Function Of Price Research Articles

Low-carbon economic optimization strategy for industrial loads in parks considering source-load-price multivariate uncertainty

A low-carbon economic optimization method is proposed for industrial loads in parks considering multivariate uncertainty. Model of dynamic load node carbon intensity is proposed considering the uncertainty of clean energy and dynamic conventional units carbon intensity based on system currents. The robustness is improved based on multi-interval uncertainty set. The gap is filled by hybrid copula model with Generalized Autoregressive Conditional Heteroskedasticity (GARCH) prices in the uncertainty model. The GARCH is used to built the marginal distribution function of prices, and a hybrid copula model is proposed to obtain the joint probability density function based on the weights calculated by Euclidean distance. The LNCI probability distributions of the regulation potential of loads are proposed with the virtual energy storage and the virtual power model to improve the descriptive ability of uncertainty based on the expansion and contraction functions. Finally, the typical scenarios are extracted based on Monte Carlo and Wasserstein measures. The Column sum Constraint Generation algorithm and strong duality theory are used to get the robust-stochastic optimization. The method proposed in the paper has a positive effect on enterprises to rationalize their production schedules, reduce electricity and carbon emissions costs and increase the revenue from participating in grid regulation.

How much are we willing to pay for quality wine? A meta‐analysis and meta‐regression analysis

AbstractThis paper performs a meta‐analysis and a meta‐regression analysis on the price semi‐elasticity of wine quality ratings in the context of estimating hedonic price functions. To do so, we thoroughly search for publications that have estimated hedonic functions in this market, resulting in 223 articles estimating 1595 functions, although not all of them incorporate quality ratings. Additionally, 60 aspects of the different studies are coded, including sample characteristics, incorporated attributes, and other implemented methodological choices. The meta‐analysis is then performed, revealing evidence of selection and publication biases. Taking them into account, the price elasticity is found to be around 5%–6%, remaining robust to several robustness checks. The meta‐regression explores the unexplained heterogeneity in the price elasticities observed in the meta‐analysis. Results indicate that quality ratings are more influential in the context of red wines, and less important in the case of sparkling and organic wines. The nature of the data source matters, but generally not the type of price used except the ones obtained from auction and en primeur sales. The influence of the remaining attributes incorporated in the hedonic function is relatively small, except for other quality aspects, reputation, and environment. The econometric techniques used in the estimation do not affect the effect size. This is also the result found for the type of publication, except for studies published in books. Finally, we find that the most cited studies are those that show a smaller effect.

Insider trading in discrete time Kyle games

AbstractWe present a new discrete time version of Kyle’s (Econometrica 53(6):1315–1335, 1985) classic model of insider trading, formulated as a generalised extensive form game. The model has three kinds of traders: an insider, random noise traders, and a market maker. The insider aims to exploit her informational advantage and maximise expected profits while the market maker observes the total order flow and sets prices accordingly. First, we show how the multi-period model with finitely many pure strategies can be reduced to a (static) social system in the sense of Debreu (Proc Natl Acad Sci 38(10):886–893, 1952) and prove the existence of a sequential Kyle equilibrium, following Kreps and Wilson (Econometrica 50(4):863–894, 1982). This works for any probability distribution with finite support of the noise trader’s demand and the true value, and for any finite information flow of the insider. In contrast to Kyle (1985) with normal distributions, equilibria exist in general only in mixed strategies and not in pure strategies. In the single-period model we establish bounds for the insider’s strategy in equilibrium. Finally, we prove the existence of an equilibrium for the game with a continuum of actions, by considering an approximating sequence of games with finitely many actions. Because of the lack of compactness of the set of measurable price functions, standard infinite-dimensional fixed point theorems are not applicable.

What is the impact of demand patterns on integrated online-offline and buy-online-pickup in-store (BOPS) retail in a smart supply chain management?

