Unique Price Research Articles

Quantum computational finance for martingale asset pricing in incomplete markets

A derivative is a financial asset whose future payoff is a function of underlying assets. Pricing a financial derivative involves setting up a market model, finding a martingale (“fair game”) probability measure for the model from the existing asset prices, and using that probability measure to price the derivative. When the number of underlying assets and/or the number of market outcomes in the model is large, pricing can be computationally demanding. In this work, we first formulate the pricing problem in a linear algebra setting, including the realistic setting of incomplete markets where derivatives do not have a unique price. We show that the problem can be solved with a variety of quantum techniques such as quantum linear programming and the quantum linear systems algorithm. While in previous works the martingale measure is assumed to be given, here it is extracted from market variables akin to bootstrapping, a common practice among financial institutions. We discuss the quantum zero-sum game algorithm and the quantum simplex algorithm as viable subroutines. For quantum linear systems solvers, we formalize a new market assumption milder than market completeness, which allows the potential for large speedups. Towards prototype use cases, we conduct numerical experiments motivated by the Black–Scholes–Merton model.

Optimized shared energy storage in a peer-to-peer energy trading market: Two-stage strategic model regards bargaining and evolutionary game theory

With the increasing demand of users for distributed energy storage (ES) resources and the emerging development of peer to peer (P2P) transaction technology, shared energy storage (SES) has great potential to contribute into new business models of demand-side ES. In order to compromise essential elements like safety, stability and efficiency of P2P trading, as well as to improve the utilization rate of demand-side ES, this paper devotes to construct a P2P transaction framework based on a partially decentralized topology and proposes a two-stage trading optimization strategy of SES in a P2P market, considering the equilibrium state of supply and demand flow. In the first stage, this paper simultaneously balances the interests of buyers and sellers and brings the carbon trading mechanism into the transaction process. The interaction of interests of bilateral parties with consideration of carbon trading mechanism has been investigated, and a SES capacity sharing model is, then, established based on the bargaining game theory. In the second stage, a unique pricing mechanism for SES leasing fee is designed based on a multi-strategy evolutionary game model, considering bounded rational decision-making for SES operators and communities. Finally, numerical simulation verified the feasibility and superiority of the proposed P2P trading strategy of SES.

CONVERGENCE OF WHOLESALE ELECTRICITY PRICES ACROSS EUROPEAN MARKETS: ARE WE THERE YET?

European electricity market integration has been a research focus, especially after the third electricity directive emphasizing the internal market. Electricity markets are characterized by immediate consumption with storage and transmission constraints, leading to unique price dynamics. Price convergence in European markets can lead to competitive pricing, advocating market integration. This research examines whether European wholesale spot electricity prices converge. Prior literature uses sigma and beta-convergence to test price convergence. We have examined the stochastic convergence of prices across 19 European nations from 2013 to 2021 utilizing monthly data. For this analysis, we employed LM tests incorporating trend breaks grounded on the RALS regression framework. RALS-LM tests are more powerful than linear tests when structural breaks and nonnormal errors are present. LM and RALS-LM unit root tests with two trend breaks resulted in rejection of the unit root hypothesis for all countries, suggesting relative wholesale prices are stationary. Thus, shocks to relative prices are transient, supporting price convergence at the wholesale level. This result should be considered carefully as it contradicts the results from recent literature that examines retail electricity prices using beta- sigma- and club convergence. The policy implications of this finding can provide a foundation for establishing policies that promote a harmonized and integrated European electricity market.

