Pricing Method Research Articles

A new pricing method for integrated energy systems based on geometric Brownian motions under the risk-neutral measure

An integrated energy system (IES) is a promising energy-saving technology. With the advancement of technology and deregulation of the energy market, numerous distributed energy systems (DESs) will be accessed in the IES with various trading modes in the future. This makes it even more challenging to evaluate the fair energy selling price of IES, which is of great concern to market regulators and participants. To address this issue, this study proposes a risk-neutral pricing method for IES based on geometric Brownian motions. First, geometric Brownian motions are introduced to characterize the uncertainty feature of the energy price of the DESs. Then, we propose a stochastic partial differential equation (SPDE) for the energy price of an IES by utilizing the Ito lemma and the properties of the martingale under the risk-neutral measure. Finally, sensitivity analyses of the proposed SPDE on the volatilities and two types of correlation coefficients are conducted by implementing the proposed model in the given scenario.

Analisis Penetapan Harga Jual Produk dan Volume Penjualan: Studi Kasus pada Rumah Makan Raja Ayam Kota Gorontalo

This research aims to determine the selling price of products at the Raja Ayam Restaurant in Gorontalo City and to determine the sales volume of products at the Raja Ayam Restaurant in Gorontalo City. The research method uses a survey method. The primary data source in this research is the owner of Raja Ayam Restaurant, Gorontalo City. The data obtained will be presented in tabular form and analyzed descriptively. The results of the research show that the product selling price at the Raja Ayam Restaurant in Gorontalo City is determined by calculating all costs which consist of fixed costs and variable costs. Raja Ayam Restaurant in Gorontalo City uses a selling price method that involves a mark-up of 30% of the total production costs. Thus, the selling price of the product is calculated by adding 30% of the total production costs. This strategy provides clarity in determining prices and includes the desired profit elements. In the main menu of Rumah Makan Raja Ayam, the price of each package or size of chicken is determined by applying a certain mark-up above the purchase price. By using the Mark-Up Pricing Method, these prices cover the profits desired by business actors and provide an overview of the pricing strategy used in the Raja Ayam business in Gorontalo City. The average sales volume of Rumah Makan Raja Ayam reaches around 1,229 packages of chicken every month, with sales of around 40 packages per day. The net income earned every month reaches Rp. Rp. 29,388,093.18, and daily net income is Rp. 979,603.11. Stable sales volume and good income reflect the positive performance and operational success of Rumah Makan Raja Ayam in Gorontalo City.

Analisis Penetapan Harga Jual Berdasarkan Metode Cost Plus Pricing (Studi Kasus pada Rumah Makan Raja Muda Kabupaten Pangkajene dan Kepulauan)

This study aims to analyze the determination of selling prices based on the cost plus pricing method at Raja Muda Restaurant, Pangkajene Regency and Islands. Each restaurant sets very different prices and the way they set those prices also varies. Cost plus pricing is one of the methods used to determine the selling price. The type of research used in this study is a type of qualitative research. The sample of this study is the expenditure record of the Raja Muda Restaurant in October 2023 in Pangkajene and Islands Regency. Data collection was carried out using documentation and interview techniques. Data analysis was carried out by descriptive analysis. The results of this study show that in determining the selling price according to the cost plus pricing method by adding the total cost and mark up by 30.11% and the calculation results are greater than the method applied by restaurant owners with a difference of Rp 253

Claim Prediction and Premium Pricing for Telematics Auto Insurance Data Using Poisson Regression with Lasso Regularisation

We leverage telematics data on driving behavior variables to assess driver risk and predict future insurance claims in a case study utilising a representative telematics sample. In the study, we aim to categorise drivers according to their driving habits and establish premiums that accurately reflect their driving risk. To accomplish our goal, we employ the two-stage Poisson model, the Poisson mixture model, and the Zero-Inflated Poisson model to analyse the telematics data. These models are further enhanced by incorporating regularisation techniques such as lasso, adaptive lasso, elastic net, and adaptive elastic net. Our empirical findings demonstrate that the Poisson mixture model with the adaptive lasso regularisation outperforms other models. Based on predicted claim frequencies and drivers’ risk groups, we introduce a novel usage-based experience rating premium pricing method. This method enables more frequent premium updates based on recent driving behaviour, providing instant rewards and incentivising responsible driving practices. Consequently, it helps to alleviate cross-subsidization among risky drivers and improves the accuracy of loss reserving for auto insurance companies.