Retailers aim to provide in-person and remote interactions to accelerate purchases through hybrid methods, which introduce additional delivery and emissions loads but create a seamless shopping experience. The challenges of unified inventory, customer information, pricing, demand forecasting complications, and order fulfillment are met successfully in the hybrid methods. Here, a smart supply chain management that integrates traditional physical presence with cloud-based and hybrid buy-online-pickup in-store channels is optimized to meet customer demand in different demand patterns. The demand patterns considered are linear, cubic, and normalized functions of the selling price, the advertising cost, and the product lifetime. The article incorporates sustainable supply chain and omnichannel strategies to optimize supply chain performance and profitability. The paper compares the results of the proposed models with traditional supply chain models and provides some managerial insights and suggestions for online retailers. Numerical simulation reveals that nonlinear price dependence in demand provides an opportunity for higher promotional investment with a substantial rise in optimal selling price and overall profit. The shift-to-imperfect probability is seen to be the volatile component in the solution surface. The markup of the offline channel has a greater impact than buy-online-pickup in-store markup.

A fully decentralized demand response and prosumer peer-to-peer trading for secure and efficient energy management of community microgrid

A novel demand response program combines the advantages of price-based demand response under the time-of-use pricing and the incentive-driven demand reduction bidding strategy to manage energy demand, and maximize the net present value of both participants and the community-microgrid. It also facilitates the seller prosumers to optimally sell the surplus power in a fully decentralized peer-to-peer power trading manner. A novel mathematical demand reduction bidding model is proposed to minimize the community-microgrid cost by the prioritized selection of participants for demand reduction. This study proposed a novel decentralized three non-cooperative dynamic games strategy, incorporating Nash equilibrium approaches to achieve the optimum schedules of appliance, buying, and power trading agents to maximize the net present value of both prosumers and the community-microgrid while preserving participants’ privacy and control network congestion. The priority-based optimal selection of seller and buyer prosumers according to their utility and pricing function in the peer-to-peer power trading scheme is accomplished using a proposed color-based segmented decentralized bilateral power trading to maximize the net present value. The numerical and simulation results demonstrate the feasibility and effectiveness of the proposed strategies and increase the net present value of prosumers up to 42.4 % and community-microgrid up to 34.79 %.

An online auction-based mechanism for pricing and allocation of instant delivery services

Recently, instant delivery has been growing rapidly, with numerous platforms emerging to offer such services. Requestors dynamically arrive at the platform to place delivery service requests that detail their pickup locations, recipient locations, package weights, departure times, and willingness-to-pay (WTP). The platform then uses its dedicated riders, scattered in different places, to fulfill these requests. Given the dynamic and fluctuating characteristics of the demand, coupled with limited rider resources and heterogenous pickup costs, the platform faces the critical problem of dynamically pricing the requests and assigning the riders to maximize social welfare. To address this problem, we propose an online auction-based transaction mechanism. Specifically, we first propose a scoring function to evaluate the values of the requests over multi-period operations taking into account the requests’ attributes, riders’ delivery costs, and resource availability. Based on the scoring function, we design a time-varying Vickrey–Clarke–Groves (VCG)-like payment rule that can reflect the impacts of fluctuating supply-demand imbalances. Under this rule, a requestor will pay more during undersupply periods than during oversupply periods. To carve out the different impact degrees of the supply-demand imbalances, we further consider the linear, quadratic, and exponential time-varying resource parameters to construct the payment rule. In addition, we develop an online instant delivery resource allocation model to efficiently assign the riders to fulfill the accepted requests. We show that the proposed mechanism has desirable properties (individual rationality, budget balance, and incentive compatibility) and is computationally efficient. Furthermore, we give a lower bound for the mechanism efficiency. To validate the practicality of our mechanism and get some managerial insights into the operations of the instant delivery platform, we conduct numerical studies to compare the performance of our mechanism to the First-in, first-out (FIFO) allocation mechanism and to investigate the impacts of pricing functions, rolling horizon configurations, and rider numbers on the mechanism's performance.

Nonsubstitutable Consumption Growth Risk

Standard applications of the consumption-based asset pricing model assume that goods and services within the nondurable consumption bundle are substitutes. We estimate substitution elasticities between different consumption bundles and show that households cannot substitute energy consumption by consumption of other nondurables. As a consequence, energy consumption affects the pricing function as a separate factor. Variation in energy consumption betas explains a large part of the premia related to value, investment, and operating profitability. For example, value stocks are typically more energy intensive than growth stocks and thus riskier, because they suffer more from the oil supply shocks that also affect households. This paper was accepted by Lukas Schmid, finance. Funding: C. Schlag gratefully acknowledges research and financial support from the Leibniz Institute for Financial Research SAFE. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.01269 .

