Pricing Model Research Articles

Research on the Pricing Model of Financial Derivatives

Based on the importance of the pricing model of financial derivatives, this paper points out the defects and risks of the current model and proposes some possible solutions. The research method is mainly to summarize the framework of the existing model by searching the academic literature and commercial examples related to the pricing model. The final conclusion is that an obvious flaw of the pricing model is that it is based on an idealized market state and cannot predict hidden market trends, but through random volatility, consideration of market friction, and the development of mathematical technology, the market can refine the pricing model. Research and forecast risk premium, market liquidity and asset price changes in advance when considering pricing. In addition, the market is also supported to consider the application of large machines in the portfolio as well as the issue of options panels, so as to deepen the understanding of the movement of financial products.

Analysis of the Impact of Luxury Marketing Methods on Customer Consumption Psychology

Our research object is Herms, a world-renowned luxury brand, especially its brand strategy, distribution and purchase system, and the psychological characteristics of brand consumers. We use a variety of research methods for analysis, including an in-depth study of Herms' brand history, product lines, and marketing strategies through literature. Using the 4P marketing theory model of product, price, location and promotion, it makes a comprehensive analysis of Hermes' marketing strategy. Finally, a questionnaire about Hermes consumers was used to conduct detailed data analysis and interpretation. It is found that the success of Hermes is largely due to its unique brand strategy and marketing strategy. Although the distribution system is controversial, for Herms, its existence is exactly in line with its brand positioning, and it has successfully attracted some wealthy consumers who are willing to pay for luxury experiences.

Low carbon dispatch method for hydrogen-containing integrated energy system considering seasonal carbon trading and energy sharing mechanism

The increase in the share of renewable energy sources in the integrated energy system (IES) has led to the bidirectional flow of electricity and carbon in the transmission and distribution network, which has increased dispatch complexity. To address these issues, this paper proposes an optimal scheduling method for IES considering the seasonal carbon trading mechanism and electricity-carbon quota sharing method. First, an energy hub model of the integrated electric-heat-carbon energy system was established, with the introduction of carbon flow. Then, a seasonal carbon trading mechanism that considers the characteristics of carbon emissions in each period of the system is proposed, and the ladder-type carbon price model and carbon quota allocation method are optimized. Moreover, to fully utilize the interaction between electricity and carbon to achieve low-carbon economic dispatch, a dual time-scale electricity-carbon quota sharing method is proposed. Finally, the effectiveness of the proposed method is verified by the case studies. The results show that the scheduling method considering seasonal carbon trading mechanism and electricity-carbon quota energy sharing can optimize the allocation of resources such as electricity and carbon quota among the systems, and reduce the cost of the system by 6.04 % and carbon emission by 4.27 %.

The Role of FPGAs in Modern Option Pricing Techniques: A Survey

In financial computation, Field Programmable Gate Arrays (FPGAs) have emerged as a transformative technology, particularly in the domain of option pricing. This study presents the impact of Field Programmable Gate Arrays (FPGAs) on computational methods in finance, with an emphasis on option pricing. Our review examined 99 selected studies from an initial pool of 131, revealing how FPGAs substantially enhance both the speed and energy efficiency of various financial models, particularly Black–Scholes and Monte Carlo simulations. Notably, the performance gains—ranging from 270- to 5400-times faster than conventional CPU implementations—are highly dependent on the specific option pricing model employed. These findings illustrate FPGAs’ capability to efficiently process complex financial computations while consuming less energy. Despite these benefits, this paper highlights persistent challenges in FPGA design optimization and programming complexity. This study not only emphasises the potential of FPGAs to further innovate financial computing but also outlines the critical areas for future research to overcome existing barriers and fully leverage FPGA technology in future financial applications.

IMPLEMENTATION OF A DYNAMIC PRICING SYSTEM IN RETAIL: AN ANALYTICAL REVIEW OF THE PRACTICES OF RUSSIAN COMPANIES

Over the past few years, the conditions and nature of doing business for Russian companies have changed dramatically. The processes of procurement, logistics, product sales, accounting and other business processes are automated. The development of artificial intelligence technology and the growing volume of data are additional growth drivers for the company. Within the framework of this article, an analytical review of the practices of applying the pricing system in retail of Russian companies was conducted. The pricing process is key, which allows you to directly influence the main economic indicators of the company’s activities, since sales volume, profit, demand for products, inventories of inventory resources can be regulated by means of competent changes in prices for goods. Dynamic pricing is gaining great popularity among retailers, which leads to the expansion of patent activities in terms of software and models for automating pricing, including using machine learning. In this article, the authors reviewed some of the most popular offers on the market for the introduction of dynamic pricing systems and analyzed them, revealed the main features and identified common features of building automated dynamic pricing models. The practices of implementing dynamic pricing systems in Russian companies were also investigated. The results obtained allow us to draw conclusions about the importance of using automated pricing systems in retail in modern conditions, their capabilities and application problems.

