil prices positively and significantly impact GCC ETFs' pricing. When we control for unsynchronized trading days between the US and the GCC home markets, we find a structural difference between overlapping and nonoverlapping trading days. This structural difference manifests in a sluggish adjustment to correct mispricing in the ETF market on the day the home market is closed; however, other variables, including the S&P500, show no discernible difference, which refutes the overreaction explanation. This recurrent pattern is reflected in a clear day-of-the-week pattern in the price discovery these ETFs offer to their underlying home markets. [ABSTRACT FROM AUTHOR]

To address information asymmetry in supply chains, the literature suggests that manufacturers should shift from market governance to hierarchical governance or a combination of both. However, as many manufacturers must continue to rely on market governance, this research introduces a governance strategy, manufacturer–retailer bridge governance (MRBG), for these manufacturers. The research examines the governance effects of MRBG on distributor behavior and the contingent effects of manufacturer status and MRBG strategic orientation. Hypotheses are developed based on in‐depth interviews with sales and purchasing managers and tested in two experiments with sales managers. The results show that MRBG has a double‐edged effect on distributor behavior, and this effect is moderated by manufacturer status. Moreover, the alignment between manufacturer status (high versus low) and MRBG strategic orientation (collaboration versus arbitrage) influences the effectiveness of MRBG. [ABSTRACT FROM AUTHOR]

This research proposes a novel arbitrage approach in multivariate pair trading, termed the Optimal Trading Technique (OTT). We present a method for selectively forming a "bucket" of fiat currencies anchored to cryptocurrency for monitoring and exploiting trading opportunities simultaneously. To address quantitative conflicts from multiple trading signals, a novel bi-objective convex optimization formulation is designed to balance investor preferences between profitability and risk tolerance. We understand that cryptocurrencies carry significant financial risks. Therefore this process includes tunable parameters such as volatility penalties and action thresholds. In experiments conducted in the cryptocurrency market from 2020 to 2022, which encompassed a vigorous bull run followed by a bear run, the OTT achieved an annualized profit of 15.49%. Additionally, supplementary experiments detailed in the appendix extend the applicability of OTT to other major cryptocurrencies in the post-COVID period, validating the model's robustness and effectiveness in various market conditions. The arbitrage operation offers a new perspective on trading, without requiring external shorting or holding the intermediate during the arbitrage period. As a note of caution, this study acknowledges the high-risk nature of cryptocurrency investments, which can be subject to significant volatility and potential loss. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article presents examples of arbitrage deterrence from the pharmaceutical, chemical and auto industries. Based on these cases, it develops two models where a monopolist prices and spends to deter arbitrage. The models differ in whether the lower price is set by the firm or negotiated with a representative of consumers. In both models, imports into the high-price market are completely deterred, but the two markets are nonetheless linked by the threat of arbitrage. If this linkage is ignored and the absence of arbitrage is misattributed to exogenous factors, econometric estimates of firm bargaining power will be biased upwards. [ABSTRACT FROM AUTHOR]

Low-beta stocks deliver high average returns and low risk relative to high-beta stocks, an opportunity for professional investors to "arbitrage" away. We argue that beta-arbitrage activity generates booms and busts in the strategy's abnormal trading profits. In times of low arbitrage activity, the beta-arbitrage strategy exhibits delayed correction, taking up to three years for abnormal returns to be realized. In contrast, when arbitrage activity is high, prices overshoot and then revert in the long run. We document a novel positive-feedback channel operating through firm leverage that facilitates these boom-and-bust cycles. This paper was accepted by Lukas Schmid, finance. Funding: This work is financially supported by National Natural Science Foundation of China [NSFC Grant Number 72303226] and the Key Program of National Natural Science Foundation of China [NSFC Grant Number 72233003]. Supplemental Material: The internet appendix and data are available at https://doi.org/10.1287/mnsc.2023.4929. [ABSTRACT FROM AUTHOR]

Copyright of Research Papers of the Wroclaw University of Economics / Prace Naukowe Uniwersytetu Ekonomicznego we Wroclawiu is the property of Uniwersytet Ekonomiczny we Wroclawiu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we introduce the concept of statistical arbitrage through the definition of a mean-reverting trading strategy that captures persistent anomalies in long-run relationships among assets. We model the statistical arbitrage proceeding in three steps: (1) to identify mispricings in the chosen market, (2) to test mean-reverting statistical arbitrage, and (3) to develop statistical arbitrage trading strategies. We empirically investigate the existence of statistical arbitrage opportunities in crude oil markets. In particular, we focus on long-term pricing relationships between the West Texas Intermediate crude oil futures and a so-called statistical portfolio, composed by other two crude oils, Brent and Dubai. Firstly, the cointegration regression is used to track the persistent pricing equilibrium between the West Texas Intermediate crude oil price and the statistical portfolio value, and to identify mispricings between the two. Secondly, we verify that mispricing dynamics revert back to equilibrium with a predictable behaviour, and we exploit this stylized fact by applying the trading rules commonly used in equity markets to the crude oil market. The trading performance is then measured by three specific profit indicators on out-of-sample data. [ABSTRACT FROM AUTHOR]