Feasibility Analysis on Tempe Industrial Home (Case in Tempe Yayan Industrial Home, Lebaksiuh Village, Ciawi District, Kuningan Regency)

This study aims to evaluate the financial viability of tempeh home industry businesses by analyzing how production costs affect profitability. The design used in this study is a quantitative descriptive method with a technical survey approach. The data collection techniques include observation, interviews, and questionnaires (questionnaires). The results of this study are as follows: (1) Total production BEP and BEP business price of Tempe Yayan's home industry business in Lebaksiuh Village, Ciawi District, Kuningan Regency, were analyzed. (2) The R/C ratio analysis was used to analyze the profit and loss of the home industry. (3) The total profit of this home industry was calculated as a function of production costs, BEP price, and total profit. (4) Based on the results of the research and discussion conducted regarding the Profit and Loss Analysis of the Tempe YaYan Home Industry Business, future research could focus on a comparative analysis of financial performance and profitability across different tempay home industry ventures in various regions. The research would provide a broader understanding of best practices and challenges faced by similar businesses, offering valuable insights for both entrepreneurs and policymakers. (5) The research could explore the impact of government support and interventions on the growth and success of home industry enterprises, particularly in terms of capital access, production efficiency, and marketing effectiveness.

Application of water cycle algorithm with demand follows green level and nonlinear power pattern of the product for an inventory system

It is commonly known that a number of variables, including price, supply levels, time, and green level, affect how quickly certain things are in demand. Furthermore, the inventory carrying cost is considered to be a nonlinear representation of time and is subject to variation throughout time. More precisely, it rises with time since longer storage times necessitate more costly warehouse space. This study presents a fully backlogged situation inventory system for a single commodity where the product’s selling price, green level, and time are used to simultaneously compute the demand rate in accordance with a power pattern. Purchase price is determined by the product’s nonlinear green level. Complete backorders are available for shortages. The impact of the product’s selling price, green level and time power function are combined to determine the product’s demand. Moreover, the holding cost also rises as the product is stored for a longer period of time. The primary goal is to determine the best inventory policy to maximise total profit per unit of time. Though the problem is highly nonlinear in nature. Hence, we cannot solve it analytically. To overcome these difficulties, we have applied several well-known popular metaheuristic algorithms (Water Cycle Algorithm (WCA), Artificial Electric Field Algorithm (AEFA), Teaching Learning Based Optimization Algorithm (TLBOA), Grey Wolf Optimizer Algorithm (GWOA), Sparrow Search Algorithm (SSA), Whale Optimizer Algorithm (WOA), Prairie Dog Optimization Algorithm (PDOA), Gazelle Optimization Algorithm (GOA), A Sinh Cosh Optimizer Algorithm (SCHOA) and White Sherk Optimizer Algorithm (WSOA), Archimedes Optimization Paradigm Algorithm (AOPA), Marine Predator Optimization Algorithm (MPOA), Geyser Inspired Algorithm (GIA), Runge Kutta Optimization Algorithm (RKOA), Lungs Performance-based Optimization Algorithm (LPOA) and Dwarf Mongoose Optimization Algorithm (DMOA)). It is observed that WCA perform better than other algorithms with respect to the convergence rate. A numerical example is taken in order to validate the proposed model. Finally, a post optimality analysis is performed in order to make a fruitful conclusion.

US Dollar Exchange Rate Elasticity of Gold Returns at Different Federal Fund Rate Zones

We examine the relationship between gold prices and the U.S. dollar exchange rate, arguing that their interactions are state-dependent and asymmetric under different market conditions. State dependency hinges on different short-term interest rate zones. To prove this point, we determine three distinct levels or zones of the effective federal funds rate using SETAR(2,p) tests. Subsequently, we perform conditional least square estimations of log changes in gold prices as a function of log changes in the nominal broad U.S. dollar exchange rate index for each of the obtained zones. Their relationship is consistently inverse, suggesting that gold and the U.S. dollar are risk-hedging substitutes for normal market periods. This also implies that gold is a safe-haven asset against the U.S. dollar exchange rate risk against a broad range of currencies. The substitution is weaker in the low-interest rate zone, more robust in the intermediate zone, and very pronounced in the high zone. We also perform a Markov switching test on the double-log function of gold prices and the exchange rate. The tests show a pronounced inverse relationship, i.e., substitution between assets, at normal market conditions. The relationship becomes significantly positive during episodes of financial distress, indicating complementarity between gold and U.S. dollar assets.

