Reserve Price Research Articles

Estimating Stand-Level Carbon Supply Curves for Loblolly Pine and Douglas-Fir Plantations

AbstractWe estimate the carbon sequestration supply curve at the stand level based on the optimal rotation decision and conduct a marginal analysis for payments necessary for postponing harvest for additional 1-year increments of two commercially important species in the United States, loblolly pine and Douglas-fir. Under certain costs, production and timber prices assumptions, payments ranged from $62.23 ($26.97–$105.87) ac/yr. for loblolly pine plantations and $399.17 ($189.80–$628.72) ac/yr. for Douglas-fir plantations. Our results indicate that higher carbon sequestration occurs with higher site index, higher trees per acre, and in unthinned stands. Significant variability within and between species was heavily dependent on the number of years that final harvest was postponed. In addition, we show the effect of prices on the quantity supplied under multiple silvicultural treatments. The study should assist willing forest landowners and potential partners to determine initial reservation prices for carbon sequestration and temporary provision for a 1-year period in line with programs offering this contracting mechanism.

Tourist choice, competitive tourism markets and the effect of a tourist tax on producers revenues

We propose a model for the tourism sector assuming basically two markets, one for tourist services and the other for accommodation. These sub-markets are considered as separate but interrelated. The nature of the feedback is determined by a vertical complementarity between tourist services and lodging. We obtain the optimal solution of the tourist choice problem, the primary demand for tourist services and the derived demand for overnight stays. Then, we focus on the equilibrium outcomes assuming perfectly competitive tourism markets. We do not address the externalities caused by tourism activities. Consequently, we move away from efficiency by introducing a tax on overnight stays and inspecting the consequences for the competitiveness and for producers’ revenues in each market. The answer key elements are, apart from reservation prices, the direct and cross-price elasticities of demand for tourist services. The study of structural parameters extends and completes our analysis of tourism.

Conflicts of interest and agent heterogeneity in buyer brokerage

AbstractThis article investigates the incentives of agents working with buyers (buying agents) under the fixed percentage commission system and the implications on housing market outcomes. Our model shows that the absence of a binding contract creates a risk of losing clients for buying agents, which helps mitigate the conflict of interest between buying agents and their clients. Both the buying agent's prediction accuracy regarding their client's reservation prices and the level of tolerance given by the buyer to the buying agent affect the binding force. Results from simulations and empirical analyses using house transactions in Canada support our model predictions.

Game Theoretic Approach for Multipriority Data Transmission in 5G Vehicular Networks

The vehicle-to-vehicle (V2V) communication driven by the fifth generation (5G) cellular mobile network with the features of ultra-high reliability and low latency provides promising solutions to various applications in the intelligent transportation system (ITS). To improve the resource utilization and guarantee the quality-of-service (QoS), users in 5G vehicular networks have to select appropriate communication modes and control their own transmission power. However, the highly dynamic network topology and channel status pose challenges to the mode selection. In this paper, we propose a scheme for joint mode selection and power adaptation based on the game theoretic approach with the objective of maximizing the overall system throughput. We consider the transmission requirements of multi-priority packets of different vehicular applications, where packets with higher priority have more stringent latency constraints. The segmented auction method with reserve price is performed to select modes for the vehicular users (VUEs) and the Stackelberg gaming model is introduced to solve the problem of cochannel interference. We compare our approach with three existing methods in extensive simulations. The results show that our approach outperforms the existing methods in terms of network performance, including the network throughput, resource utilization and QoS violation rate.