DIGITALISATION OF PRICING METHODS TO DETERMINE THE OPTIMAL PRICE

The article discusses modern approaches to pricing in the context of digital business transformation. The author discusses the impact of artificial intelligence, dynamic pricing and personalisation on the development of optimal pricing strategies. The article reveals how machine learning algorithms allow businesses to effectively analyse large amounts of data and accurately predict market trends. Focusing on dynamic pricing, in the context of the digital environment, it is revealed as a strategy that allows companies to respond quickly to changing market conditions and effectively adapt prices to demand and competition in real time. A specific feature of the article is the analysis of the impact of personalisation on price offers. Taking into account the individual needs and buying habits of customers allows businesses to create unique pricing strategies that increase customer loyalty and satisfaction. Personalisation of price offers and experimentation with different strategies are also identified as key factors in optimising pricing. This article serves as an important reference for those seeking to understand how digital tools are shaping a new approach to effective pricing management in today's business environment. Overall, the digitalisation of marketing pricing methods not only ensures greater accuracy and efficiency in pricing, but also opens up new opportunities for businesses to adapt to changing market conditions and meet individual customer needs. Businesses can use digital platforms to hold auctions or listings for their goods or services. This not only creates competition among buyers, but also allows them to effectively determine the optimal price based on real demand. The digital transformation in marketing pricing opens up new opportunities for businesses to determine the optimal price of goods. The use of analytics, personalisation, dynamic pricing and other digital tools allows businesses to maximise profits by adapting to rapidly changing market conditions. The development of digital pricing strategies is setting a new standard for effective pricing management, allowing companies to remain competitive in today's digital business environment.

Purchase decision patterns of non-bank sharia housing in Bandar Lampung City during Covid-19: Price exploration and digital marketing

IntroductionThe housing sector, including sharia housing, has been affected by Covid-19. Many studies have analyzed the impact of Covid-19 on the sharia housing industry, but research focusing on non-bank financing is still limited. ObjectivesThis study aims to analyze how consumer purchasing decisions for housing during Covid-19 are influenced by price and digital marketing. MethodThis study can be categorized as quantitative study by collecting data from 500 consumers selected through purposive sampling in the Bandar Lampung City. The obtained data was then analyzed using SmartPLS software. ResultsThe result shows that price and digital marketing have significant impact on consumers’ purchase decision of non-bank Sharia housing during Covid-19. ImplicationsSharia housing developers should effectively utilize digital marketing content and pricing strategy to attract potential consumers in Bandar Lampung City.Originality/NoveltyThis study highlights unique pricing and digital marketing strategies by Sharia housing developers during Covid-19. This study is also unique for its approach to non-bank Sharia housing as mode of financing.

Decoding the Unique Price Behavior in the Japanese Stock Market with Convolutional Neural Networks

Decoding the Unique Price Behavior in the Japanese Stock Market with Convolutional Neural Networks

HTA74 Novel Approach to Decision Making for Orphan Drugs

This policy perspective presents a unique pricing and reimbursement (P&R) system for orphan medicinal products (OMP) recently adopted in Czechia.

Traveling salesperson problem with unique pricing and stochastic thresholds

In this variant of traveling salesperson person problem, only the customers who accept to pay a unique service fee are serviced. Each customer has a random price acceptance threshold and accepts to pay any fee lower than this threshold. To address this issue, a chance-constrained stochastic model is developed and two exact methods, one based on a Branch & Cut scheme, and the other on Logic Based Benders Decomposition, are built. Numerical tests show that, on small instances both methods are efficient but on larger instances with 100 customers, Logic Based Benders Decomposition dominates clearly the Branch & Cut method.

FACTORS of PRODUCTION RICE CORPORATE FARMING MODEL in CENTRAL JAVA

The agricultural sector is still faced with various problems. Starting from the increasingly narrow land, decreased land productivity due to the use of inappropriate production factors, erratic and unpredictable climatic conditions to the issue of unique prices that fluctuate at any time. These obstacles and constraints will ultimately affect the income of farmers. The distribution of income is obtained from the production and productivity of rice farming during the growing season. Increased production can be done through the agricultural model of corporate farming. This study aims to estimate the factors that influence rice production with the corporate farming model. The research location was determined purposively in Sukoharjo Regency. Sampling by census was 51 corporate farmers. Estimated data to produce factors of production using the Cobb-Douglass production function model. The results showed that the factors that significantly affected the increase in rice production using the corporate farming model were land area and urea fertilizer. Meanwhile, the production factors of SP-36 fertilizer, NPK fertilizer, pesticides and labor did not have an effect on the increase in rice production so that these production factors could be an opportunity to improve production and productivity.

Practical Applications of Does Rare Whisky Add Value in Multi-Asset Portfolios?