An extension analysis of Amihud's illiquidity premium: Evidence from the Taiwan stock market

This study proposes a novel illiquidity measure that excludes overnight returns and incorporates the recency effect, and investigates whether this new metric more effectively captures the illiquidity premium in the Taiwan stock market relative to Amihud's (2002) measure and other illiquidity measures that exclude overnight returns or account for the recency effect. Cross-sectional and time-series asset pricing tests reveal that our measure dominates other illiquidity measures, yielding a more profitable long–short strategy. Vector autoregressive analysis confirms these results for the aggregate market. Our study contributes to the measurement of liquidity in emerging markets and suggests potential improvements for portfolio management and asset pricing models.

Pricing Decisions in Dual-Channel Supply Chains Considering the Offline Channel Preference and Service Level

With the rapid development of e-commerce, the online channels encroaching on the offline sales market are becoming more serious, which will definitely harm the offline market. Moreover, there exists a certain percentage of consumers (mostly elderly people) who are not able to purchase online because they lack digital skills. Therefore, understanding the impact of the purchase channel preference and service level on pricing decisions is vital for the dual-channel supply chain management. Focusing on the channel preference and service level, we first develop an optimal pricing model containing centralized and decentralized decision-making for an online and offline retailer by deploying the Stackelberg game. We first develop a Stackleberg game to capture such a dual-channel supply chain with the offline channel preference and service level. Secondly, under centralized decision-making, we derive the optimal retail prices and obtain the optimal total profit. Thirdly, under decentralized decision-making, we obtain the optimal retail prices and optimal total profit as well. Moreover, extensive monotonicity properties when system parameters change are obtained. Relying on the theoretical results, firstly, we show that the improvement of the offline service level would lead to higher pricing of the commodities for both online and offline channels. From our numerical results, when the service level is improved, the offline and online optimal pricing increases by 47.5% and 31.1%, respectively, which may contradict the conventional belief that the improvement of one channel would harm another one. Secondly, we demonstrate that the benefit of improving the offline service level has a diminishing marginal effect. The numerical results show that when the current service level is low, the effectives of improving the service level is roughly five times that when the service level is high. This indicates that the investment in improving the offline service level should not be unlimited. Thirdly, we show that the pricing decision under centralized decision-making should be adopted with the existence of both the offline channel preference and offline service.

On the influence of conventional and automated market makers on market quality in cryptoeconomic systems

Decentralized exchanges (DEXs) have become an alternative to centralized exchanges (CEXs) for trading assets in the form of tokens in cryptoeconomic system markets. The emergence of DEXs is strongly driven by their potential to tackle challenges for market quality originating from CEXs by design, such as opaque market-making strategies and centralization of power. However, it remains unclear to what extent DEXs can enhance market quality compared to CEXs. A core reason for this is the lack of an analysis concept for investigating influences of market makers, including automated market makers (AMMs) used in DEXs and conventional market makers used in CEXs, on market quality in cryptoeconomic systems. To better understand influences of market makers on market quality in cryptoeconomic systems, we developed an analysis concept based on our formal price model grounded in established concepts of market microstructure. We demonstrate the usefulness of the analysis concept by examining conventional market makers on CEXs (i.e., Binance and Coinbase) and automated market makers (AMMs) on DEXs (i.e., Uniswap v2 and Uniswap v3). The main purpose of this work is to support the analysis of influences of different market makers on market quality in cryptoeconomic systems. This is useful to better understand how cryptoeconomic systems can ensure high market quality and safeguard market participants, when issuing tokens.

Techno-Economic Assessment of Industrial Vanadium Flow Batteries Based on Experimental Data

