Reserve Price Research Articles

On reservation prices in the all‐pay contest with correlated information

AbstractThis paper investigates the effects of reservation prices in all‐pay auctions based on the Bayesian Nash equilibrium of symmetric distributions with binary and correlated types. Our study finds that reservation prices affect players' behavior in two ways. Given the reservation price, some give up the original strategy of offering a bid lower than such minimum requirement. Therefore, the reservation prices may discourage the effort supply of weaker players. However, stronger innovators do not modify their strategies: They will start giving bids above the reservation price. Hence, setting a quality standard is effective in boosting effort supply. Our study contributes to the function of this particular pattern in the contests, thereby gaining more insight into designing and assessing such competition under different circumstances.

Predicting transaction outcomes under customized pricing with discretion: A structural estimation approach

We consider a firm selling heterogeneous products with prices customized for each customer and the final price is set by negotiations between the seller and the customer. This type of pricing modality is referred to as customized pricing with discretion and is commonly used in insurance, consumer loans, mortgages, and many business‐to‐business markets. We assume that each sales agent has a reserve price and each customer has a willingness‐to‐pay, which are jointly drawn from a distribution and, if the transaction is successful, they agree on a price between these two values based on their relative bargaining power. Given the outcomes of a series of negotiations, our goal is to estimate the underlying joint distribution of reserve price and willingness‐to‐pay, and predict the outcomes of future transactions. We assume that the price that prevails as the outcome of the negotiation can be represented as a generalized Nash bargaining equilibrium. We develop a structural estimation method based on the expectation‐maximization algorithm to estimate the parameters of reserve price and willingness‐to‐pay distribution. Using a real‐world data set from indirect auto‐lending industry, we show that our proposed method, which accounts for heterogeneity in sales agent's reserve price and customer's willingness‐to‐pay, improves the predictive accuracy of final price (APR) and take‐up probability (i.e., probability of a customer accepting the loan) on real‐world test data by about 8.70% and 3.68%, respectively, compared to a (Tobit‐based) model, which accommodates unobserved heterogeneity in customer's willingness‐to‐pay only. Using the structural EM estimates, we conduct counterfactual analyses to understand the impact of different pricing policies, which vary in the amount of discretion provided to the sales agent during the negotiation process. For example, we find that the lender may increase profits by 13.63% compared to the status quo by optimally imposing a customized minimum price (annual percentage rate) below which a loan should not be offered to the customer.

Optimal Procurement with Quality Concerns

Adverse selection in procurement arises when low-cost bidders are also low-quality suppliers. We propose a mechanism called LoLA (lowball lottery auction) which, under some conditions, maximizes any combination of buyer’s and social surplus, subject to incentive compatibility, in the presence of adverse selection. The LoLA features a floor price, and a reserve price. The LoLA has a dominant strategy equilibrium that, under mild conditions, is unique. In a counterfactual analysis of Italian government auctions, we compute the gain that the government could have made, had it used the optimal procurement mechanism (a LoLA), relative to a first-price auction (the adopted format). (JEL D44, D82, H57, L14)

А conceptual model for the formation of oil and gas accumulation zone within the Paleozoic basement of the southeastern West Siberian basin

In modern conditions of high demand for oil reserves in the Russian Federation, the most important region for its production remains the West Siberian oil and gas basin with target objects in well studied and developed Jurassic and Cretaceous sediments. The high degree of depletion of oil reserves at traditional sites and low exploration of complex and unconventional oil accumulations contribute to intensification of prospecting activities in Achimov, Bazhenov and Tyumen deposits, as well as those in deeper and less studied Lower Jurassic, Triassic and Paleozoic deposits located in the basement of sedimentary cover. The issue of oil and gas content of the pre-Jurassic complex of the West Siberian oil and gas basin has been debated for many years by experts, who highly appreciate the potential of identifying new large-scale oil and gas accumulations in such sediments. A series of recent discoveries in the Tomsk region (Urman-Archinsk group) confirms the importance of selecting such an exploration area. A review of existing ideas about the prospects of oil and gas content in the Paleozoic basement of the southeastern part of the West Siberian basin, along with an analysis of the identified conditions of hydrocarbon accumulation formation, allowed to propose a conceptual model that can be used as a basis for prediction when searching for accumulations in the pre-Jurassic complex.