In <b><i>Does Rare Whisky Add Value in Multi-Asset Portfolios?</i></b> from the Spring 2022 issue of <b><i>The Journal of Alternative Investments</i></b>, <b>Lars Tegtmeier</b> at <b>Merseburg University of Applied Sciences</b> finds that investing in rare whisky¹ adds value to a portfolio. He explains that supply and demand factors driving whisky prices are different from many other assets. This difference stems in part from the whisky aging process, so the current supply is due to production decisions made many years in the past. Past demand and supply imbalances led to a shortage in rare whisky and subsequently a dramatic increase in auction sales of whisky bottles and high returns. Tegtmeier explains that the unique pricing factors driving returns are uncorrelated with other major asset classes. He then studies the impact of adding rare whisky to multiasset portfolios. Additionally, he reviews several methods of accessing rare whisky exposure and the associated costs in this small but developing market.

Pricing Patterns in Wholesale Electricity Markets: Unilateral Market Power or Coordinated Behavior?*

AbstractWe examine allegations that firms in Alberta's electricity industry manipulated public information to coordinate in the wholesale market. We investigate whether bids by firms who employed unique pricing patterns were consistent with unilateral expected profit maximization. Our results suggest that these firms could have increased expected profits through unilateral deviations. For one firm, the potential to increase profits is greater on days when certain offer patterns are observed, providing support for the claim that such patterns may have assisted coordination on high‐priced outcomes. These results suggest that regulators should exercise caution when designing information disclosure policies in concentrated electricity markets.

Asset Pricing Model Based on Fractional Brownian Motion

This paper introduces one unique price motion process with fractional Brownian motion. We introduce the imaginary number into the agent’s subjective probability for the reason of convergence; further, the result similar to Ito Lemma is proved. As an application, this result is applied to Merton’s dynamic asset pricing framework. We find that the four order moment of fractional Brownian motion is entered into the agent’s decision-making. The decomposition of variance of economic indexes supports the possibility of the complex number in price movement.

Stable allocations for choice-based collaborative price setting

Horizontal price agreements can fall within the scope of exemptions to antitrust competition if they are expected to create pro-consumer benefits. Inspired by such horizontal agreements, we introduce a cooperative game in which a set of transport operators can collectively decide at what price to offer sustainable urban mobility services to a pool of travelers. The travelers choose amongst the mobility services according to a multinomial logit model, and the operators aim at maximizing their joint profit under a constant market share constraint. After showing that various well-known allocation rules (i.e., proportional rules and the Shapley value) do not always generate core allocations, we present a core-guaranteeing allocation rule, the market share exchange rule. This rule first allocates to each transport operator the profit he or she generates under collaboration, and then subsequently compensates those transport operators that lose part of their market share, which is paid by the ones that receive some extra market share. This exchange of market share is facilitated by a unique price, which can be expressed as the additional return by cooperating per unit of market share. Finally, we show that, under some natural conditions, the market share exchange rule still sustains the collaboration when the transport operators need to pay back part of the joint profit to society.

Cost-savings and potential cost-savings through the distribution of generic antiretroviral drugs within the statutory health insurance market of Germany between January 2017 and June 2019

BackgroundRecent patent losses for antiretroviral drugs (ARV) have led to the debate of cost-saving through the replacement of patented drugs with generic drugs. The split of recommended single-tablet regimens (STR) into their single substance partners is one of the considerations mentioned in said debate. Particularly, generic tenofovir disoproxil/emtricitabine (TDF/FTC) is expected to hold untapped cost-saving potential, which may curb increasing overall expenditures for combined antiretroviral therapy (cART) within the statutory health insurance (SHI) of Germany.MethodsData of ARV reimbursed by the SHI were used to describe the trends of defined daily doses (DDD) as well as the revenue within the German ARV market. They were also used to determine the cost-savings of moving to generic drugs. The time period observed was between January 2017 and June 2019. The potential cost-savings were determined with following assumption in mind: the maximum possible use of generic ARV, including 1) the split of STR and replacing all substance partners with generic ones, and 2) replacing patented tenofovir alafenamide/emtricitabine (TAF/FTC) with generic TDF/FTC.ResultsThroughout the observation period, the DDD of generic ARV increased nearly five-fold while their revenue increased more than four-fold. Total cost-saving showed a sharp increase over the same period, with generic TDF/FTC accounting for a share of around 70%. The largest potential cost-saving could have been achieved through replacing patented TAF/FTC with generic TDF/FTC, peaking at nearly 10% of total revenue, but showing decreasing trends in general.ConclusionThe progressive distribution of generic ARV ensured increasing cost-savings, but consequently curbed the potential cost-savings. Unique price reductions of generic TDF/FTC have played a pivotal role for these effects. In any case, substituting with generic ARV should not fail to adhere to the treatment guidelines and continue to consider the medical requirements for the treatment.