Concepts are introduced and applied for analyzing and selecting arbitrage portfolios in the face of uncertainty about initial positions and risk preferences. A stochastic arbitrage opportunity is defined as a zero-cost investment portfolio that enhances every feasible host portfolio for all admissible utility functions. The alternative to the existence of such investment opportunities is the existence of a solution to a dual system of asset pricing restrictions based on a class of stochastic discount factors. Feasible approaches to numerical optimization and statistical inference are discussed. Empirical results suggest that equity factor investing is appealing for all risk-averse stock investors with a wide range of initial position and sufficiently low transactions costs by mixing multiple factor portfolios with high after-cost appraisal ratios, low mutual correlation, and negative exposures to the relevant host portfolios. These findings weaken the case for risk-based explanations for the profitability of factor investing. This paper was accepted by Kay Giesecke, finance. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4892. [ABSTRACT FROM AUTHOR]

Arbitrage trading is a common quantitative trading strategy that leverages the long-term cointegration relationships between multiple related assets to conduct spread trading for profit. Specifically, when the cointegration relationship between two or more related series holds, it utilizes the stability and mean-reverting characteristics of their cointegration relationship for spread trading. However, in real quantitative trading, determining the cointegration relationship based on the Engle-Granger two-step method imposes stringent conditions for the cointegration to hold, which can easily be disrupted by price fluctuations or trend characteristics presented by the linear combination, leading to the failure of the arbitrage strategy and significant losses. To address this issue, this article proposes an optimized strategy based on long-short-term memory (LSTM), termed Dynamic-LSTM Arb (DLA), which can classify the trend movements of linear combinations between multiple assets. It assists the Engle-Granger two-step method in determining cointegration relationships when clear upward or downward non-stationary trend characteristics emerge, avoiding frequent strategy switches that lead to losses and the invalidation of arbitrage strategies due to obvious trend characteristics. Additionally, in mean-reversion arbitrage trading, to determine the optimal trading boundary, we have designed an optimized algorithm that dynamically updates the trading boundaries. Training results indicate that our proposed optimization model can successfully filter out unprofitable trades. Through trading tests on a backtesting platform, a theoretical return of 23% was achieved over a 10-day futures trading period at a 1-min level, significantly outperforming the benchmark strategy and the returns of the CSI 300 Index during the same period. [ABSTRACT FROM AUTHOR]

Mycoheterotrophy, whereby plants acquire both carbon and nutrients from a fungal partner, is an evolutionarily puzzling phenomenon. According to biological market models, mycoheterotrophs have nothing to offer and thus should be shunned as trading partners by discriminating fungi. Nevertheless, mycoheterotrophy is common, particularly among orchids, and an estimated 10% of all plant species are facultatively mycoheterotrophic at early stages in their life cycle. Reconciling mycoheterotrophy with biological market models, I describe how mycoheterotrophs could use arbitrage trading to net a profit of carbon and nutrients, without acquiring either from the abiotic environment. The model requires that mycoheterotrophs simultaneously buy and sell both carbon and nutrients, exploiting variability in the trading ratios offered by mycorrhizal fungi. The model relies on several conditions, including the ability of the mycoheterotroph to form indirect hyphal associations with two or more neighbouring autotrophic mycorrhizal associations, the existence of variable carbon:nutrient exchange ratios among these associations and the ability of mycoheterotrophs to invert the net‐direction of resource trade. Evidence that these conditions occur in a state of nature varies from incontrovertible to plausible given available models. The arbitrage model provides evolutionary rationale for mycoheterotrophy from both the plant and fungal perspective. Accordingly, mycoheterotrophs match trading ratios offered by autotrophic plants and, thus, need not be antagonists. The model makes novel predictions that distinguish it from source‐sink models, most notably in the existence of resource exchange inversions at the plant‐mycorrhizal interface. Finally, the model emphasizes market inefficiencies as the foundation on which mycoheterotrophs construct an arbitrage niche. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Many studies have been done in the field of predicting the Volatility of Commodities; however, very little or no analysis has been conducted on any sector, industry, or indices to identify which model is best to understand the asset's characteristics, as there is a hypothesis that all financial time series can be interpreted by implementing the same model. The primary objective is to identify different tools developed by the researchers in estimating impulsive clustering and leverage effects. A comparison will be made among the available tools of the GARCH family models to suggest the best tool to forecast and calculate volatility with the least error. The data used are historical time series data of Indian base metal indices, i.e., Aluminum (AL), Copper (CO), Lead (LE), Nickel (NI), and Zinc (ZI) from NSE for a period from 1st June 2012 to 31st August 2022 from the official website of NSE of India. The study compared and attempted to identify which GARCH family model is suitable to measure the volatility clustering and leverage effect in Indian base metal indices by reducing the chances of error. The study has revealed that the GRACH asymmetric models, while approximating and predicting the financial time series, can enhance the model's output when it has a high frequency. Here, the asymmetric GARCH models (TARCH, CGARCH, EGARCH, and PARCH) better predict volatility than classic models. This study is original in its approach, as a previous study stated the presence of volatility or leverage effect by implementing any one tool. However, this study will compare available tools to suggest which is appropriate for which sector. This analysis will support future researchers and practitioners in evaluating volatility clustering and the effect of leverage by implementing the appropriate GARCH family model without believing in a hypothesis that a single model is good enough to predict volatility. [ABSTRACT FROM AUTHOR]