MARKETING PRICING POLICY OF A RETAIL BUSINESS ENTERPRISES

Pricing policy is a core element of marketing strategy for any business, especially in the retail sector. In the context of high competition and changing consumer preferences, iterative price adjustments for goods and services become a critical factor for the success of a retail business. Therefore, the article aims to define the basic principles of forming the marketing pricing policy for a retail business. The authors highlight the fact that the marketing pricing policy of a retail business uses price as a tool to achieve specific marketing objectives. It has been proven that such a policy is based on processes and actions involving the continuous analysis and adjustment of prices for goods to balance several core marketing indicators. Future research prospects may focus on analyzing the effectiveness of specific pricing tools and methods of their implementation in the rapidly changing retail market, as well as on developing new approaches for integrating these tools into comprehensive pricing management strategies.

Pricing multi-asset options with tempered stable distributions

We derive methods for risk-neutral pricing of multi-asset options, when log-returns jointly follow a multivariate tempered stable distribution. These lead to processes that are more realistic than the better known Brownian motion and stable processes. Further, we introduce the diagonal tempered stable model, which is parsimonious but allows for rich dependence between assets. Here, the number of parameters only grows linearly as the dimension increases, which makes it tractable in higher dimensions and avoids the so-called “curse of dimensionality.” As an illustration, we apply the model to price multi-asset options in two, three, and four dimensions. Detailed goodness-of-fit methods show that our model fits the data very well.

Machine learning based methods for ratemaking health care insurance

In insurance, proposing an accurate premium that is adjusted to the insured risk profile allows companies to better manage their portfolios and to be more competitive. Machine learning methods have recently been adopted for various improvements in insurance ratemaking, especially in the automobile industry. These models are specifically used to mine potential data information and to build a predictive model for a variable of interest using explanatory variables. In this paper, we aim to provide a pricing method for ratemaking individual healthcare insurance contracts using machine learning algorithms that are applied to a Tunisian healthcare insurance portfolio. We start with a simple Classification and Regression Tree, and we work toward more advanced methods that are Random Forest, Extreme gradient boosting, Support Vector Regression, and Artificial Neural network regression model. The predictive performance of these non-parametric methods is compared with the standard generalized linear model. Our results showed the applicability of machine learning in the healthcare insurance market and that the XGBoost algorithm outperforms the predictive capacity of the classical generalized linear model.

Multi-objective robust dynamic pricing and operation strategy optimization for integrated energy system based on stackelberg game

The integrated energy system (IES) with hydrogen storage has become one of the most important developments in multi-energy coupling field, where the severe conflict of interests between different entities leads to great challenges to the economy and low-carbon operation. A multi-objective robust dynamic pricing and operation strategy optimization method based on the Stackelberg game is proposed for the hydrogen-containing energy storage (HES) IES. Firstly, the HES-IES trading framework is established based on the introduction of an integrated energy operator (IEO) and a load aggregator (LA). Secondly, a multi-objective robust Stackelberg game model is developed with the IEO as the leader and the LA as the follower, considering the minimization of operating costs and carbon emissions of the IEO and the minimization of integrated energy costs of the LA as the objectives. Finally, the compromise planning and the max-min fuzzy are addressed to solve the multi-objective model, which adopts the adaptive differential evolution (ADE) algorithm. In addition, the robust optimization (RO) with adjustable coefficients is employed to tackle uncertainties of source and load. The results show that this method can effectively balance the operating costs and carbon emissions of the system, improve the benefit of the IEO, reduce the costs of the LA, and avoid the uncertainty risk. Compared with traditional algorithms, the ADE algorithm has significant advantages in the number of iterations and solving time. In summary, the multi-objective robust dynamic pricing and operation strategy optimization proposed in this paper could effectively achieve the benefit balance between the IEO and the LA, also further improve the economy and robustness of the system and reduce carbon emissions.

