Reserve Price Research Articles

What Determines Egypt’s Demand for Foreign Reserves?

Objective: The main objective of this paper is to identify the determinants of the demand for foreign reserves in Egypt. Methods: The buffer stock model, quarter-century data spanning over the past 20 years, and Auto-Regressive Distributed Lag (ARDL) regression are all employed to estimate Egypt's demand function for foreign reserves, assess its stability, and predict how swiftly it may change if the underlying Cointegrated connection is disturbed. Results: The results reveal that exports and imports have elasticities of demand for foreign reserves of 1.16 and -3.69, respectively. Moreover, Egypt's foreign reserve assets have notably increased due to foreign direct investment and foreign portfolio investment growth. Lastly, the demand function for foreign reserves is steady, and in the event of a shock, it returns to its steady state in less than two years. Conclusion: To improve Egypt's foreign reserve holdings, policymakers should concentrate on measures encouraging exports and attracting foreign investment.

A market framework to exploit the multi-energy operating reserve of smart energy hubs in the integrated electricity-gas systems

Faced with increasing uncertainties in the integrated electricity and gas system (IEGS), the multi-energy operating reserve(MOR), i.e., extra electric and gas reserve capacity, is becoming vitally important to guarantee the reliable operation of the system. Smart energy hubs(SEHs) located at the demand side of the IEGS can adjust their electricity and gas consumption through energy substitution, which has great potential to provide the MOR. Current studies mainly concentrate on the energy trading mechanism among different SEHs in the energy-only market, which may not effectively incentivize SEHs to provide MOR for system operation. Besides, due to the limited energy capacity, SEHs cannot directly participate in the wholesale reserve market along with other large-capacity resources, e.g., power plants and gas wells. To address these, a two-stage market framework is proposed for exploiting the multi-energy operating reserve of SEHs. In the first stage, the wholesale market is cleared considering the participation of large-capacity resources to determine the clearing results of MOR. Based on the results, an energy-loss-cost calculation model is developed to measure the value of the specific MOR level. By altering the reserve requirements, the values of different MOR levels can be accordingly calculated to formulate the multi-energy operating reserve demand surface(MOR-DS), which can quantify varying MOR values with different levels of the reserve. With the MOR-DS describing the price elasticity of the MOR, a distributed MOR clearing mechanism is developed in the second stage to determine the quantity and price of the reserve from SEHs. The clearing mechanism is constructed as a distributed optimization model considering optimal collaboration among different SEHs. Case studies verify that the proposed market framework can effectively stimulate SEHs to provide MOP through economic incentives, which can realize the reliability improvement of IEGS and the revenue increase of SEHs.

Multi-side incentive compatible transaction fee mechanism

Propelled by ongoing advances in blockchain technologies, transaction volumes of major cryptocurrencies like Bitcoin and Ethereum have surged, causing strain on network capacities—a challenge known as the scarcity of transaction inclusion resources. While the widely adopted Transaction Fee Mechanism (TFM) efficiently allocates transaction resources, existing research falls short in achieving simultaneous user incentive compatibility (UIC), miner incentive compatibility (MIC), and miner-user side contract proofness (SCP), essential for blockchain security. In response, we propose the Optimal Revenue Burning (ORB) mechanism, adjusting reserve price and burning rate to deter deviations from honest strategies. Our theoretical analysis strongly supports that ORB mechanism simultaneously satisfy UIC, MIC, and SCP. We also outline conditions for equilibrium strategies. Through simulations, ORB mechanism not only ensures stable miner revenue but also reduces user payments by an average of 5.34%, addressing exorbitant transaction fees. Our research has significant implications for guiding blockchain TFM design.

Towards a circular economy with waste‐to‐resource system optimization

AbstractMotivated by mounting pressures to achieve environmental sustainability, and the emergence of online waste exchange platforms, in this work we propose an optimization‐based framework for studying waste‐to‐resource formation in the industry. We first develop a linear programming market clearing model comprising self‐interested agents participating in waste‐to‐resource trading. We then embed this in a bi‐level capacity optimization problem under uncertainties in the agents' reserve prices, the goal of which is to maximize the economic savings achieved by waste‐to‐resource. We show that the resulting robust optimization models admit linear mixed integer programming formulations that can be readily computed using available solvers. Furthermore, we provide the formulas for applying an equitable levy and incentive scheme, to enable capacity cost‐sharing among the agents. Numerical studies based on a case of organic waste streams demonstrate the usefulness of the proposed models in generating valuable insights for decision support in the design of waste‐to‐resource markets.