We establish the equivalence between a principle of almost absence of arbitrage opportunities and nearly rational decision-making. The implications of such principle are considered in the context of the aggregation of probabilistic opinions and of stochastic choice functions. In the former a bounded arbitrage principle and its equivalent form as an approximately Pareto condition are shown to bound the difference between the collective probabilistic assessment of a set of states and a linear aggregation rule on the individual assessments. In the latter we show that our general principle of limited arbitrage opportunities translates into a weakening of the McFadden–Richter axiom of stochastic rationality, and gives an upper bound for the minimum distance of a stochastic choice function to another in the class of random utility maximization models. [ABSTRACT FROM AUTHOR]

The risk of lending money collected from savers is that it leaves banks liable to default with depositors if events (and hence repayment demands) become 'abnormal'. Even though international and national regulation has been introduced to ensure that a certain level of capital is retained by banks, such regulation can be subverted. The current system of international regulation based on the Basel III agreements does not stipulate a standardised approach for inspection frequency or penalty magnitude. This leaves the potential for regulatory arbitrage. The scientific value of an analysis to optimise regulatory efficiency and reduce such arbitrage is therefore considerable. This work therefore assesses the results of the empirical testing of a model based on the Von Neumann–Morgenstern utility function and consequently proposes that this model be used as a basis for standardising capital adequacy limit infraction penalties on an international level to prevent regulatory arbitrage. A survey is undertaken in order to test the responses of participants on the level of penalty which would deter them from regulatory transgression under different theorised levels of profit and probability of discovery. Based on the responses of two distinct subject groups ('bankers' and 'non-bankers') in different scenarios of hypothetical capital adequacy violation, the Von Neumann–Morgenstern utility function is reviewed against empirical results and revealed to show a semi-strong correlation. Lastly, the analysis reveals the striking similarities of the two groups' responses, posing regulatory implications for the industry. [ABSTRACT FROM AUTHOR]

The Fundamental Theorem of Asset Pricing is extended to a market model over a finite probability space with many assets that can be exchanged into one another under combined fixed and proportional transaction costs. The absence of arbitrage in this setting is shown to be equivalent to the existence of a family of absolutely continuous single-step probability measures and a multi-dimensional martingale with respect to such a family. [ABSTRACT FROM AUTHOR]

Classic inventory theory suggests that inventory plays a vital role in matching demand and supply. This paper provides both country- and firm-level evidence that inventory can be used as a financial instrument to take advantage of arbitrage opportunities in financial markets with limited capital mobility. Using data from China customs' metal imports and firm-level inventory from metal processing industries, we show that firms can utilize the inventory of an imported product to carry lower cost capital into a country with strict capital controls and, thus, gain higher financial returns. Specifically, at the country level, we find that the level of imported metal commodities is positively associated with the expected returns from financial arbitrage. At the firm level, we find that a higher expected return from financial arbitrage will incentivize a firm to increase its inventory level through short-term borrowing. In addition, our evidence supports the notion that firms with higher liquidation value, larger size, and higher sales growth (i.e., firms with higher borrowing capacity) may be more active in using inventory as an instrument to seek higher financial gains. We also utilize a unique regulatory policy shock as a quasi-natural experiment to enhance the causality interpretation of our empirical findings. This paper was accepted by Vishal Gaur, operations management. Funding: This work was supported in part by the National Natural Science Foundation of China [Grant 72201230] and the Research Grants Council of Hong Kong, University Grants Committee [Grant 14504621]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4873. [ABSTRACT FROM AUTHOR]

One of the most interesting applications of blockchain is given by the automated market makers (AMMs). In the paper, we discuss how arbitrage activity between the AMMs and the other exchange nodes can affect the volumes of assets in liquidity pools of constant function AMMs. In particular, we argue that arbitrage superimposes to the constant function in determining the liquidity volumes within the same AMM and across different AMMs. Yet, despite representing an additional condition in the model, equilibrium arbitrage is typically not unique because it may depend on several elements, such as the amount of liquidity in the system and the number of exchange nodes. Hence, the paper discusses how the constant function and arbitrage jointly determine the relationship across the assets’ liquidity volume in the pool but not a unique value for such volumes unless further constraints are introduced. Therefore, a platform interested in predicting the pool’s liquidity volumes may face indeterminacy as to which equilibrium would prevail. Though arbitrage has been discussed in related literature, equilibrium indeterminacy does not seem to have been pointed out.