MANAGEMENT POLICY IN TNK MARKETING STRATEGIES

The organization of international marketing during the globalization phase requires an understanding of the problems of glocalization, which is a tendency of combination of both global and regional, local. TNCs, by applying global marketing strategies, are confronted with pluralism of cultures, cultural standards, multiculturalization which actualizes the appeal to multilocal strategies. The conquest of world TNC markets in the modern era is based on global competition strategies, made possible by the rapid development of information technologies and markets liberalization. The strategies of companies entering the world markets are multivariable, however, they are primarily guided by the strategies of creating competitive advantages and are aimed at successfully overcoming the uncertainty of the business environment, reducing costs through the introduction of innovations. For countries in which TNCs are present in domestic markets, their activities provide additional incentives to increase the quality of products and services of national companies, and they also have the opportunity to use the experience, technology, development strategies of TNCs, in particular those that have been successfully implemented in other countries. Local strategies are used by those companies that strive to take into account the specifics and uniqueness of each national market that they enter. Such companies develop a unique price policy for each new market.

Assessing the Regional Redistributive Effect of Renewable Power Production Through a Spot Market Algorithm Simulator: The Case of Italy

We develop an algorithm that simulates by iterative splitting the hourly equilibrium (price-quantity) of the Italian day-ahead market. The algorithm is employed to study the sensitivity of equilibria to changes in production from renewable units at different locations. We show that, when power markets are organised on zonal-basis with locational price signals and final buyers pay a unique price for the power bought in the day-ahead market, a larger renewable production decreases the average zonal prices, but the distribution of benefits largely depends on power plants’ localisation. We analyse the impact of a larger renewable production on network congestion occurrence, zonal balance between demand and supply and zonal generation mix as well. We calculate the zonal substitution effects between renewable and non-renewable technologies, and within renewable technologies. Our analysis sheds some lights on the multiple consequences of energy transition policies and highlights the need of prioritizing over policies’ objectives.

Formula omitted]: COVID-19 adopted Hybrid and Parallel deep information fusion framework for stock price movement prediction

The novel of coronavirus (COVID-19) has suddenly and abruptly changed the world as we knew at the start of the 3rd decade of the 21st century. Particularly, COVID-19 pandemic has negatively affected financial econometrics and stock markets across the globe. Artificial Intelligence (AI) and Machine Learning (ML)-based prediction models, especially Deep Neural Network (DNN) architectures, have the potential to act as a key enabling factor to reduce the adverse effects of the COVID-19 pandemic and future possible ones on financial markets. In this regard, first, a unique COVID-19 related PRIce MOvement prediction (COVID19 PRIMO) dataset is introduced in this paper, which incorporates effects of social media trends related to COVID-19 on stock market price movements. Afterwards, a novel hybrid and parallel DNN-based framework is proposed that integrates different and diversified learning architectures. Referred to as the COVID-19 adopted Hybrid and Parallel deep fusion framework for Stock price Movement Prediction (COVID19-HPSMP), innovative fusion strategies are used to combine scattered social media news related to COVID-19 with historical mark data. The proposed COVID19-HPSMP consists of two parallel paths (hence hybrid), one based on Convolutional Neural Network (CNN) with Local/Global Attention modules, and one integrated CNN and Bi-directional Long Short term Memory (BLSTM) path. The two parallel paths are followed by a multilayer fusion layer acting as a fusion center that combines localized features. Performance evaluations are performed based on the introduced COVID19 PRIMO dataset illustrating superior performance of the proposed framework.