First-degree price discrimination water bank to reduce reacquisition costs and enhance economic efficiency in agricultural water buyback

In water buyback programs a public institution (the water bank) purchases predetermined amount of water from willing sellers, part of which can be reallocated to users in a subsequent lease phase. This makes possible to buy water at low monopsonistic prices and sell a fraction of this water at high monopolistic prices, where the water reacquired in excess of sales is used to restore natural assets. We propose a price discrimination water bank where the public institution leverages its monopsonistic (monopolistic) position to pay (ask) a price for every unit of water sold (bought) that matches the reserve price of every willing buyer (seller) in the market. Thus, both the consumer and producer surpluses are wholly transformed into public revenues, which reduces the budgetary burden of the environmental restoration without negatively impacting economic efficiency. We illustrate the performance of the price discrimination water bank under uncertainty through an hydroeconomic multi-model ensemble that is applied to the Upper Douro sub-basin (Spain). Our results show that the price discrimination water bank can achieve the same water reacquisition target as a conventional water bank (no price discrimination, no lease phase) at a significantly lower cost (59.5%–288.8% reduction) while achieving a significantly higher productive surplus (331%–570% increase).

Characterization of Vickrey auction with reserve price for multiple objects

Characterization of Vickrey auction with reserve price for multiple objects

Implementing optimal outcomes through sequential auctions

AbstractWe study sequential auctions as optimal mechanisms for selling multiple heterogeneous items to unit‐demand buyers. We find that as long as the items can be ordered in decreasing variation of the buyers' values, any combination of static standard auctions in this sequence achieves full efficiency and the constrained optimal revenue, subject to (BIC), (IIR), and the all‐sold constraint. In addition, with increasing threshold types and optimal static reserve prices, any l‐th price auctions in the same sequence can achieve the optimal revenue subject to (BIC) and (IIR). Our results have implications for practical auction design, such as sponsored search auctions.

Stochastic optimization of trading strategies in sequential electricity markets

Quantity and price risks determine key uncertainties market participants face in electricity markets with increased volatility, for instance due to high shares of renewables. In the time from day-ahead until real-time, there lies a large variation in best available information, such as between forecasts and realizations of uncertain parameters like renewable feed-in and electricity prices. This uncertainty reflects on both the market outcomes and the quantity of renewable generation, making the determination of sound trading strategies across different market segments a complex task. The scope of the paper is to optimize day-ahead and intraday trading decisions jointly for a portfolio with controllable and intermittent renewable generation under consideration of risk. We include a reserve market, a day-ahead market and an intraday market in stochastic modeling and develop a multi-stage stochastic Mixed Integer Linear Program. We assess the profitability as well as the risk exposure, quantified by the conditional value at risk metric, of trading strategies following different risk preferences. We conclude that a risk-neutral trader mainly relies on the opportunity of higher expected profits in intraday trading, whereas risk can be hedged effectively by trading on the day-ahead. Finally, we show that reserve market participation implies various rationales, including the relation of expected reserve prices among each other, the relation of expected reserve prices to spot market prices, as well as the relation of the spot market prices among each other.

Reservation Wage for High Skilled Graduates in Bangladesh: Preliminary Factors

This paper preliminarily discusses about the determinants of reservation wage for graduates in Bangladesh. The researchers discussed several models in order to identify the common factors which determine the reservation wage of the tertiary graduates in Bangladesh. All these models are multiple linear regression models. Data was collected on both employed and unemployed individuals across the country who graduated from different private and public universities. Initially the researchers investigated the following factors: socio-economic status (Parental income), selection of occupation, duration of unemployment, difference between actual and reservation wage, managerial level of job, sources of different media of job applications (both formal and informal) and in the revised phase of the model the researchers discovered that that inclusion of ‘previous working experience’ made a significant difference in the relationship among dependent and independent variables. One noticeable difference in the findings of the current researchers compared to those who worked on the same topic earlier was: positive relationship between duration of unemployment and reservation price of the graduates. The researchers further explored reasons of this above result theoretically. Finally, the researchers accepted the following variables (rejected as null hypothesis) as significant factors (Socio-economic background, difference between actual salary and reservation price, managerial level of job, media of job applications in terms of formal or informal, previous working experience) to influence the graduate reservation wage in Bangladesh (in the alternatives). Additionally, the researchers also explained why’ duration of unemployment’ was not selected as a crucial determinant in the current paper. Finally the researchers prescribed some positive solutions on how firms in Bangladesh could deal with the increasing demand of wage premiums from high skilled graduates and what the possible consequences are if they failed to do so.      