An Automated Deep Reinforcement Learning Pipeline for Dynamic Pricing

Dynamic pricing problem is difficult due to the highly dynamic environment and unknown demand distributions. In this paper, we propose a Deep Reinforcement Learning (DRL) framework, which is a pipeline that automatically defines the DRL components for solving a Dynamic Pricing problem. The automated DRL pipeline is necessary because the DRL framework can be designed in numerous ways, and manually finding optimal configurations is tedious. The levels of automation make non-experts capable of using DRL for dynamic pricing. Our DRL pipeline contains three steps of DRL design, including MDP modeling, algorithm selection, and hyper-parameter optimization. It starts with transforming available information to state representation and defining reward function using a reward shaping approach. Then, the hyper-parameters are tuned using a novel hyper-parameters optimization method that integrates Bayesian Optimization and the selection operator of the Genetic algorithm. We employ our DRL pipeline on reserve price optimization problems in online advertising as a case study. We show that using the DRL configuration obtained by our DRL pipeline, a pricing policy is obtained whose revenue is significantly higher than the benchmark methods. The evaluation is performed by developing a simulation for the RTB environment that makes exploration possible for the RL agent.

Whole Stand Variable Density Yield Equations for Oak-Gum-Cypress Bottomland Hardwood Forests

Abstract Variable density yield equations were constructed using fuzzy linear regression for bottomland oak-gum-cypress forests along the US Gulf Coast and lower Mississippi River Delta region. The USDA Forest Service (Forest Service)’s Forest Inventory and Analysis program provided plot data (n = 526). Predictors included stand age, growing stock basal area per acre, sweetgum site index (base age 50 years), and US Environmental Protection Agency ecoregion dummy variables located in Alabama, Arkansas, Louisiana, Mississippi, Tennessee, and Texas. Dependent variables were per acre growing stock cubic foot yield (GSV) and Doyle board foot sawlog yield (SLV). Plots averaged 58 years, 90 ft2/ac basal area, 79 ft site index, 2,556 ft3/ac GSV, and 11,183 Doyle bf/ac SLV. Adjusted R2 were 0.98 (GSV) and 0.77 (SLV). Basal area possessed fuzziness in the GSV model, whereas the SLV model’s intercept was fuzzy. Six ecoregions possessed fuzziness in each model, but these were not identical across models.

Reserve Prices and Bidder Participation in an English Auction: The Case of Atlantic Cod in Norway

This study investigates the impact of reserve prices on bidder participation in the main Norwegian auction market for frozen Atlantic cod. Theoretically, there is a negative relationship between reserve prices and bidder participation. However, our empirical findings show that the reserve price actually has no negative influence on bidder participation. A simulation exercise taking buyers’ true valuations into account also shows a negligible effect of reserve prices on the probability of two or more participating bidders. Other factors are more important in attracting bidders to the auction. Most notably, cod caught by longline, which implies fish of high quality, enhances the probability of two or more bidders by 11.8%. Auctioneers should focus on information transparency regarding fish quality to attract participants and hence improve the efficiency of auction markets.

Spatial agglomeration or dispersion under Cournot‐Bertrand competition

AbstractThis research explores the equilibria of a spatial model with consumers having finite reservation prices and two firms under Cournot‐Bertrand competition. We find three types of equilibria. For high effective reservation prices, a unique equilibrium exists with spatially agglomerating firms serving all consumers. For medium effective reservation prices, the intermediate‐location‐differentiation firms serve all consumers at equilibria. For low effective reservation prices, the firms act as two monopolists and do not serve all consumers at equilibria. The results herein and from previous studies together demonstrate that changing one of two Bertrand (Cournot) firms to Cournot‐type (Bertrand‐type) can make the minimum‐location‐differentiation equilibrium appear (disappear). Moreover, both firms' location distances at our intermediate‐location‐differentiation equilibria are always larger than those at the equilibria of firms' price competition.