This work presents a techno-economic model based on experimental and market data to provide forecasts of the profitability of vanadium flow batteries (VFBs), which are emerging as a promising technology for specific stationary energy services [1‒2]. Each component affecting the capital and operative costs was analyzed and the impact of side phenomena on capacity losses was considered. Relevant economic parameters were taken from real market data: discount rate was calculated based on the Capital Asset Pricing Model (CAPM) to account for real market conditions; the electricity price follows an arbitrage strategy to profit of the daily price fluctuations taken from the historical data of the Italian energy market [3‒4]. Technical parameters are taken from large-area multi-cell stack, rather than from small single cell experiments, thus allow to characterize the behavior of real industrial reactors [5‒6]. The economic performance indicators obtained are the capital cost, the operative cost, the Levelized Cost of Storage (LCOS) and the Net Present Value (NPV). The resulting values are: a discount rate of 9.12% and electricity prices of 200 € MWh–1 in selling and of 100 € MWh–1 in buying. Capital costs and LCOS were calculated for different system power and energy (E/P) ratings. At E/P = 2 h, the values of capital costs and LCOS were in the range of 800 – 900 € kWh–1 and 0.50 – 0.55 € kWh–1, respectively, whereas at E/P = 10 h they reduced to 350 – 380 € kWh–1 and 0.29 – 0.32 € kWh–1, respectively. In addition, a NPV analysis was carried out in order to evaluate whether these capital costs could provide a profit. The analysis suggests that only at capital cost < 105 € kWh–1 the break-even point (NPV = 0) was reached. The result is that the assumed techno-economic scenario VFBs were not profitable for every considered E/P. A perspective analysis was developed to reveal when VFBs can become profitable. Different screenings were made on both technical and economic parameters. At a technical level, a power density of 0.25 W cm–2, a RTE = 85%, a SOC = 90%, and a reduced number of regenerating processes in the lifespan (Nreg = 4) were considered. Regarding the economic parameters, a discount rate of 7 % was assumed, an electricity selling price of 250 € MWh–1 and a purchasing price of 75 € MWh–1 were used, compatible with a more pronounced Duck Curve of the daily electricity price, induced by the expansion of renewable energy sources expected in the near future. Under this scenario, the system costs decreased considerably: at E/P = 2 h, the Capital Costs and LCOS ranged as 530 – 570 € kWh–1 and 0.25 – 0.27 € kWh–1, respectively, whereas at E/P = 10 h, the Capital Costs and LCOS ranges as 260 – 270 € kWh–1 and around 0.17 € kWh–1, respectively. The latter figures made VFBs profitable for E/P in the range of 4 – 10 hours, as shown in the figure below.

Information disclosure strategies of live‐streaming supply chains in the digi‐economy era

AbstractLive e‐commerce is one of the important forms of digital transformation of supply chain, which has become increasingly popular in recent years as a powerful tool for internet advertising, interaction, and merchandising. We propose two pricing models to examine the strategies of information disclosure and pricing within the live‐streaming supply chain. One model revolves around the agency framework, wherein the manufacturer determines the price while the streamer decides the level of information disclosure. The other model pertains to a self‐implementing framework, wherein the manufacturer determines both the level of information disclosure and the price. To evaluate the robustness of our findings, we extend our models to encompass three significant scenarios: (i) the platform involvement model, where a live‐streaming support platform becomes part of the supply chain; (ii) the fixed price model, which externalizes the manufacturer's pricing decision; and (iii) the dual‐channel operations model. Our analysis reveals that the cost associated with information disclosure impacts the streamer's profits in a nonlinear manner within the agency model. Interestingly, expanding the revenue‐sharing parameter does not invariably favor the manufacturer. Moreover, we observe that none of the live‐streaming sales models exhibits a clear advantage over the others. Additionally, we demonstrate that the platform's involvement limits the manufacturer's ability to utilize the agency model for live‐streaming sales. In situations with fixed pricing, the manufacturer is advised to disclose more production information during live‐streaming sales.

Dynamic pricing for perishable product in the presence of low-carbon preference

ABSTRACT As countries worldwide strive to implement energy conservation and emission reduction measures, modeling consumers’ low-carbon preferences and purchasing decisions has become a crucial factor in implementing dynamic pricing strategies for firms. This paper investigates the dynamic pricing problems for a single perishable product and substitutable perishable products, respectively, where consumer is affected by low-carbon preference. We first propose the decision framework of consumer choice with low-carbon preference based on the multinomial logit model. On this basis, we establish dynamic pricing models by the dynamic programming method and obtain the optimal pricing strategies. We analytically show that consumer’s low-carbon preference has a positive effect on the optimal price for a single perishable product. Meanwhile, numerical examples illustrate that consumer’s low-carbon preference has a positive effect on the optimal prices for substitutable perishable products. Finally, we give some suggestions to increase firm’s revenue when consumer’s low-carbon preference is present.