Operational flexibility enhancements using mobile energy storage in day-ahead electricity market by game-theoretic approach

The global share of renewable energy sources (RES) in total generation capacity reached 34.7% in 2019 and has been continuously increasing. Power system flexibility addressing the uncertainty and variability of RES has become a major concern in energy transition. This paper proposes to apply mobile energy storage (MES) from independent MES owners as a flexibility-enhancement ancillary service in the day-ahead electricity market. First, we have proposed a market-based methodology to incent MES owners to provide flexibility service in power systems by simultaneously releasing Locational Marginal price (LMP) and Uncertainty Marginal Price (UMP). The LMP and UMP were obtained by solving robust unit commitment and security-constrained economic dispatch, representing the need for flexibility resources. Second, the optimal operational strategy of MESs was established to respond to LMP and UMP and maximize their profits by providing energy and reserve flexibility subjects to the reserve and traffic constraints. Third, a game-theoretic model was developed to determine market equilibrium by minimizing the operational costs of the power system and maximizing the profits of MES owners. To avoid homogenous behaviors of MESs in solving this model, a sequential dispatching algorithm was developed such that each MES of one MES owner has a unique price signal. An objective function update strategy was accordingly developed to take the interaction among MESs into consideration and ensure a maximum total profit for MES owners. Finally, case studies with IEEE 6-bus system, IEEE 118-bus system, and MATPOWER 300-bus system were provided to validate the proposed methods, demonstrating that MESs increase power system flexibility and their own profits while decreasing the energy and reserve payment of consumers.

Prevision and planning for residential agents in a transactive energy environment

Transactive Energy (TE) has brought exciting opportunities for all stakeholders in energy markets by enabling management decentralization. This new paradigm empowers demand-side agents to play a more active role through coordinating, cooperating, and negotiating with other agents. Nevertheless, most of these agents are not used to process market signals and develop optimal strategies, especially in the residential sector. Accordingly, it is indispensable to create tools that automate and facilitate demand-side participation in TE systems. This paper presents a new methodology for residential automated agents to perform two key tasks: prevision and planning. Specifically, the proposed method is applied to a forward market where agents' planning is a fundamental step to maintain the dynamic balance between demand and generation. Since planning depends on future demand, agents' prevision of consumption is an inevitable part of this step. The procedures for automating the targeted tasks are developed in a general way for residential prosumers and consumers, interacting at the distribution level. These players are managed by a demand aggregator as the leader by means of the Stackelberg game. The suggested process results in a TE setup for multi-stage single-side auctions, useful to manage future Smart Energy Markets. Through simulated transactions, this paper examines the market clearing mechanism and the convenience of agents' planning. The results show that customers with higher price-elasticity leverage lower costs periods. However, they make it harder to reduce the peak-to-average ratio of the aggregated demand profile since a unique price signal can create prisoner's dilemma conditions.

Rethinking Commodities

Commodities have long been viewed as the poor cousin in the investment universe, and often for a good reason. Unlike equities, commodities do not offer a so-called market beta with prices drifting higher over time in line with economic activity. In contrast, they present a collection of unique price returns that reflect the underlying supply and demand dynamics of physical assets that serve as the global economy’s building blocks. This article takes a new look at commodities as an asset class and at their uses in a portfolio, which historically have been diversification and inflation protection. The authors analyze different commodity beta allocations and identify alternative investment uses of commodities. Such uses include building blocks to express particular investment themes, tactical trading tools, and multiasset risk premia allocation components. <b>TOPICS:</b>Commodities, portfolio construction, financial crises and financial market history <b>Key Findings</b> ▪ Although investors have rightly questioned the benefits of commodities in a diversified portfolio over the past decade, there may still be some validity to the diversification and inflation protection arguments touted by long-only commodity advocates. ▪ Evidence suggests a positive relationship between the level of inflation and returns of a broad basket of commodities. The contemporaneous nature of this relationship can make it difficult to capture unless exposure is held continuously, which may be an unjustifiable drag on the performance of a diversified multiasset portfolio. ▪ The commodities market is well suited to risk premia strategies. It is a unique market in which participants with different objectives interact on a futures curve, thereby creating persistent and harvestable risk premia.