Given the rapid development of the distributed energy resources (DER), involving DERs into the wholesale market under the market and renewable uncertainties to achieve economic benefits is necessary but challenging. In this work, an arbitraging strategy is proposed for DER aggregators that bridge DERs with the wholesale market through energy trading. Besides, a novel self-adaptive minimax regret (MMR)-based optimal offering model is proposed for the DER aggregator to handle the uncertainties in both renewable generations and market prices. Additionally, an exact and communication-free solution methodology is proposed to resolve the formulated optimization problem with high computational efficiency. In the numerical results, the proposed methods can achieve near-to-optimal profits. Moreover, the performance of the proposed approach remains satisfactory even if the environment becomes very volatile.

Abstract Strong concerns over greenhouse gas emissions have required the construction of non-polluting energy sources such as wind farms and photovoltaic plants. This need, combined with recent technological developments, has enabled the global installed capacity of wind farms in particular to grow from a negligible level in 2001 to more than 900 GW in 2023. However, due to the high variability of wind, this energy source is non-dispatchable, i.e., it cannot be controlled by operators to meet demand in a short amount of time. This problem can be mitigated by the allocation of energy storage systems (ESSs) to appropriate buses. This paper proposes an optimization model for the allocation of large-scale ESSs using a genetic algorithm (GA) and a linear multi-period optimal power flow (LMOPF). The GA enables the best allocation of the ESS to buses, their dimensions (i.e., selection of the power and energy of the ESS) and four types of technology. The LMOPF is used to perform system planning for a horizon of np periods, and to carry out the generation dispatch of a high voltage hydro-thermal-wind system and the charging and discharging processes of the ESS allocated by the GA. Due to the large size of the system and the complexity of resolution, a single-phase network model was chosen, and reactive power was disregarded. The model was tested using a system in the south of Brazil. The results showed that the optimal allocation of ESSs allowed for a hydroelectric energy time-shift, reducing the daily costs of operation.

Integrating solar PV inverters and storage devices into the modern power grid generates multiple power profiles with varying magnitudes. The intermittent nature of PV necessitates installing storage devices to reduce unit commitment challenges and accommodate reserve power. This paper proposes various operational factors to determine the boundaries for individual and aggregated power profiles within a cluster of PV systems and storage units. The study utilizes dispatchability and operating reserve factors (primary and secondary) during storage discharge mode to extract dispatchable power and primary and secondary reserve power. Similarly, storage arbitrage and non-dispatchability factors extract arbitrage, non-dispatchable, and a portion of supplemental or tertiary reserve power during storage charging mode. An optimization model leveraging mixed-integer linear programming is formulated to minimize operational expenditures considering these hybrid PV-storage power profiles. Extrapolated electricity market rates for hybrid power profiles are projected using the prevailing prices of photovoltaic (PV) systems and storage technologies. This paper utilizes the proposed aggregated operational factors as constraints while extracting these power profiles for a cluster of PV-storages. The efficacy of the proposed optimization framework is tested on a modified IEEE 34-bus system by adding 10 PV-storage devices on 10 buses. The final quantitative values correspond to the percent penetration and arbitrage of these power profiles.

Copyright of Wirtschafts- und Sozialwissenschaftliches Institut Mitteilungen is the property of Nomos Verlagsgesellschaft mbH & Co. KG and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Bidirectional charging, such as Vehicle-to-Grid, is increasingly seen as a way to integrate the growing number of battery electric vehicles into the energy system. The electrical storage capacity in the system can be enhanced by using electric vehicles as flexible storage units. However, large-scale applications of Vehicle-to-Grid may require significant expansion of distribution grids. Previous studies lack a comprehensive environmental assessment of related impacts. Contributing to this research gap, this article combines techno-economic grid simulations with scenario-based Life Cycle Assessments. The case study focuses on rural distribution grids in Southern Germany, projecting the repercussions of different charging scenarios by 2040. Besides a Vehicle-to-Grid scenario, a mixed scenario of Vehicle-to-Home, Vehicle-to-Grid, and direct charging is investigated. Results indicate that Vehicle-to-Grid charging increases grid impacts due to higher charging simultaneities and power losses, especially when following spot market prices. Despite these challenges, the secondary use of battery electric vehicles as storage units can offset adverse environmental effects. Bidirectional charging allows for higher use of volatile renewable energies and can accelerate their integration into the power system. When considering these diverse environmental effects, bidirectional charging scenarios show overall lower impacts on climate change per battery electric vehicle compared to direct charging. The insights provided are valuable for researchers, industry, utilities, and policymakers to understand the potential positive and negative impacts of large-scale battery electric vehicle integration. The article highlights the most influential parameters that should be considered before large-scale penetration.