Euro area monetary policy and TARGET balances: A trilogy

The growth in TARGET balances after 2009 has given rise to intense academic and public debate. Our paper offers a systematic exposition of the necessary conditions for TARGET balances to emerge and provides a clear link to monetary policy. We show that large TARGET balances can only arise with excess liquidity. The interpretation of TARGET balances therefore depends on the monetary policy context in which excess liquidity is created. We distinguish three phases of TARGET balances growth and propose some easy-to-derive metrics for policy makers and academics to assess developments in TARGET balances. We develop a comprehensive econometric framework to account for relevant factors driving TARGET balances in the different phases. We find that while financial market stress and economic imbalances were the drivers of TARGET balances during the great financial and sovereign debt crises, the implementation of Eurosystem asset purchases was the driving force since March 2015. As excess liquidity is likely to persist on account of higher demand for central bank reserves compared to the pre-crisis period, TARGET balances have the potential to remain sizeable in the future.

Auction timing and market thickness

A seller faces a pool of potential bidders that changes over time. She can delay the auction to have a thicker market later on. The seller imposes static distortions (through her choice of reserve prices) and dynamic distortions (through her choice of market thickness). Under a condition on types that generalizes increasing hazard rates, we show that (i) regulating only static distortions can harm efficiency; (ii) when regulating only dynamic distortions, a social planner should reduce market thickness; (iii) if a planner can affect both types of distortions, she should still choose a lower market thickness than the seller, i.e., market thickness is inefficiently high; (iv) the extent of timing disagreement between the seller and a social planner is higher when they both have to use an efficient auction than when they both have to use an optimal one.

The Optimal Supply of Central Bank Reserves under Uncertainty

This paper provides an analytically tractable theoretical framework to study the optimal supply of central bank reserves when the demand for reserves is uncertain and nonlinear. We fully characterize the optimal supply of central bank reserves and associated market equilibrium. We find that the optimal supply of reserves under uncertainty is greater than that absent uncertainty. With a sufficient degree of uncertainty, it is optimal to supply a level of reserves that is abundant (on the flat portion of the demand curve) absent shocks. The optimal mean spread between the market interest rate and administered rates under uncertainty may be higher or lower than that absent uncertainty. Our model is consistent with the observation that the variability of interest rate spreads is a function of the level of reserves.

An information–Theoretic approach to partially identified auction models

We consider a situation in which we have data from ascending auctions with symmetric bidders, independent private values, and exogenous entry in which the bidders’ value distribution is partially identified. Focusing on the case in which the seller intends to use a second price auction, we discuss how to determine an optimal reserve price. We justify the use of maximum entropy, explore the properties of the estimand, determine the asymptotic properties of our maximum entropy estimator, evaluate its behavior in a simulation study, and demonstrate its use in a modest application. As an extension, we propose a maxmin decision rule with entropy regularization, which includes Aryal and Kim (2013) and the maximum entropy solution as extreme cases.

Human health risks associated with the consumption of groundwater in the Gaza Strip

Groundwater of the Gaza Strip, the main source of drinking water for the Gazans, is highly contaminated by several chemicals of natural and anthropogenic origins. The results of this study confirm the findings of several studies conducted over the past two decades. Over those two decades, the population of Gaza has doubled, resulting in heavy demand for the limited reserves of groundwater. After 20 years since the first comprehensive study, it was found that groundwater salinity increased by 30 %, due to seawater intrusion. On the other hand, nitrate (NO3) decreased by 30 %, due to expansion of the sewer network and decrease in the number and distribution of septic tanks. Salinity, chloride (Cl), NO3 and fluoride (F) distribution maps for the year 2022 are very similar to those of the year 2002. This indicates that sources and loads of such contaminants are still the same. Metals and metalloids are still within the permissible limits set by the World Health Organization (WHO). Strontium (Sr) only showed concentrations of 12 mg/L across the Gaza Strip, which calls for further investigations. Maximum concentrations of the NO3 and F were 365 and 2.6 mg/L, respectively. The results of probabilistic risk assessment using Monte Carlo simulation showed that NO3 and F consumption through drinking water were above the reference dose for 35 % and 5 % of the trials performed, respectively. Consequently, the hazard quotient (HQ) is larger than 1 for 35 % and 5 % of the exposure scenarios simulated for these ions. For all metals and metalloids analyzed, HQ were below one (HQ1) indicating no risk; however, Sr presented an HQ 95th percentile equal to 0.19. Exposure routes such as dietary intake and soil ingestion, among others, should be further investigated to ensure that cumulative exposure does not surpass the safety limit. Recent advances in desalination technology should put an end to this truly regrettable situation.