Coca-Cola Transfer Pricing Dispute: United States Case Analysis and Tax Policy Lessons for Indonesia

Decision of the Coca-Cola transfer pricing dispute in the United States Tax Court, which resulted in tax underpayment, can be used as a lesson for Indonesia. This research method is doctrinal, with a law, case, concept and interpretation approach. The data studied is secondary data in the form of laws, cases and relevant references, which are analyzed to obtain conclusions and recommendations. Research shows that the court considered using the Comparable Profits Method by tax authorities to evaluate the profitability of The Coca-Cola Company's supply points, given the difficulty of valuing intangible assets directly and the need for disproportionate profit adjustments. To avoid similar problems, companies in Indonesia must use transfer pricing methods that are reliable and appropriate to local conditions, as well as ensure good intellectual property management and documentation transparency, while tax authorities must monitor transactions between affiliated entities by international standards and the Arm's Length Principle.

Revenue Service 6 March 2024 TC Judgment Case No. IT 14302

This article deals with the first transfer pricing case to come before the courts in South Africa which deals with the transfer pricing provisions contained in the South African tax legislation. It has attracted much interest and attention from tax practitioners in South Africa. It relates to the determination of an arm’s length royalty in respect of intellectual property in the form of a trademark. After a consideration of various methods to be used in determining the arm’s length rate of the royalty, the court held that the comparable uncontrolled price method was the appropriate method to be used in the circumstances and that the appellant possessed a valid internal comparable. Accordingly, the court upheld the appeal of the taxpayer.

Get by how much you pay: A novel data pricing scheme for data trading

As a crucial step in promoting data sharing, data trading can stimulate the development of the data economy. However, the current data trading market primarily focuses on satisfying data owners' interests, overlooking the demands of data requesters. Ignoring the demands of data requesters may lead to a loss of market competitiveness, customer loss, and missed business opportunities while damaging reputation and innovation capabilities. Therefore, in this paper, we introduce a novel pricing mechanism named Get By How Much You Pay (GHMP) based on compressed sensing technology and game theory to address pricing problems according to data requesters' demands. This scheme employs a dictionary matrix as the sparse basis matrix in compressed sensing. The quality of this matrix directly affects the precision with which the requester can reconstruct the data. If the requester requires higher-precision data, the corresponding payment will also increase accordingly so as to realize the pricing method based on the requester's demands. A game pricing method is proposed to address the final pricing and purchasing issues between the data requester and the data owner by utilizing an authorized smart contract as an intermediary. As an entity participating in the game, the smart contract can only receive a higher transaction fee if it successfully assists the data requester and data owner in completing the pricing. Therefore, it strives to establish more reasonable prices for both parties during the trading process to obtain profits. The experimental results demonstrate that this game-based approach assists the data requester and owner in achieving optimal data pricing, thereby satisfying the maximization of interests for both parties.

METODE BLACK-SCHOLES DALAM PENENTUAN HARGA OPSI

The Black-Scholes Method is one of the option pricing method that introduced by Fischer Black and Myron Scholes in 1973. This study aim to review the determination of the price of an option with stocks as the underlying assets and based on Black and Scholes assumptions. These assumptions lead to construct an equation named Black-Scholes differential equations, which is the equation that must be satisfied for option as the derivative instrument and non-dividend giving stocks as the underlying assets. After the Black-Scholes differential equations formed successfully, the next step is to find the solution of that equation. Consider the option is call option, the solution that will be obtained from solving the equation is the price of call option. Then substitute it to the put-call parity equation, which is the equation that shows the relationship between call and put option prices, so the price of put option can be obtained too. The estimation that obtained from the Black-Scholes method is the highest price for a contract is said to be fair and worth to buy for the holder.

Effectiveness of Community Forest Management as an Ecosystem-Based Adaptation Strategy in the Context of Nepal