Deep learning models struggle to effectively capture data features and make accurate predictions because of the strong non-linear characteristics of arbitrage data. Therefore, to fully exploit the model performance, researchers have focused on network structure and hyperparameter selection using various swarm intelligence algorithms for optimization. Sparrow Search Algorithm (SSA), a classic heuristic method that simulates the sparrows' foraging and anti-predatory behavior, has demonstrated excellent performance in various optimization problems. Hence, in this study, the Multi-Strategy Modified Sparrow Search Algorithm (MSMSSA) is applied to the Long Short-Term Memory (LSTM) network to construct an arbitrage spread prediction model (MSMSSA-LSTM). In the modified algorithm, the good point set theory, the proportion-adaptive strategy, and the improved location update method are introduced to further enhance the spatial exploration capability of the sparrow. The proposed model was evaluated using the real spread data of rebar and hot coil futures in the Chinese futures market. The obtained results showed that the mean absolute percentage error, root mean square error, and mean absolute error of the proposed model had decreased by a maximum of 58.5%, 65.2%, and 67.6% compared to several classical models. The model has high accuracy in predicting arbitrage spreads, which can provide some reference for investors. [ABSTRACT FROM AUTHOR]

In this paper, we study the pricing of contracts in fixed income markets under volatility uncertainty in the sense of Knightian uncertainty or model uncertainty. The starting point is an arbitrage-free bond market under volatility uncertainty. The uncertainty about the volatility is modeled by a G-Brownian motion, which drives the forward rate dynamics. The absence of arbitrage is ensured by a drift condition. Such a setting leads to a sublinear pricing measure for additional contracts, which yields either a single price or a range of prices and provides a connection to hedging prices. Similar to the forward measure approach, we define the forward sublinear expectation to simplify the pricing of cashflows. Under the forward sublinear expectation, we obtain a robust version of the expectations hypothesis, and we show how to price options on forward prices. In addition, we develop pricing methods for contracts consisting of a stream of cashflows, since the nonlinearity of the pricing measure implies that we cannot price a stream of cashflows by pricing each cashflow separately. With these tools, we derive robust pricing formulas for all major interest rate derivatives. The pricing formulas provide a link to the pricing formulas of traditional models without volatility uncertainty and show that volatility uncertainty naturally leads to unspanned stochastic volatility. [ABSTRACT FROM AUTHOR]

We analyze the problem of constructing multiple buy-and-hold mean-variance portfolios over increasing investment horizons in continuous-time arbitrage-free stochastic interest rate markets. The orthogonal approach to the one-period mean-variance optimization of Hansen and Richard (Econometrica 55(3):587–613, 1987) requires the replication of a risky payoff for each investment horizon. When many maturities are considered, a large number of payoffs must be replicated, with an impact on transaction costs. In this paper, we orthogonally decompose the whole processes defined by asset returns to obtain a mean-variance frontier generated by the same two securities across a multiplicity of horizons. Our risk-adjusted mean-variance frontier rests on the martingale property of the returns discounted by the log-optimal portfolio and features a horizon consistency property. The outcome is that the replication of a single risky payoff is required to implement such frontier at any investment horizon. As a result, when transaction costs are taken into account, our risk-adjusted mean-variance frontier may outperform the traditional mean-variance optimal strategies in terms of Sharpe ratio. Realistic numerical examples show the improvements of our approach in medium- or long-term cashflow management, when a sequence of target returns at increasing investment horizons is considered. [ABSTRACT FROM AUTHOR]

The rise of cryptocurrencies as alternative financial investments, with potential safe-haven and hedging properties, highlights the need to examine their market efficiency. This study is the first to investigate the combined impact of liquidity and volatility features of cryptocurrencies on their price delays. Using a wide spectrum of cryptocurrencies, we investigate whether the COVID-19 outbreak has affected market efficiency by studying price delays to market information. We find that as liquidity increases and volatility decreases, cryptocurrencies demonstrate stronger market efficiency. Additionally, we show that price delay differences during the COVID-19 outbreak increase with higher levels of illiquidity, particularly for highly volatile quintiles. We suggest that perceived risks and high transaction costs in illiquid and highly volatile cryptocurrencies reduce active traders' willingness to engage in arbitrage trading, leading to increased market inefficiencies. Our findings are relevant to investors, aiding in improving their decision-making processes and enhancing their investment efficiency. Our paper also presents significant implications for policymakers, emphasizing the need for reforms aimed at enhancing the speed at which information is incorporated into cryptocurrency returns. These reforms would help mitigate market distortions and increase the sustainability of cryptocurrency markets. [ABSTRACT FROM AUTHOR]

We propose a doubly subordinated Lévy process, the normal double inverse Gaussian (NDIG), to model the time series properties of the cryptocurrency bitcoin. By using two subordinated processes, NDIG captures both the skew and fat-tailed properties of, as well as the intrinsic time driving, bitcoin returns and gives rise to an arbitrage-free option pricing model. In this framework, we derive two bitcoin volatility measures. The first combines NDIG option pricing with the Chicago Board Options Exchange VIX model to compute an implied volatility; the second uses the volatility of the unit time increment of the NDIG model. Both volatility measures are compared to the volatility based on the historical standard deviation. With appropriate linear scaling, the NDIG process perfectly captures the observed in-sample volatility. [ABSTRACT FROM AUTHOR]