Simulation-based estimation of the economic benefits implied by coordinated balancing capacity procurement and deployment in a day-ahead market model

In this paper we analyze how increasing levels of cooperation in reserve procurement and activation affect the implied economic benefits and the respective network loads assuming various paradigms of coordination. To approach the question we construct a simulation model based on the portfolio-bidding day-ahead market clearing context, which takes into account the stochastic nature of reserve activation. Results show that the application of reserve demand netting is desirable in all considered aspects and benefits tend to saturate with increasing number of cooperating zones. Assuming the simultaneous application of the common merit order and reserve demand netting, in which case the benefits are the most significant, simulations show that reserve activation costs are reduced by 71% in the case of 4 zones, compared to the reference case with no coordination, while increasing the number of cooperating zones from 4 to 6, form 6 to 8 and from 8 to 10, implies only a further incremental improvement of 8, 3 and 2%. Regarding policy aspects the results point out that it may be desirable to facilitate the formation of multiple regional cooperation frameworks, which should be easier to establish compared to full-scale integration of reserve procurement and activation platforms.

Multi-product pricing: A discrete choice model based on Markov Chain Choice Model combined with reservation prices

Finding the optimal selling prices for an assortment of multiple, substitutable products is a problem that occurs daily in many industries. To solve this problem, a new customer choice model is proposed, based on the Markov Chain Choice Model combined with reservation prices. A discrete version of the model is proposed and it is solved to optimality. This discrete model is instrumental in a simulation procedure to estimate the optimal reservation prices in the original continuous model. Important benefits of the proposed model are that the model is intuitive and close to reality, because it includes both the customers' willingness to pay and substitution behavior explicitly, and it can handle any type of correlation between products.

Reserve Prices in Internet Advertising Auctions: A Field Experiment

Reserve Prices in Internet Advertising Auctions: A Field Experiment

The scheduling methods with different demand priorities for shared autonomous vehicle system in hybrid demands mode considering dynamic travel time

The shared autonomous vehicle (SAV) is an emerging intelligent transportation mode driven by artificial intelligence, and the vehicle scheduling method is a crucial technology for SAVs. However, researches on the scheduling method of hybrid demand in SAVs lack consideration of dynamic travel time and an analysis of the impact of demand characteristics on system performances. According to the distinct submission and processing approaches of reservation and real-time demands, this paper proposes two hybrid demands scheduling methods for SAV system: the scheduling method with no demand priority (NDP) and the scheduling method that prioritizing short-term reservation demands (PSRD). The NDP method processes the short-term reservation and real-time demands together. The PSRD method processes the short-term reservation demands first, then inserts other requests into the arrangements of reservation demands. A method for calculating dynamic travel time using the ant colony algorithm is proposed. This method aims to identify the optimal path according to the future road network conditions. The results indicate that the PSRD method can give full play to the advantage of knowing the travel information of reservation demand in advance and has good system performance and running efficiency, especially when the number of reservation demands is large. This forms the foundation for advancing research in the SAV traffic systems.

Motivational stimuli to donate sperm among non-donor students

BackgroundSperm banks face a continuously evolving gap between the increasing demand for sperm donation (SD) vs. limited available reserve. To improve donors’ recruitment and increase supply, motivations towards SD should be investigated specifically among young men who have the potential to become donors. Our aim was to evaluate factors which increase and decrease predisposition to donate sperm among non-donor students, who represent a “potential pool” for possible donors’ recruitment.ResultsNinety-three men fulfilled the questionnaire with mean age of 28.2 ± 4.5 years. The most powerful incentive to donate sperm was financial reward followed by a willingness to help others to build a family (3.8 and 3.4, respectively). The most dominant consideration to decline donation was the fear of anonymity loss and future regret (4 and 3.8). While participants’ willingness for anonymous SD was fair (2.8), the open-identity donation was rated significantly lower (1.75, p < 0.01). Familiarity with recipients and offspring had lower scores (1.9–2.2) as well.ConclusionsYoung single men represent a suitable cohort for anonymous donation. Financial reward and willingness to help others are important positive incentives while anonymity preservation is crucial to maintain their willingness towards SD. Regulatory shifting towards open-identity SD necessitates the establishment of an alternative “potential pool” population as a reliable source to recruit donors.

Selling Assets: Are Sellers Better Off with Strong Buyers?

When do sellers benefit from the presence of a strong buyer? In a second price auction with independent private values and entry costs for buyers, we allow for one buyer to be stochastically “strong.” We show that, when buyers make positive net profits, seller revenue is higher with a stronger buyer. In contrast, when competition is endogenous so that all buyers other than the strong buyer break even, seller revenue is always lower in the presence of a strong buyer. We also study seller actions such as providing buyer subsidies, setting reserve prices, securing multiple strong buyers, bundling assets, and optimally choosing the time of sale that might alleviate the fall in revenue. This paper was accepted by Will Cong, Finance. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4938 .