Optimal scheduling of distributed hydrogen refueling stations for fuel supply and reserve demand service with evolutionary transfer multi-agent reinforcement learning

With the extensive utilization of fossil fuels, environmental concerns have been growing. Hydrogen-powered vehicles (HPVs) as well as hydrogen refueling stations (HRSs) are expected to proliferate to alleviate the pollution. In order to promote the economic valibity of HRSs, this paper proposes an optimal scheduling model for distributed HRSs to maximize HRSs’ revenue during a day. HRSs serves both the mobility sector with fuel individually and the power system with reserve demand response collaboratively. The elaborated operating of electrolysers and relaxed control of hydrogen storage are involved to enhance practicability and flexibility of the scheduling. Considering that this model has multiple variables, intractable high-dimension nonlinear constraints, a transfer multi-agent reinforcement learning algorithm is developed. The algorithm consists of two parts: the one is the multi-agent optimization method by transforming multi-decision optimization into a multi-agent optimization; the other is to transfer the empirical knowledge of the source task to reinforcement learning according to the similarity between the source task and the target task, making it cut down the blindness of exploration and accelerate convergence. Numerical studies reveal that participating in the reserve services market collaboratively increases overall revenues of HRSs by up to 32.90%, and the computation time is sharply shortened by approximately 34 times compared to the basic reinforcement learning.

Lying about Reservation Prices in Business Negotiation: A Qualified Defense

This essay offers a philosophical defense of deception about reservation prices in business negotiation. Its discussion is prompted by arguments that Nicholas Sherwood makes in a recent issue of Business Ethics Quarterly and develops ideas I put forward in an earlier issue of Business Ethics Quarterly. The essay argues that although reservation price deception cannot be justified by appeal to the consent of negotiating parties, it can be justified by appeal to a separate but related notion, assumption of risk, as long as the assumption of risk occurs in a suitably fair context.

Optimal three-part tariff pricing with Spence-Mirrlees reservation prices

Optimal three-part tariff pricing with Spence-Mirrlees reservation prices

Costly multi-unit search

We formulate and solve a costly multi-unit search problem for the optimal selling of a stock of goods. Our showcase application is an inventory liquidation problem with holding costs, such as warehousing, salaries or floor planning. A seller faces a stream of buyers periodically arriving with random capped demands. At each decision point, he decides how to price each unit and also whether to stop searching or not. We set this as a dynamic programming problem and solve it inductively by characterizing optimal search rules and reservation prices. Our inductive logic does not rely in the use of the first order conditions for optimal pricing and optimal search decisions.We show that combining multiple units with a fixed per period search cost might translate into non-monotone selling costs and reservation prices. This lack of monotonicity naturally leads to discontinuities of the pricing strategy. In particular, the seller optimally employs strategies such as bundling, and more sophisticated ones that endogenously combine purchase premiums, when inventory is large, with clearance sales and discounts, when inventory is low. Instead, a variable search cost always yields an increasing marginal selling cost and thus can only make sense of pricing strategies that employ purchase premiums.Our model extends search theory by explicitly accounting for the effects of search costs on optimal multi-unit pricing strategies, pushing it into a richer class of problems and offering solutions that extend beyond optimal stopping rules.

The interaction of wholesale electricity market structures under futures with decarbonization policy goals: A complexity conundrum

Competitive wholesale electricity markets can help facilitate energy system decarbonization by incentivizing investments in clean energy technologies that meet evolving system needs. We explore market structure impacts on generator operations and deployment by risk-averse, heterogeneous investor firms using the Electricity Markets and Investment Suite – Agent-based Simulation (EMIS-AS) model. We apply clean energy targets of 45%–100% by 2035 considering energy, ancillary services, capacity, and clean energy credit products and pricing and eligibility rules. Results highlight a complexity conundrum, whereby finding the “right” market design to achieve decarbonization goals and avoid unintended consequences can be a highly-nuanced, non-incremental challenge. Carefully designed energy-only markets can achieve the same clean energy targets as capacity market structures but with different revenue and profitability outcomes. Operating reserve demand curve-based scarcity pricing can substitute capacity markets for similar deployment outcomes. Carbon pricing alone is most effective at achieving decarbonization levels at low clean energy targets, and clean energy credit markets and carbon pricing are substitutionary at high clean energy targets. Restricting technology participation in capacity and operating reserve markets can impact deployment and operations, even for nonrestricted technologies. Adding an inertia product with fast frequency response yields insufficient provision at high clean energy targets, but work is needed to understand frequency requirements and capabilities.