Due to its effectiveness as a risk-hedging trading strategy in financial markets, futures arbitrage is highly sought after by investors in turbulent market conditions. The essence of futures arbitrage lies in formulating strategies based on predictions of future futures price differentials. However, contemporary research predominantly focuses on projections of single indicators for the subsequent temporal juncture, and devising efficacious arbitrage strategies often necessitates the examination of multiple indicators across timeframes. To tackle the aforementioned challenge, our methodology leverages a PSO Deep-ConvLSTM network, which, through particle swarm optimization (PSO), refines hyperparameters, including layer architectures and learning rates, culminating in superior predictive performance. By analyzing temporal-spatial data within financial markets through ConvLSTM, the model captures intricate market patterns, performing better in forecasting than traditional models. Multistep forward simulation experiments and extensive ablation studies using future data from the Shanghai Futures Exchange in China validate the effectiveness of the integrated model. Compared with the gate recurrent unit (GRU), long short-term memory (LSTM), Transformer, and FEDformer, this model exhibits an average reduction of 39.8% in root mean squared error (RMSE), 42.5% in mean absolute error (MAE), 45.6% in mean absolute percentage error (MAPE), and an average increase of 1.96% in coefficient of determination (R2) values. [ABSTRACT FROM AUTHOR]

As the trendsetter of the digital currency market, Bitcoin fluctuates dramatically in a short period of time and has received increasing attention from investors. However, its high volatility has brought great uncertainty to the financial market. In this paper, we focus on forecasting the realized volatility of Bitcoin by using an optimized deep learning model. Firstly, we construct a more comprehensive system of factor indicators and employ different methods for feature selection, and find that the Random Forest-based feature selection fits better on the deep learning model. Then, we use the particle swarm optimization (PSO) algorithm to optimize the parameters of gated recurrent unit (GRU) model to improve the prediction accuracy, and the results show that the prediction accuracy of PSO–GRU model is 10.47%, 15.28%, 21.73%, 34.79% better than the GRU model, long-short term memory model, machine learning models and the generalized autoregressive conditional heteroscedasticity model on the mean absolute error, respectively. Finally, we establish an early risk warning scheme for Bitcoin volatility and a butterfly option arbitrage strategy, that provide investors with a reference for reasonable arrangement of trading strategies. [ABSTRACT FROM AUTHOR]

We investigate a class of statistical arbitrage strategies under the assumption that stock prices are driven by fractional Brownian motions. Specifically, the buy-and-hold with a stop-profit threshold strategies are analysed to demonstrate the existence of statistical arbitrage opportunities. Our analysis establishes the conditions for the considered strategy class to yield statistical arbitrage. The Hurst parameter in the fractional Brownian motion-based asset price model is shown to be a determining factor. The analysis is confirmed by a Monte Carlo simulation study. Furthermore, a modified Thompson sampling method is proposed for optimizing the strategy parameters of the selling-threshold and its growth rate to maximize investment performance. [ABSTRACT FROM AUTHOR]

We develop a binary tree pricing model with underlying asset price dynamics following Itô–McKean skew Brownian motion. Our work was motivated by the Corns–Satchell, continuous-time, option pricing model. However, the Corns–Satchell market model is incomplete, while our discrete-time market model is defined in the natural world, extended to the risk-neutral world under the no-arbitrage condition where derivatives are priced under uniquely determined risk-neutral probabilities, and is complete. The skewness introduced in the natural world is preserved in the risk-neutral world. Furthermore, we show that the model preserves skewness under the continuous-time limit. We provide empirical applications of our model to the valuation of European put and call options on exchange-traded funds tracking the S&P Global 1200 index. [ABSTRACT FROM AUTHOR]

The two-dimensional estimation problem of the arbitrage-free option price surface from available best bid-ask quotes is difficult to solve due to insufficient as well as skewed quotes for various strikes and maturities besides the prevalence of arbitrage opportunities in the observed quotes. This article presents a semi-closed form approximation for constructing an arbitrage-free European call option price surface from the observed quotes. The estimated option pricing surface is represented as an interpolation-based convex combination of shape-restricted Bernstein polynomials. A fast and straightforward surface-fitting algorithm is presented based on the quadratic programming method. This research also includes a simulation utilizing the Heston stochastic volatility model to evaluate the proposed method. The empirical applicability of the proposed method is demonstrated using S&P 500 call option price data. [ABSTRACT FROM AUTHOR]

The overstatement of asset collateral values reduces bank capital requirements. We identify this novel form of regulatory arbitrage by studying housing overappraisals, the difference between housing collateral values computed by appraisers and actual transaction prices. We leverage granular loan-level data from Spain and a kink in the scheme of residential mortgage risk weights to show that tighter regulatory requirements cause larger overappraisals. This bias depends on the relationship between appraisers and banks; appraisers inflate mortgage collateral values only for their major customer banks. On average, overappraisals lower risk-weighted mortgages by 9%. Mortgage overappraisals allow banks to free up regulatory capital to support additional risk-taking in the corporate loan market. This paper was accepted by Victoria Ivashina, finance. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4805. [ABSTRACT FROM AUTHOR]