Analyzing the Risk-Return Tradeoff in Commercial Banks' Common Stock: A Case Study of Nepalese Banks

This study investigates the risk-return tradeoff in the common stock of Nepalese commercial banks over a five-year period from 2018/19 to 2022/23. By analyzing financial data from four prominent banks listed on the Nepal Stock Exchange (NEPSE) i.e. Global IME Bank Limited (GBIME), Nepal Investment Mega Bank (NIMB), Nabil Bank Limited (NABIL), and Kumari Bank Limited (KBL). The study depends on the closing prices of these four commercial banks, which were generously provided through the NEPSE Index and annual general meeting (AGM) reports of each respective bank. The research aims to provide a comprehensive understanding of their risk profiles and expected returns using the Capital Asset Pricing Model (CAPM). The study employs descriptive statistics like mean, standard deviation, coefficient of variation, and maximum/minimum returns. The findings reveal significant variations in performance and risk levels: GBIME and KBL show positive average returns with high volatility, whereas NIMB and NABIL exhibit negative average returns, indicating losses. GBIME’s stock is slightly less volatile than the market, while NIMB’s low beta suggests it is suitable for conservative investors. NABIL’s and KBL’s higher betas indicate more volatility, appealing to high-risk tolerant investors. The study underscores the importance of portfolio diversification and continuous monitoring of market conditions for informed investment decisions. These insights are valuable for investors, financial regulators, and policymakers in developing effective risk management strategies and enhancing financial stability in Nepal's banking sector.

Analysis of the Prices of Recycling Byproducts Obtained from Mechanical–Biological Treatment Plants in the Valencian Community (Spain)

Municipal solid waste (MSW) management in Spain, particularly in the Valencian Community, heavily relies on mechanical–biological treatment (MBT) plants followed by landfill disposal. These MBT facilities utilize mechanical processes like shredding, screening, and sorting to segregate recyclables (metals, plastics, paper) from organic material and other nonrecyclables. While public funding supports these plants, private entities manage them through complex, long-term concession contracts. This structure restricts access to crucial data on the sale prices of the byproducts generated during MBT. Publicly available information on relevant company and administration websites is typically absent, hindering transparency surrounding byproduct revenue. This study addresses this gap by analyzing 2012’s available data on revenues obtained from byproduct sales following mechanical treatment at MBT plants within the Valencian Community and comparing them with Spanish national data. This research revealed a significant finding—the statistical distribution of average prices obtained from Ecoembes auctions in the Valencian Community mirrored the corresponding distribution for prices calculated from auctions conducted in other Spanish regions. This suggests a potential uniformity in byproduct pricing across the country. It has also been found that none of the analyzed price distributions exhibited a normal (Gaussian) distribution. The findings also highlight the need for alternative pricing models that move beyond simple averages and account for regional variations and outliers. As actual prices are not available after 2012, this lack of transparency poses a challenge in comprehensively evaluating the economic viability of MBT plants. Furthermore, it raises concerns regarding whether the revenue generated from byproduct sales reflects fair market value. Limited public access to this information can potentially indicate conflicts of interest or inefficiencies within the waste management system.

Do optimistic portfolios outperform pessimistic portfolios: Evidence from textual sentiment

We examine whether textual sentiment (from news and social media) explains the cross-section of stock returns. Sentiment scores are used to sort stocks into tercile portfolios daily. Various asset pricing models, including CAPM and Fama-French models, are used to assess the sentiment's impact on returns after accounting for traditional risk factors. Results show that portfolios with higher (optimistic) sentiment consistently yield better average returns across the two sentiment measures and the different asset pricing models.

A game theoretic approach for tradable white certificates regarding energy rebound and government intervention

In recent decades, many government administrations have tracked energy efficiency programs (EEPs) against environmental concerns. This was done so that it could potentially be useful as a supportive mechanism for smart technologies applied under the smart city concept. To facilitate this, tradable white certificates (TWCs) have been implemented as popular financial instruments used by energy-intensive sectors to boost cleaner production. In this study, we address an industrial EEP development with a TWC instrument as a multi-agent problem. We study this problem for the first time in the context of a supply chain that includes a manufacturer, an energy producer, and household energy consumers. Furthermore, we explore a new monopolistic pricing model for energy services and energy-efficient products, regarding the rebound effect, energy consumption, and social welfare. Additionally, we discuss two revenue-cost-sharing contracts and compare them as contracts using a comprehensive parametric and experimental analysis. The results show that the second proposed contract has some advantages over the first one. However, the second contract leads to less production than the first one does, while at the same time leading to less social welfare. Also, the findings suggest that the second investigated contract is a more appropriate instrument for the obligated parties than the first one when the aim is to improve the performance of TWC schemes. These findings can provide better circumstances for governance to optimize the critical parameters’ level on TWC schemes with the lowest analytical cost.Graphical abstract

A common shock model for multidimensional electricity intraday price modelling with application to battery valuation

In this paper, we propose a multidimensional statistical model of intraday electricity prices at the scale of the trading session, which allows all products to be simulated simultaneously. This model, based on Poisson measures and inspired by the Common Shock Poisson Model, reproduces the Samuelson effect (intensity and volatility increases as time to maturity decreases). It also reproduces the price correlation structure, highlighted here in the data, which decreases as two maturities move apart. This model has only three parameters that can be estimated using a moment method that we propose here. We demonstrate the usefulness of the model on a case of storage valuation by dynamic programming over a trading session.