Seller Compound Search for Bidders*

This article studies a seller's compound search for bidders by a deadline. We show that the optimal search outcomes can be implemented by a sequence of second‐price auctions, characterized by declining reserve prices and increasing search intensities (sample sizes) over time. The monotonicity results are robust in both cases of short‐lived and long‐lived bidders. Furthermore, a seller with short‐lived bidders sets lower reserve prices and searches more intensively than one with long‐lived bidders. We also show that the inefficiency of an optimal search auction can stem from its inefficient search rule.

Identifying and prioritizing marketing strategies for the building energy management systems using a hybrid fuzzy MCDM technique

AbstractPreventing energy waste in residential and office buildings has emerged as a critical issue in both developed and developing countries over recent decades. The growing demand for oil and energy reserves has amplified the urgency of this concern. The deployment of building energy management systems (BEMSs) can lead to timely responses to changes in environmental conditions, the prevention of energy wastage, a reduction in CO2 emissions, and an increase in the longevity of building equipment. Despite the undeniable benefits of BEMSs, their market size remains small, creating challenges for providers in reaching potential customers. This research seeks to identify and prioritize the marketing strategies for BEMSs. A case study was conducted, employing the “Strengths, Weaknesses, Opportunities, and Threats” analysis as a tool for identifying marketing strategies related to BEMSs. This method resulted in the identification of 18 distinct marketing strategies. These strategies were subsequently prioritized using a novel fuzzy multicriteria decision‐making technique, VIkor‐topSIS, considering six specific criteria. The findings of the study suggested a hierarchical influence of six criteria on the BEMS market, arranged in the following order of significance: effectiveness, cost, attainability, complexity, timing, and popularity. Furthermore, the top three marketing strategies for BEMSs were found to be internet advertising strategies, discounts to consumers, and online sales. The analysis of the results has also offered valuable insights into the strengths and weaknesses of the studied BEMS provider, as well as the opportunities and threats present within the BEMS market.

A machine learning-based Biding price optimization algorithm approach

Trading companies of used product market are struggling to gain customers attentaion and to sell the products. The aim of this research is to develop a mechanism that can maximize the sale of products while considering profit implications. The literature review classifies the procurement mechanism. Given the limited-supply nature, that also includes unpredictable quality levels and a procurement mechanism that perceives the company offering prices to suppliers on a single-item basis. The academic literature has not covered such a mechanism. Techniques like those that improve the required bidding strategy are reviewed and considered fit to be included in the support tool as the procedures intention to maximize an objective function depending on the bidding price and contain the probability of winning the auction and the profit made from the proceeding, the motivation laid on the approach that predicts the probability. It is determined that this assembles a Response to Reverse Request for Quotation that meets the assumptions of a First-Price Sealed-Bid(FPSD) auction that potentially includes a hidden reservation price.

Exploring Reserve Price (RP) and Average Revenue Per User (ARPU)

Indian Telecom market is a huge attraction for the entire world on the context of being the cheapest and the largest telecom market in the world. The sector which allows 100% FDI participation has seen withdrawal of many international players like Vodafone and Telenor. This paper attempts to solve this mystery by using the 5G auctions 2022 as a reference model and its benefits over the 4G Auctions. The Auction model of SMRA has been questioned in this paper and newer effective models like CCA has been proposed. The Power of 5G in enabling India to be a fully developed nation by2047 has been reemphasized in this paper. Finally what is a sustainable revenue model for the operators and its linkage to the reserve price has been closely examined. Through this I have tried to establish the missed link between ARPUs and the RPs which could be the answer to a decade long spectrum worries for the nation.

The effects of seemingly nonbinding price floors: An experimental analysis

Price floors are common policies in markets for storable goods such as commodities, bankable emissions permits, and currencies. Hard price floors are implemented as unlimited government buybacks and prevent the price from falling below the floor; soft floors, whether implemented as limited buybacks or as reserve prices in emission permit auctions, allow the market price to fall below the floor. We specify and then test in the laboratory a two-period model with the same properties as our infinite-horizon model, Salant et al. (2022). Theory predicts that asset prices will respond to price floors even in a set of circumstances where the floor seems nonbinding. Most of our experimental findings are consistent with theoretical predictions: a seemingly nonbinding floor can cause the price and carryover to jump up, the jump is higher with a hard floor than a soft one, and when the floor is so low that the theory predicts no effect, none is observed. In contrast to theoretical predictions, however, the soft floor fails to increase the carryover and market price in our experiment.