Objectives: This study attempted to explore the effectiveness of community forest (CF) management as an ecosystem based adaptation (EbA) strategy in combating and adapting with the climate change impacts upon local ecosystems and communities. Method: In order to identify EbA options, effectiveness, efficiency, sustainability, governance and socioeconomic function of CF were analyzed. Effectiveness and sustainability were examined and compared through Land Use Land Cover (LULC) database during 1996 and 2021. Efficiency was explored through the economic valuation of provisioning and cultural services, using economic approach–revealed price (market price) method. Socioeconomic functions and governance were examined through the existing plans/policies and secondary data review. Findings: CF is effectively and sustainably managing the available resources. It has increased forest cover, water body, grassland and remarkable decline in bush and sand area. Bush area (79.25 ha in 1996) dropped (to 6.52 ha in 2021) while forest cover increased from 340.46 ha to 375.54 ha. Waterbody (pond or lake area) was limited to 0.88 ha that expanded to 5.04 ha in 2021 due to the conservation oriented management practices. Meanwhile, economic value of provisioning services is USD 7182 accounting wood and timber the highest (average annual USD 4800) among the provisioning services while average annual value of firewood and poles is USD 1367 and NTFP (fish and rubber) is USD 1015 whereas the economic value of cultural services is USD 18803. Novelty: The novelty of the research work lies in the unique methodology and the selection of CF for the EbA case study, which is least explored. By exposing the integration of ecological and socio-economic considerations, this research underscores the significance CF as EbA option in shaping sustainable development pathway in Nepalese context. Keywords: Ecosystem Services, Ecosystem Based Adaptation, Community Forest, Nepal, Land Management

Sequential pricing of electricity

This paper investigates the design and analysis of price formation in wholesale electricity markets given variability, uncertainty, non-convexity, and intertemporal operating constraints. The paper’s primary goal is to develop a framework to assess the many resource participation models, reserve product definitions, and enhanced pricing methods that have arisen in U.S. systems, especially in the context of growing contributions from wind, solar, and storage. Departing from the static models typically used for electricity auctions based on thermal resources, the paper situates price formation within the sequential decision problem faced by system operators. This more complete description of the problem has several implications for price formation. Since prices are derived from operational models, algorithmic choices in the design of policies for the sequential decision problem influence the prices ultimately formed. In numerical tests, policy variants with comparable operational performance (within 4% in terms of total cost) lead to substantial differences in prices and resource remuneration. Storage is particularly affected, earning revenues ranging from 57% to 117% of the amount suggested as economically efficient by a benchmark approximated through stochastic programming.

Towards optimal customized electricity pricing via iterative two-layer optimization for consumers and prosumers

Designing an electricity price mechanism that efficiently enables users to manage their self-production and consumption behavior to accommodate renewable energy and regulate peak load is a hard task with the emergence of prosumer with photovoltaic and energy storage. To address this issue, we propose an iterative two-layer optimization to investigate customized price of electricity for different types of users in the presence of photovoltaic uncertainty. By taking into account power flow restrictions, the model is used to explore the optimal time-of-use, demand, and on-grid prices for both electricity consumers and prosumers. The consumer and prosumer peak-flat-valley periods are initially divided in the upper layer. Then, a model for distribution system operator profit maximization is proposed to minimize operation cost, power loss, and peak–valley difference. A stochastic optimization model is presented in the lower layer for consumer and prosumer to hedge against the risk of uncertain photovoltaic with the aim of minimizing electricity tariff, demand tariff, and life cycle cost of energy storage. Numerical experiments conducted on modified 15-bus and 69-bus distribution systems with different types of users show the effectiveness of the iterative two-layer optimization for customized price of electricity in peak-shaving and valley-filling while ensuring the interests of distribution system operator and users. Results show that the total profit increases by more than 4% and the net load fluctuation variance reduces higher than 20% compared with the traditional pricing method.

The life care annuity: enhancing product features and refining pricing methods

AbstractThe state-of-the-art proposes life care annuities, that have been recently designed as variable annuity contracts with Long-Term Care payouts and Guaranteed Lifelong Withdrawal Benefits. In this paper, we propose more general features for these insurance products and refine their pricing methods. We name our proposed product “GLWB-LTC”. In particular, as to the product features, we allow dynamic withdrawal strategies, including the surrender option. Furthermore, we consider stochastic interest rates, described by a Cox–Ingersoll–Ross process. As to the numerical methods, we solve the stochastic control problem involved by the selection of the optimal withdrawal strategy through a robust tree method, which outperforms the Monte Carlo approach. We name this method “Tree-LTC”, and we use it to estimate the fair price of the product, as some relevant parameters vary, such as, for instance, the entry age of the policyholder. Furthermore, our numerical results show how the optimal withdrawal strategy varies over time with the health status of the policyholder. Our findings stress the important advantage of flexible withdrawal strategies in relation to insurance policies offering protection from health risks. Indeed, the policyholder is given more choice about how much to save for protection from the possible disability states at future times.