Cryptocurrencies like Bitcoin have received substantial attention from financial exchanges. Unfortunately, arbitrage-based financial market price prediction models are ineffective for cryptocurrencies. In this paper, we utilize standard machine learning models and publicly available transaction data in blocks to predict the direction of Bitcoin price movement. We illustrate our methodology using data we merged from the Bitcoin blockchain and various online sources. This gave us the Bitcoin transaction history (block IDs, block timestamps, transaction IDs, senders' addresses, receivers' addresses, transaction amounts), as well as the market exchange price, for the period from 13 September 2011 to 5 May 2017. We show that segmenting publicly available transactions based on investor typology helps achieve higher prediction accuracy compared to the existing Bitcoin price movement prediction models in the literature. This transaction segmentation highlights the role of investor types in impacting financial markets. Managerially, the segmentation of financial transactions helps us understand the role of financial and cryptocurrency market participants in asset price movements. These findings provide further implications for risk management, financial regulation, and investment strategies in this new era of digital currencies. [ABSTRACT FROM AUTHOR]

In this study, the impact of arbitrage resulting from Covered Interest Parity (CIP) deviations on Korea's long-term interest rates was analyzed, utilizing Vector Error Correction (VEC) models for Granger Causality and Impulse Response Function analyses. This analysis covered the period from February 2002 to September 2023, with a comparative analysis of the periods before and after the Global Financial Crisis (GFC). The Granger Causality analysis indicated that changes in the swap basis reflecting CIP deviation presented a significant Granger causal relationship with the variations in domestic long-term interest rates. Notably, in the post-GFC period, when CIP deviations were relatively pronounced, the incentives for arbitrage trading exhibited a stronger leading effect in terms of inducing changes in domestic long-term interest rates. The Impulse Response Function analysis showed that domestic long-term interest rates significantly and negatively responded to the positive shocks in the swap basis. This response was even more pronounced during the period following the GFC. Additionally, foreign long-term interest rates and monetary policy variables also demonstrated a significant impact on domestic long-term interest rates. These findings imply that the adjustment path back to equilibrium from CIP deviations, driven by arbitrage, was developed more through changes in domestic interest rates rather than exchange rate fluctuations, especially after the GFC. [ABSTRACT FROM AUTHOR]

We provide a formal statistical theory of consistent estimation of the set of all arbitrage portfolios that meet the description of being a stochastic arbitrage opportunity. Two empirical likelihood ratio tests are developed: one for the null that a given arbitrage portfolio is qualified, and another for the alternative that the portfolio is not qualified. Apart from considering generalized concepts and hypotheses based on multiple host portfolios, the statistical assumption framework is also more general than in earlier studies that focused on special cases with a single benchmark portfolio. Various extensions and generalizations of the theory are discussed. A Monte Carlo simulation study shows promising statistical size and power properties for testing the null, for representative data dimensions. The results of an empirical application illustrate the importance of selecting a proper blocking structure and moment estimation method. [ABSTRACT FROM AUTHOR]

This paper develops a dynamic model of tender offers in which there is trading on the target's shares during the takeover, and bidders can freeze out target shareholders (compulsorily acquire remaining shares not tendered at the bid price), features that prevail on almost all takeovers. We show that trading allows for the entry of arbitrageurs with large blocks of shares who can hold out a freezeout—a threat that forces the bidder to offer a high preemptive bid. There is also a positive relationship between the takeover premium and arbitrageurs' accumulation of shares before the takeover announcement, and the less liquid the target stock, the stronger this relationship is. Moreover, freezeouts eliminate the free-rider problem, but front-end loaded bids, such as two-tiered and partial offers, do not benefit bidders because arbitrageurs can undo any potential benefit and eliminate the coerciveness of these offers. Similarly, the takeover premium is also largely unrelated to the bidder's ability to dilute the target's shareholders after the acquisition, also due to potential arbitrage activity. [ABSTRACT FROM AUTHOR]

The increased uptake of variable renewable energy sources has increased electricity price volatility in many energy pool markets, providing an opportunity for storage systems to profit through energy arbitrage. Comparison between the cost or value of storage systems engaging in energy arbitrage should be performed on a levelised basis due to differences in system lifetime. Existing energy arbitrage models with bid/offer curves and imperfect forecasting are typically computationally expensive and are impractical for calculating lifetime levelised cost metrics. In this work, an open-source modular energy arbitrage model with bid and offer curve inputs was developed for a lithium-ion battery energy storage system (BESS) and pumped hydro system (PHS) to analyse lifetime levelised cost and revenue. The mixed integer linear program scheduling module included a new piece-wise linearised description of PHS charging behaviour for rapid optimisation. A one-at-a-time sensitivity analysis indicated that levelised cost and revenue were highly sensitive to discharging efficiency. In a case study based on Australia's National Electricity Market, imperfect forecasting with no risk hedging was found to increase levelised costs by up to 24% and decrease levelised revenue by up to 50% relative to perfect price forecasting, despite 95% of prices being forecast to be within $35/MWh of the actual trading price. BESS levelised costs were more significantly correlated with consistent low risk bids (Kendall Tau-b of 0.75), since the undiscounted capital costs contribute to a larger proportion of the overall costs than in the PHS systems. [ABSTRACT FROM AUTHOR]