Demand Segmentation in the Federal Funds Market

This paper outlines a model of demand segmentation in the federal funds market with two types of borrowers - the "interest on reserves (IOR) arbitrage'' type and the "regulatory'' type - which have different reservation prices and cannot always be separated. When fed funds trade above IOR, the "regulatory" type is revealed and consequently pays an interest rate closer to its real reservation price, pushing the fed funds rate further up. When fed funds trade below IOR, a decrease in the fed funds rate encourages entry in the market for IOR arbitrage purposes thus counteracting the downward pressure on the fed funds rate. We use probit regression models and daily data for the period April 2018 to February 2020 to provide empirical support for this model. We find the following: 1) When fed funds trade above IOR, there is, on average, a 10 percentage points increase in the probability that the fed funds rate increases the following period. Furthermore, analysis using confidential bank-level data shows that this increase in the probability is higher for banks that report their liquidity profile daily and that were present all trading days during this period. 2) When the fed funds trade below IOR, the probability of a decrease in the fed funds rate decreases with the widening of the spread between the fed funds rate and IOR.

Semi-nonparametric estimation of secret reserve prices in auctions

This letter proposes an estimation method for the distribution of unobservable secret seller reserve prices in auctions. Within the symmetric affiliated private values model, I construct a density estimator using the semi-nonparametric quasi maximum likelihood approach developed by Gallant and Nychka (1987). I then use Monte Carlo experiments to demonstrate its performance in relatively small samples. A discussion on applying this method to structurally similar identification problems is provided. • This letter offers an approach to identify the distribution of secret reserve prices. • The model allows for affiliated private values among buyers and sellers. • Input data include the auction’s paying bid and whether the item sold at said bid. • A semi-nonparametric estimator identifies the distribution of secret reserve prices. • This method works well in relatively small samples ( ∼ 100 ).

Deposit Requirements in Auctions

We examine optimal auction design when buyers may receive future outside offers. The winning bidder may choose to default upon observing her outside offer. Under the optimal mechanism, the bidder with the highest value wins if and only if her value is above a cutoff, and the winner never defaults. The optimal auction takes the form of a second-price auction with a reserve price and a deposit by the winning bidder. Under regularity conditions, both the optimal reserve price and the deposit increase when the distribution of outside offers worsens. (JEL D44, D82)

The Semi-Scheduling Mode of Multi-Energy System Considering Risk–Utility in Day-Ahead Market

The large-scale development of renewable energy has an urgent demand for an adjustable power supply. For a multi-energy system with multiple types of heterogeneous power sources, including wind power, photovoltaic (PV) power, hydropower, thermal power and pumped storage, a novel semi-scheduling mode and a solution method were proposed in this paper. Firstly, based on the load and the reserve demand during the peak load period, the semi-scheduling mode was adopted to determine the start-up combination of thermal power units. Furthermore, by predicting the generating/pumping power, the working state of pumped storage units was determined to realize the independent solution of discrete integer variables. Secondly, the risk–utility function was constructed to quantify the attitude of pumped storage towards the uncertainty of renewable energy output, which completed the quotation and clearing of the pumped storage in the ancillary service market. Finally, by taking the minimum total quotation cost as the objective, the wind–solar–hydro- thermal-pumped storage coordinated (WSHTPC) model was built in the day-ahead market. The feasibility and effectiveness of the proposed model were verified through the simulation of a typical day with different renewable energy penetration rates.