Part-load analysis and preliminary annual simulation of a constant inventory supercritical CO2 power plant for waste heat recovery in cement industry

The present work investigates the part-load performance of a MW-scale sCO2 power plant designed as waste heat recovery unit for an existing cement plant located in Czech Republic, in the framework of the H2020 funded project CO2OLHEAT. The study first presents the selected power plant configuration and then focuses on the evaluation of its part-load operation due to variation of flue gas mass flow rate and temperature. The range of flue gas conditions at the outlet of the upstream process is retrieved from a preliminary statistical analysis of historical trends obtained through the cement plant monitoring. The numerical model developed for this study aims at providing realistic results thanks to the adoption of turbomachinery performance maps provided by the turbomachinery manufacturer of the project. Moreover, heat exchangers have been modelled through a discretized approach which has been validated against manufacturer data, while piping inventory and pressure losses have been assessed through a preliminary sizing that considers the actual distances to be covered in the cement plant. Performance decay is estimated for the whole range of flue gas conditions, reporting the most significant power cycle parameters, and identifying the main causes of efficiency loss. The part-load analysis is carried out considering a constant CO2 inventory, in order to reduce the system complexity and capital cost and simplify plant operation. Results show that the operation entails minor variation of the compressors operative points in the whole range of operating conditions of the cement plant, avoiding the risk of anti-surge bypass activation. Moreover, the plant is able to work close to the nominal thermodynamic cycle efficiency (20.5 %–23.0 %) for most of the year and benefits from part-load operation in terms of overall performance. In the last part of the work, a preliminary techno-economic analysis of the plant is also presented to highlight the potential advantages of sCO2 technology for waste heat recovery applications. The results of the part-load performance of the plant are combined with the flue gases data obtained from the preliminary statistical analysis and the cement plant historical monitoring. An annual electricity production equal to 13′909.7 MWh is obtained, corresponding to 6560 equivalent hours and a system capacity factor of 74.9 %. The investment cost of each CO2OLHEAT plant component is estimated by means of cost correlations obtained from literature and the non-discounted payback time is computed as a function of the electricity selling price. The results show that, even considering electricity prices before 2022, the payback time of the CO2OLHEAT plant is estimated to be lower than 8 years, justifying the industrial interest in the proposed technology.

A novel suggestion on how to adequately treat stochastic non-stationary seasonality in tourism export forecasting

Using a quarterly data sample of 28 EU countries ranging from 2004Q1 to 2016Q4, real tourism exports are modeled and estimated as a function of relative tourism export prices and aggregate real GDP. For almost all countries, HEGY tests applied to first-differenced variables cannot reject the null hypothesis of the presence of seasonal unit roots. Seasonal differencing would be necessary to remove the seasonal patterns from the data, yet employing seasonally differenced data results in information loss, distorted elasticity estimates, and mediocre forecast accuracy. Our suggestion is therefore to perform estimation and forecasting for each quarter separately. This approach avoids the use of seasonal differencing, results in plausible, seasonally varying estimates of income elasticities, and yields statistically significantly superior forecast accuracy, also in comparison to two data-driven benchmarks for seasonal data (i.e., SARIMA and ETS).