Competing auctions with non-identical objects

We study a competition model with two sellers that auction non-identical objects, unlike most of the literature on competing auctions. Each bidder has bidimensional private information, his values for the objects, and chooses the auction in which he participates (if any) after each seller has set a reserve price for her auction. We show that in some cases the duopoly reserve price is greater than the reserve price for a monopolist auctioning a single object; thus, an increase in the number of sellers may make some bidder’s types worse off. In our analysis we first characterize the unique symmetric equilibrium for the game of auction choice played by the bidders and investigate its features. Then for the game of reserve price setting played by the sellers, we show that in each symmetric equilibrium the reserve price level is determined by the interplay of two effects, a virtual value effect and a business stealing effect. The former tends to lift the equilibrium reserve price above the monopoly level, the latter may drive the equilibrium reserve price significantly below the monopoly level when the number n of bidders is small. For a large n we find that different reserve prices in the two auctions have little effect on bidders’ auction choices. As a consequence, when n is large the business stealing effect weakens with respect to the virtual value effect, and for the cases we consider the equilibrium reserve price is above the monopoly level.

Enhanced Oil Recovery

Abstract: Enhanced oil recovery procedures, which are part of improved oil recovery, are used to recover the leftover oil include Steam injection, Polymer flooding, Gas injection and Water injection. Only a small percentage of the total hydrocarbons in the reservoir can be recovered using standard oil recovery technologies. Even if normal recovery procedures are implemented, almost 2 trillion barrels of conventional oil and 5 trillion barrels of heavy oil will remain in reservoirs around the world. Many factors, both economic and technological, influence the strategy chosen and the projected recovery. The study evaluates the EOR approaches that are currently in use in the field. The current EOR technologies are put into context, with the technical reasons for their failure highlighted. Recovering additional oil is difficult and expensive, and it has only been done successfully in a few cases under strict conditions. Despite this, EOR will continue to play a significant role in oil production due to rising energy demand and constrained supply. It is estimated that a significant amount of research is required to develop new technologies for recovering almost two-thirds of the oil that remains unrecoverable in the reservoir. New methods of enhanced oil recovery improves the efficiency of existing processes and reduce demand for new reserves in the longer term. This paper represents the new technologies evolved in the petroleum industry for Enhanced oil recovery.

Application of an Operating Reserve Demand Curve in ERCOT in the South Korean Electricity Market to Accommodate High Penetration Levels of Renewable Energy

Application of an Operating Reserve Demand Curve in ERCOT in the South Korean Electricity Market to Accommodate High Penetration Levels of Renewable Energy

Reserve provision of combined-cycle unit in joint day-ahead energy and reserve markets

With the continuous development of natural gas-fired combined cycle power generation technology, the combined cycle (CC) unit's installed capacity is gradually increasing in the power system because of its flexible operation characteristics. Participating in the energy and reserve markets and taking advantage of flexible mode transfer characteristics, the CC unit can provide the system with energy and sufficient reserve capacity. This paper proposes a bi-level strategic bidding model to study the CC unit’s bidding behavior in the day-ahead energy and reserve markets. The upper-level model is an optimal bidding model for the configuration-based CC unit aiming to maximize its profit. The lower-level model is the joint market clearing model of the energy and reserve markets managed by the independent system operator (ISO). CC unit and ISO in this model influence each other by the locational marginal electricity prices (LMEP) and the locational marginal reserve prices (LMRP) obtained from the lower-level market-clearing model. The CC unit submits the bids to the ISO and arranges the generation plan according to the market clearing results of the lower-level market-clearing model carried out by the ISO. The proposed bi-level optimization model is transformed into a mathematical equilibrium constraint (MPEC) problem by Karush-Kuhn-Tucker (KKT) conditions and duality theory. In this paper, the improved IEEE-6 bus and IEEE-57 bus systems are used as case studies to verify the effectiveness of the proposed bidding strategy for the CC unit. The results show that the proposed bidding strategy can effectively improve the profits of the CC unit in the electricity market. Compared with the case of the CC unit only participating in the energy market, the profits of the CC unit in the market increased by 8.84% and 12.3%.