A Model of QE, Reserve Demand, and the Money Multiplier

AbstractQuantitative easing (QE) programs have driven unprecedented expansions in the supply of central bank reserves around the world over the past two decades, fundamentally changing the implementation of monetary policy. The collapse in money multipliers following QE episodes has often been interpreted as implying banks are happy to passively hold most of the reserves created by QE. This paper develops a simple microsimulation model of the banking sector that adapts the traditional money multiplier model and allows for bank reserve demand to be inferred from monetary aggregates. The model allows the use of unwanted reserves by banks to play out over time alongside QE purchases and incorporates both significantly higher reserve demand after 2008 and capital constraints. With these additions, the model explains the persistently lower money multipliers seen in the United States following QE, as well as the growth in commercial bank deposits. The model suggests the demand from banks for reserves has increased substantially since the introduction of QE but not to the point where banks are passively absorbing all newly created reserves.

Online Learning via Offline Greedy Algorithms: Applications in Market Design and Optimization

Motivated by online decision making in time-varying combinatorial environments, we study the problem of transforming offline algorithms to their online counterparts. We focus on offline combinatorial problems that are amenable to a constant factor approximation using a greedy algorithm that is robust to local errors. For such problems, we provide a general framework that efficiently transforms offline robust greedy algorithms to online ones using Blackwell approachability. We show that the resulting online algorithms have [Formula: see text] (approximate) regret under the full information setting. We further introduce a bandit extension of Blackwell approachability that we call Bandit Blackwell approachability. We leverage this notion to transform greedy robust offline algorithms into a [Formula: see text] (approximate) regret in the bandit setting. Demonstrating the flexibility of our framework, we apply our offline-to-online transformation to several problems at the intersection of revenue management, market design, and online optimization, including product ranking optimization in online platforms, reserve price optimization in auctions, and submodular maximization. We also extend our reduction to greedy-like first-order methods used in continuous optimization, such as those used for maximizing continuous strong DR monotone submodular functions subject to convex constraints. We show that our transformation, when applied to these applications, leads to new regret bounds or improves the current known bounds. We complement our theoretical studies by conducting numerical simulations for two of our applications, in both of which we observe that the numerical performance of our transformations outperforms the theoretical guarantees in practical instances. This paper was accepted by George Shanthikumar, data science. Funding: R. Niazadeh was supported by research funding from University of Chicago Booth School of Business. N. Golrezaei was supported in part by the Young Investigator Program Award from the Office of Naval Research [N00014-21-1-2776] and the MIT Research Support Award. Supplemental Material: The Online Appendix is available at https://doi.org/10.1287/mnsc.2022.4558

Optimizing high-dimensional stochastic forestry via reinforcement learning

In proceeding beyond the generic optimal rotation model, forest economic research has applied various specifications that aim to circumvent the problems of high dimensionality. We specify an age- and size-structured mixed-species optimal harvesting model with binary variables for harvest timing, stochastic stand growth, and stochastic prices. Reinforcement learning allows solving this high-dimensional model without simplifications. In addition to presenting new features in reservation price schedules and effects of stochasticity, our setup allows evaluating the simplifications in the existing research. We find that one- or two-dimensional models lose a high fraction of attainable economic output while the commonly applied size-structured matrix model overestimates economic profitability, yields deviations in harvest timing, including optimal rotation, and dilutes the effects of stochasticity. Reinforcement learning is found to be an efficient and promising method for detailed age- and size-structured optimization models in resource economics.

Bidding for an optimal portfolio of keywords in sponsored search advertising: From generic to branded keywords

With the increasing prominence of digital media, retailers attempt to attract consumers to their websites by investing in sponsored search advertising. However, due to stiff competition among retailers, sponsored search advertising can be expensive. This paper develops a multi-period, dynamic programming model that provides a retailer with an optimal portfolio of generic and branded bids. We model two critical aspects of consumer search behavior: (i) the spillover effect of generic searches leading to branded search arrivals in subsequent periods and (ii) the memory effect that leads to a decline of consumer awareness of a brand over time. We find that the retailer can effectively shuffle his investments on generic and branded keywords depending on several consumer parameters, e.g., awareness level, brand retention and reservation price variances. We develop a bidding policy framework to highlight the shift in bid shares from generic to branded at different levels of consumer awareness. We find that harnessing the benefits of spillover from generic to branded keywords allows the retailer to save on generic bids at higher awareness and retention levels and lower variance in consumers’ reservation prices. Further, we extend our model to different consumer purchase situations/ product classifications, viz., Convenience, Shopping and Specialty purchasing. Our analysis suggests prevalence of generic bids for certain purchase/product situations, whereas branded bids remain salient in other situations.