Stock Making Investment Decisions Using the Capital Asset Pricing Model (CAPM) Analysis of the Business Index-27 on the Indonesian Stock Exchange

The purpose of this study is to measure the ability of the Capital Asset Princing Model (CPAM) in analyzing investment decision making by predicting the risk and return that will be obtained by investors and helping investors in choosing efficient and inefficient stocks. CAPM is a measuring tool that can be used to determine the level of risk and return obtained and evaluate the rate of return on investment. The purposive sampling technique is used in selecting samples to be used in the study, namely companies listed on the Indonesia Stock Exchange and their shares are consistently included in the Bisnis-27 stock index. The stock criteria used in selecting efficient stocks, namely when individual return results exceed the expected return. The results of this study indicate that there are 8 stocks that meet the efficiency criteria, namely ADRP, AKRA, AMRT, BBNI, BMRI, INKP, JSMR, and PGAS with individual returns results exceed the expected returns. In investment decisions, stocks that are included in these efficient stocks are the priority stocks that investors should buy. Based on the analysis, there is a non-linear relationship between systematic risk and expected stock returns, making an important contribution to investment decision making in the Indonesian stock market.

DataPrice: An Interactive System for Pricing Datasets in Data Marketplaces

With the flourishing of data-driven applications, data marketplaces, which can dramatically facilitate data utilization, have emerged recently. However, determining the appropriate price for datasets presents a significant challenge due to the intangible nature of data. DataPrice alleviates the challenge by providing an interactive system based on a pricing model trained on real datasets collected from commercial data marketplaces. The pricing model can estimate an appropriate price according to the dataset description as a reference for both data sellers and data buyers. By leveraging Shapley values to evaluate the contribution of each attribute in metadata on the estimated price, DataPrice offers an explanation of the pricing process in a text form to enhance user trust.

An endogenous evolution mechanism model of asset prices based on time-varying risk aversion coefficient

In the traditional heterogeneous agent model, investors are assumed to be risk averse, and the wealth expected utility function maximization principle is used to form the optimal asset quantity demand. In such models, the risk aversion coefficient of investors is often assumed to be constant. This paper considers that the risk aversion coefficient of investors is time-varying and changes with the change of wealth, and establishes an endogenous evolutionary mechanism model formed by fundamental analysts, technical analysts, and market makers. Compared with the fixed risk aversion coefficient model, this paper analyzes the investor’s behavior, the interaction between investor behaviors, and the influence of different types of investors on the stability of the market. At the same time, we test asset price and asset behavior and conclude that investor behavior affects the stability of the system model. The numerical simulation of the corresponding stochastic model shows that the model can simulate the basic characteristics of financial time series, such as the partial peak and thick tail of asset return series, and the long memory of fluctuations.

Valuation of Medical Innovation Handling with Uncertainty and Risk.

Background: The purpose of this paper is to address how to handle uncertainty when performing an economic valuation of a medical innovation R&D project in orphan diseases from the perspective of the investor. Methods: We describe the specific uncertainty related to cash flows and the cost of capital for innovation in orphan diseases. The uncertainty in cash flows relates to sales, manufacturing and R&D costs, and probabilities of failure for each phase in the clinical trial program. We consider different net present values (NPVs) and higher standard deviations for orphan drugs compared to non-orphan drugs. Results: Numerical case base examples showed the differences in trade-off by an investor for R&D projects with differences in NPV and level of uncertainty. The investor will transfer the additional uncertainty in cash flows in a higher cost of capital. An alternative approach is the application of an "acceptability curve" based on a probabilistic sensitivity analysis, which displays the cumulative probabilities at a range of different values for the NPV. Finally, we consider uncertainty in the cost of capital itself by applying the Capital Asset Pricing Model (CAPM). Conclusions: In this paper, we described various types of uncertainty and explored various approaches to how to handle uncertainty in the economic valuation of medical innovation in orphan diseases. The bridging of health economics with economic valuation theory in the healthcare market is to our knowledge a novel approach for the valuation of medical innovation by investors.