A Resource-Based Dynamic Pricing and Forced Forwarding Incentive Algorithm in Socially Aware Networking

In socially aware networking, nodes typically behave selfishly due to resource constraints and social correlations, resulting in low network performance. To incentivize selfish nodes to actively participate in message forwarding, this paper proposes a resource-based dynamic pricing and forced forwarding incentive algorithm (DFIA). Firstly, the algorithm introduces virtual currency as a transaction medium and then designs a pricing function based on factors such as the node’s resource status, participation contribution, location relevance, and social connectivity. It ensures that the forwarding service is transacted at a reasonable price through bargaining rules. Secondly, a forced forwarding strategy is implemented to compel selfish nodes, which are unwilling to participate in other nodes’ message forwarding, to forward a certain number of non-local messages. Meanwhile, in order to prevent nodes from discarding messages and to ensure successful forwarding to the destination, specific rules are used to allocate contribution values to nodes that successfully participate in message forwarding. Lastly, to avoid false quotation behavior, blockchain technology is employed. Transaction information is packaged into blocks and added to the blockchain after consensus validation by other nodes in the network, ensuring the transparency and immutability of transaction data. Simulation results indicate that compared with the existing incentive algorithms, this algorithm not only enhances message delivery probability but also effectively reduces average latency.

A Differential Game Approach to Investment in Greentech Innovation in a Duopoly with Network Externality

In this paper, we revisit a well-known differential game model to investigate the R&D activity for Greentech innovation in a duopoly market with network externality. In our study, firms carry out independent R&D or Cartel R&D for Greentech innovation which aims to reduce end-of-pipe emission and network externality effects play a role in the R&D activity via the inverse demand functions. Firms are concerned not only with their own profits, but also with the relative profits. At the steady-state equilibrium, the efforts for Greentech innovation under independent R&D are higher than that under Cartel R&D when the degree of altruism is higher than the degree of technological R&D spillover, and the network size under independent R&D is lower than that under Cartel R&D. Moreover, under a given market structure, whether profits under independent R&D are higher than under Cartel R&D depends on the initial network size, which means that at the initial moment firms can decide to undertake independent or Cartel R&D activities based on the initial network size.

Pinball boosting of regression quantiles

An algorithm for boosting regression quantiles using asymmetric least absolute deviations, better known as pinball loss, is proposed. Existing approaches for boosting regression quantiles are essentially equal to least squares boosting of regression means with the single difference that their working residuals are based on pinball loss. All steps of our boosting algorithm are embedded in the well-established framework of quantile regression, and its main components – sequential base learning, fitting, and updating – are based on consistent scoring rules for regression quantiles. The Monte Carlo simulations performed indicate that the pinball boosting algorithm is competitive with existing approaches for boosting regression quantiles in terms of estimation accuracy and variable selection, and that its application to the study of regression quantiles of hedonic price functions allows the estimation of previously infeasible high-dimensional specifications.

Equilibrium Interaction Strategies for Integrated Energy System Incorporating Demand-Side Management Based on Stackelberg Game Approach

This paper analyzes the balanced interaction strategy of an integrated energy system (IES) operator and an industrial user in the operation process of the IES under the demand-side management (DSM) based on game theory. Firstly, we establish an electric–thermal IES, which includes a power grid, a heat grid and a natural gas grid. Secondly, a two-stage Stackelberg dynamic game model is proposed to describe the game behavior of IES operators and industrial users in the process of participating in DSM. The interactions between the IES operator (leader) and the user (follower) are formulated into a one-leader–one-follower Stackelberg game, where optimization problems are formed for each player to help select the optimal strategy. A pricing function is adopted for regulating time-of-use (TOU), which acts as a coordinator, inducing users to join the game. Then, for the complex two-stage dynamic game model established, the lower user-side constraint optimization problem is replaced by its KKT condition, so that the two-stage hierarchical optimization problem is transformed into a single-stage mixed-integer nonlinear optimization model, and the branch-and-bound method is introduced to solve it. Finally, the equilibrium strategies and income values of both sides of the game are obtained through a case simulation, and the dynamic equilibrium strategy curves under different capacity configurations are obtained through the sensitivity analysis of key parameters. The equilibrium income of the IES is USD 93.859, while the equilibrium income of industrial users in the park is USD 92.720. The simulation results show that the proposed method and model are effective.