Frequency reserves and inertia in the transition to future electricity systems

AbstractThe transition towards an electricity system that is dominated by asynchronous and non-dispatchable generators, such as wind and solar power, entails challenges related to balancing the load and, thereby, keeping the grid frequency stable. Many technologies can contribute to load balancing and frequency control. This study investigates the interactions between electricity generation and frequency control in terms of investments and operation, using cost-minimizing, linear optimization modeling. The model is applied in three different geographic cases and for four future time-points, starting off with the already existing transmission and generation capacities, so as to yield insights into different systems and different stages along the energy transition. The results show that frequency control constraints in the optimization model have a weak impact on the system composition and cost, and that batteries are important for minimizing the impact. Furthermore, inertia requirements without a reserve demand show no impact on the cost or system composition. When allowing for vehicle-to-grid from battery electric vehicles, a large proportion of stationary grid battery investments is displaced, and the impact on system cost from adding frequency control constraints is removed.

Deposit Insurance Premiums and Bank Risk

AbstractDeposit insurance premiums impose costs on banks’ balance sheets, narrowing profit margins and inducing banks to “search for yield.” This paper estimates the effects of deposit insurance premiums on bank portfolio rebalancing using supervisory data and a kink in the insurance premium schedule. We show that deposit insurance premiums weaken banks’ demand for reserves (a liquid asset with no credit risk) and strengthen the supply of short-term interbank loans (a less liquid asset with credit risk). We discuss the implications of these findings for optimal deposit insurance pricing. (JEL G21, G28)

Russia’s development strategy in the context of the global geo-economic paradigm shift

Aim.The presented study aims to substantiate the paradigm shift in global economic development and to identify the impact of this process on the Russian economy.Tasks.The author investigates scientific papers on the research topic; analyzes the mechanism of interaction between nominal and resource systems and substantiates the use of the dollar and the euro as world currencies as a prerequisite for determining the nominal demand system in the resource market; examines the trend of the dollar and euro losing their dominant position in the global financial system and its stability; advances arguments for a paradigm shift in global economic development and determines the impact of this process on the Russian economy in the medium and long term.Methods.This study uses the methods of analysis, synthesis, generalization, and a systems approach.Results.World economy is presented as a mechanism based on the interaction between the real and the resource systems. The increasing gap between these systems at the present time is substantiated. Arguments in favor of the existence of a stable process of de-dollarization as a prerequisite for such a gap are provided. Based on the conducted analysis, a steady trend of the dollar losing major functions of world money in certain countries and country associations that are strategically important for Russia has been revealed. The existence of a paradigm shift in global world development, which consists in replacing the demand for reserves with the demand for resources, is substantiated. It is shown that for Russia, the existing changes make it possible to effectively distribute raw energy resources from the perspective of the country’s geo-economic and geopolitical interests. It is substantiated that the export of energy commodities from Russia will be irrelevant in the long term. In conclusion, the author suggests appropriate directions for a long-term development strategy.Conclusions.Currently, there is a global economic paradigm shift, which involves demand for raw materials being replaced by demand for foreign exchange reserves. Considering Russia’s role in the global mineral commodity market, the existing changes make it possible to effectively distribute raw energy resources from the perspective of the country’s geo-economic and geopolitical interests. In the medium term, the demand for Russian hydrocarbon resources will be ensured by the growing economic potential of major importers. In the long term, the leading importers of hydrocarbons will develop renewable and hydrogen energy, which will lead to the rejection of energy imports. Thus, Russia’s long-term geo-economic strategy should focus on the development and export of scientific, technological, and innovative products.