Trading mechanism for social welfare maximization in integrated electricity and heat systems with multiple self-interested stakeholders

Cooperation across multiple energy sectors is capable of maximizing social welfare as revealed by existing research, but such holistic dispatch can be hardly implemented in a reality where energy sectors are run by different stakeholders respectively. To fill this gap, energy trading among multiple self-interested stakeholders is investigated in this paper. To maximize the social welfare of the trading results, an effective pricing method, referred to as network-constrained generalized locational marginal prices, is proposed. A trading mechanism is then carefully designed to encourage stakeholders to trade at network-constrained generalized locational marginal prices, while economic properties of the resultant generalized Nash equilibrium are analyzed in depth. The effectiveness of the mechanism is validated in two integrated electricity and heat systems with different scales. Numerical comparison demonstrates that existing non-cooperative mechanisms may result in undesirable deadweight losses, while surplus sharing is a tricky issue in cooperative mechanisms, making the cooperation unstable and infeasible. On the contrary, each stakeholder may optimize its subsystem in a self-interested manner in the proposed mechanism, but the overall outcome strikingly coincides with social welfare maximization.

A Novel Function of a Research Process Based on a Power Internet of Things Architecture Intended for Smart Grid Demand Schemes

The global energy sector is currently undergoing a significant transformation to address sustainability, energy efficiency, and grid resilience. Smart grids, leveraging advanced technologies like the power internet of things (PIoT), play a crucial role in this transformation. This research focuses on enhancing the efficiency, reliability, and sustainability of electricity distribution through IoT technologies. It envisions a system where interconnected devices, sensors, and data analytics optimize energy consumption, monitor grid conditions, and manage demand response scenarios. Central to this effort is the integration of PIoT into the smart grid infrastructure, particularly in implementing dynamic pricing strategies for demand response. Leveraging power line communication (PLC) techniques, this innovative approach facilitates real-time communication between grid components and consumers. The results demonstrate improved grid stability through dynamic load management, effectively responding to demand fluctuations, and minimizing disruptions. The deployment of dynamic pricing methods using PLC-driven schemes empowers customers by offering access to real-time energy use data. This access incentivizes energy-efficient behavior. leading to a 30% increase in the adoption of energy-saving techniques among consumers. A utility company pilot study claimed a 12% drop in peak demand after adopting time-of-use charges, with an accuracy rate of 98.87% in total.

Tax Crime Risk in Mining Sector Companies : An Examination of Transfer Pricing Analysis and Capital Intensity

This research investigates how capital intensity and transfer pricing may affect the likelihood of tax offenses in the mining industry. The report uses a sample of mining businesses listed from 2018 to 2022 on the Indonesian Stock Exchange (IDX) and using a quantitative research technique. The research examines the links between the dependent variable, which is represented by the cash effective tax rate (CETR), and the independent variables, transfer pricing (TP) and capital intensity (CAP), using multiple regression analysis and a legal approach. The corporations' transfer pricing methods and CETR vary significantly, according to descriptive data, indicating intricate dynamics in their tax management techniques. The results show a positive but statistically insignificant association between transfer price and CETR, which is consistent with contradictory findings from earlier research that supported and refuted the relevance of this relationship. The study's findings are important to policymakers because they imply that, despite transfer pricing's existence, its effect on tax evasion could not be as significant or clear-cut as previously thought. This realization necessitates a more sophisticated approach to mining industry compliance and regulatory frameworks, especially in environments like Indonesia. To get a deeper understanding of the changing dynamics of capital intensity and transfer pricing in influencing tax policies, future study should broaden its scope and include a wider range of factors over a longer time period.

PRICING STRATEGIES FOR PERFORMING ARTS: A THEORETICAL PERSPECTIVE

This paper explores the various pricing strategies for performing arts. The traditional pricing strategies focus more on tangible goods that differ from services in many respects. Even in the service category, performing arts have a unique place. The reasons for this uniqueness are explained in this paper. Innovative pricing methods are extensively used in modern times. The study compares traditional, dynamic, and innovative pricing strategies for performing arts. The study suggests that there is no single strategy for any performing art. It varies with time, quantity and quality of audience, place, duration of performance, degree of competition, brand image, etc.