Appraisal is a legislatively created right that affords a shareholder the ability to seek a judicial ruling on the jhir value of their stock when their corporation undergoes a merger that they do not support. While this reme* is intended to protect shareholders from faulty merger negotiating, in the 20103 hedge fund petitioners in Delaware flooded the Delaware Chancery Court to use the remedy to make a profit-a tactic called appraisal arbitrage. While appraisal arbitrage theoretically acts as a back-end market check on controller abuses, appraisal litigation is lengthy and requires court resources. Further, appraisal arbitrage allows hedge fund petitioners, many of whom purchased shares of a target corporation after the merger announcement, to profit needlessly. In response to increased appraisal litigation in the late 20103, the Delaware legislature reacted with new statutory solutions, and the Delaware Supreme Court issued three critical opinions; all in an attempt to curb appraisal arbitrage in the Delaware Chancery Court. While these actions seem to limit the aggregate number of appraisal cases in the Delaware courts and influence the outcome of appraisal decisions, two recent Delaware Chancery Court decisions indicate that appraisal arbitrage has not quite left Delaware. This Comment focuses on the history of Delaware's appraisal statute and its use by appraisal arbitrageurs. Through analysis of the rise of appraisal arbitrage, the efforts from the Delaware legislature and Supreme Court to curb it, and two recent Delaware Chancery Court decisions, this Comment argues that appraisal arbitrage still plagues Delaware. This Comment asserts that Delaware must overturn a 2007 Chancery Court decision that allows shareholders who bought shares of a company undergoing a merger after the transaction's record date to claim appraisal rights even though they may not have voted with those shares. Moreover, the Delaware legislature must codify this change and amend its appraisal and merger statutes to correct a share-tracing problem that the 2007 decision resolved. Through these measures, Delaware can keep appraisal arbitrage out of its courts while maintaining the spirit Of the appraisal remedy. [ABSTRACT FROM AUTHOR]

A transparent, rules based, portfolio construction algorithm is proposed for convertible bond arbitrage. Feature selection is based on a mark-to-market approach where the volatility from the embedded conversion option is implied from the traded credit spread and bond price. The resulting volatility term structure created by linking the bond implied volatility to listed equity volatilities provides a forward looking rich versus cheap feature extraction. Each bond's relative value translates to its arbitrage potential as a long volatility position. The approach is back tested across all U.S. dollar bonds from 2014. The resulting portfolio returns track and outperform published index benchmarks, thereby producing a smart beta index. [ABSTRACT FROM AUTHOR]

Seasonal commodity price fluctuations can potentially offer farmers arbitrage opportunities to increase their income. However, smallholder farmers in most of sub‐Saharan Africa often do not exploit these opportunities to the fullest extent possible. To inform this issue, we conducted a randomized controlled trial among 1739 smallholder farmers in Malawi to estimate the impact of two key post‐harvest constraints, lack of appropriate storage technology and commitment issues, on farmers' legume storage and sales decisions. The treated groups received (i) an improved storage technology in the form of two hermetic (airtight) bags, (ii) the same improved storage technology under the condition that farmers store collectively with members of their farmer club in their village, and/or (iii) the improved storage technology under the condition that farmers store collectively at a centralized association warehouse. We analyzed the impacts of these treatments on storage behavior and revenue from sales. Results indicated that addressing the technological and commitment constraints simultaneously had the largest average impacts. One year after the intervention, farmers offered hermetic bags and the village storage program (Treatment 2) stored 24% more legumes at harvest, stored 27% longer, received a 3% higher price for their legumes and ultimately made 12% more on average than farmers in the control group. Farmers in that treatment also improved some (but not all) outcomes compared to farmers in other treatment groups. These findings suggest that combining technology with collective action that is localized and flexible can lead to better post‐harvest outcomes for smallholder farmers. [ABSTRACT FROM AUTHOR]

This paper employs a machine learning approach to capture firm‐pair production similarity, which depicts how firms' production processes resemble each other using textual data in corporate MD&As. We show that production‐linked firms' average return has strong predictive power on focal firm's future stock return. A hedging portfolio yields an annualised return of 11.69%, which cannot be subsumed by existing factor models. For mechanism tests, we find that the main findings are stronger in firms with higher information asymmetry and higher costs of arbitrage. The production‐linkage measure also predicts future unexpected earnings, suggesting it possibly includes valuable information on firm fundamentals. [ABSTRACT FROM AUTHOR]