Valuation of a financial claim contingent on the outcome of a quantum measurement

We consider a rational agent who at time 0 enters into a financial contract for which the payout is determined by a quantum measurement at some time T > 0. The state of the quantum system is given in the Heisenberg representation by a known density matrix p^ . How much will the agent be willing to pay at time 0 to enter into such a contract? In the case of a finite dimensional Hilbert space H , each such claim is represented by an observable X^T where the eigenvalues of X^T determine the amount paid if the corresponding outcome is obtained in the measurement. We prove, under reasonable axioms, that there exists a pricing state q^ which is equivalent to the physical state p^ such that the pricing function Π0T takes the linear form Π0T(X^T)=P0Ttr(q^X^T) for any claim X^T , where P0T is the one-period discount factor. By ‘equivalent’ we mean that p^ and q^ share the same null space: that is, for any |ξ⟩∈H one has p^|ξ⟩=0 if and only if q^|ξ⟩=0 . We introduce a class of optimization problems and solve for the optimal contract payout structure for a claim based on a given measurement. Then we consider the implications of the Kochen–Specker theorem in this setting and we look at the problem of forming portfolios of such contracts. Finally, we consider multi-period contracts.

Quantity Conjectural Variations in Oligopoly Games under Different Demand and Cost Functions and Multilevel Leadership

This paper considers a noncooperative game of quantity competition among firms in an oligopoly market under general demand and cost functions. Each firm’s optimal response to the strategies of other firms is assessed by the magnitude and sign of its conjectural variation, expressing the firm’s expectation regarding the counterparty’s supply quantity change in response to the firm’s unit change in its supply quantity. A game of n firms with the sum of conjectural variations (SCV) regarding all counterparties as the generalized response characteristic is studied. The existence of a bifurcation of the players’ response is revealed; a bifurcation is a strategy profile of the game in which both positive and negative responses are possible with an infinite-magnitude SCV value. Methods are developed for calculating the SCV value under different types of inverse demand functions (linear and power) and cost functions (linear, power, and quadratic), and the impact of these characteristics of firms on the bifurcation state is comparatively analyzed.

Online Conversion with Switching Costs: Robust and Learning-Augmented Algorithms

We introduce and study online conversion with switching costs, a family of online problems that capture emerging problems at the intersection of energy and sustainability. In this problem, an online player attempts to purchase (alternatively, sell) fractional shares of an asset during a fixed time horizon with length T. At each time step, a cost function (alternatively, price function) is revealed, and the player must irrevocably decide an amount of asset to convert. The player also incurs a switching cost whenever their decision changes in consecutive time steps, i.e., when they increase or decrease their purchasing amount. We introduce competitive (robust) threshold-based algorithms for both the minimization and maximization variants of this problem, and show they are optimal among deterministic online algorithms. We then propose learning-augmented algorithms that take advantage of untrusted black-box advice (such as predictions from a machine learning model) to achieve significantly better average-case performance without sacrificing worst-case competitive guarantees. Finally, we empirically evaluate our proposed algorithms using a carbon-aware EV charging case study, showing that our algorithms substantially improve on baseline methods for this problem.

Double hedonic price-characteristics frontier estimation for IoT service providers in the industry 5.0 era: A nonconvex perspective accommodating ratios

The advent of advanced digital technologies, including the Internet of Things (IoT), image processing, artificial intelligence (AI), blockchain, robotics and cognitive computing that have been embedded in Industry 5.0, is considerably improving the sustainability, resilience, and human-centric performance of industrial organizations. Despite the increasing use of Industry 5.0 technologies in smart product platforming in industrial organizations, a critical issue remains how to assess the providers/suppliers of such technologies in highly competitive markets to fulfil personalized products and services. Following Lancaster's characteristics approach to consumer theory, in this study we contribute to assess digital technologies service providers in the Industry 5.0 era by focusing on both theoretical and empirical evidence inquiring about the convexity of conventional nonparametric frontier estimation methods. To do so, a nonparametric double frontier estimation of the hedonic price characteristics relation is developed from both the buyer's and seller's perspectives. Moreover, a separable directional distance function-based optimization model is developed for the efficiency estimation. Furthermore, a comparable estimation of the convex and nonconvex hedonic price function is proposed. We also explicitly test the impact of convexity in evaluating the efficiency of IoT service providers in the Industry 5.0 context. In this study, we also show that the hypothesis of convexity in assessing the efficiency of IoT service providers is rejected using the Li-test comparing entire densities in the case of the seller's perspective without ratio data. Differences are less pronounced for the buyer's perspective and in the case with ratio data.