Demand Response Transit Scheduling Research Based on Urban and Rural Transportation Station Optimization

To reduce the operating cost and running time of demand responsive transit between urban and rural areas, a DBSCAN K-means (DK-means) clustering algorithm, which is based on the density-based spatial clustering of applications with noise (DBSCAN) and K-means clustering algorithm, was proposed to cluster pre-processing and station optimization for passenger reservation demand and to design a new variable-route demand responsive transit service system that can promote urban–rural integration. Firstly, after preprocessing the reservation demand through DBSCAN clustering algorithm, K-means clustering algorithm was used to divide fixed sites and alternative sites. Then, a bus scheduling model was established, and a genetic simulated annealing algorithm was proposed to solve the model. Finally, the feasibility of the model was validated in the northern area of Yongcheng City, Henan Province, China. The results show that the optimized bus scheduling reduced the operating cost and running time by 9.5% and 9.0%, respectively, compared with those of the regional flexible bus, and 4.5% and 5.1%, respectively, compared with those of the variable-route demand response transit after K-means clustering for passenger preprocessing.

Balancing liquidity and returns through interbank markets: Endogenous interest rates and network structures

In this article, we develop a model of interbank lending based on liquidity and return on equity considerations of homogeneous banks. We derive the reservation prices of interbank lending and its properties before exploring how, because of an idiosyncratic liquidity shock, banks engage in bilateral lending to form an interbank network. We establish that the resulting networks exhibit realistic properties, including a core-periphery structure. Banks in the core and the periphery of this network differ not only in the amounts of interbank lending and borrowing but also in the interest rates applied to their transactions.

Effect of Various Green Carbon Tracking Methods on Life Cycle Assessment Results for Fluid Catalytic Cracker Co-processing of Fast Pyrolysis Bio-oil

Co-processing fast pyrolysis bio-oil (FPBO) in conventional oil refineries and, specifically, in fluid catalytic cracker (FCC) units is one of the most cost-effective and least capex-intensive routes to produce advanced biofuels. It provides a direct way to replace fossil feedstocks, meaning that less crude oil is needed to meet the demand for transportation fuels and more oil reserves can stay untouched. Tracking biogenic carbon through a refinery is important for the legislative aspects of co-processing and, therefore, also for the process economics. Two types of methods exist for tracing biogenic carbon: analytical and bookkeeping methods. Choosing a certain method may influence the outcome of life cycle assessments (LCAs) that calculate the environmental impact of the advanced biofuels produced in this way, because different methods may provide a different result for the calculated volumes of bio-based products that come from co-processing. The goal of this article was to assess the various methods that exist in the literature to determine the biogenic content of products that are derived from the FCC co-processing of FPBO. Six methods were described here and used to calculate the yield of biogenic products from the co-processing of 5 wt % FPBO. These outcomes were used as input for a comparative greenhouse gas (GHG) LCA case study, which analyzed a hypothetical value chain based on a fast pyrolysis plant in Finland that produces FPBO and a petroleum refinery in western Europe where the FPBO is co-processed in the FCC. The biogasoline yields that were obtained from co-processing FPBO ranged between 27 and 94% on an energy basis, depending upon which method was selected. However, the range of differences between the resulting GHG emissions for the entire supply chain from forestry to biogasoline production was a lot narrower than the range in biogasoline yield differences, namely, between 87 and 94% GHG savings. Therefore, it can be concluded that, despite the differences between the calculation methods, all of them lead to a qualification of the biogasoline as an advanced biofuel with a high potential for GHG emission reduction.