Pricing Model Research Articles

Economic policy uncertainty and the predictability of stock returns: impact of China in Asia

PurposeMotivated by the significant role of uncertainty in affecting investment decisions and China's economic leadership in Asia, this paper investigates the predictive role of exposure to Chinese economic policy uncertainty at the individual stock level in large Asian markets.Design/methodology/approachWe estimate the monthly uncertainty exposure (beta) for each stock and then employ the portfolio-level sorting analysis to investigate the relationship between the China’s uncertainty exposure and the future returns of major Asian markets over multiple trading horizons. The raw returns of the high-minus-low portfolios are then adjusted using conventional asset pricing models to investigate whether the relationship is explained by common risk factors. Finally, we check the robustness of the portfolio-level results through firm-level Fama and MacBeth (1973) regressions.FindingsApplying portfolio-level sorting analysis, we reveal that exposure to Chinese uncertainty is negatively related to the future returns of large stocks over multiple trading horizons in Japan, Hong Kong and India. We discover this is unexplained by common risk factors, including market, size, value, profitability, investment and momentum, and is robust to the specification of stock-level Fama and MacBeth (1973) regressions.Research limitations/implicationsOur analysis demonstrates the spillover effects of Chinese economic policy uncertainty across the region, provides evidence of China's emerging economic leadership, and offers trading strategies for managing uncertainty risks.Originality/valueThe findings of the study significantly improve our understanding of stock return predictability in Asian markets. Unlike previous studies, our results challenge the leading role of the US by providing a new intra-regional return predictor, namely, China’s uncertainty exposure. These results also evidence the continuing integration of the Asian economy and financial markets. However, contrary findings for some Asian markets point toward certain market-specific features. Compared with market-level research, our analysis provides deeper insights into the performance of individual stocks and is of particular importance to investors and other market participants.

A Multifrequency Data Fusion Deep Learning Model for Carbon Price Prediction

ABSTRACTIn response to the global need for effective management of carbon emissions and alignment with sustainable development goals, predicting carbon trading prices accurately is critical. This study introduces a multifrequency data fusion carbon price prediction model (MFF‐CPPM), addressing the nonlinear characteristics of carbon trading prices and inconsistent feature factor frequencies. The MFF‐CPPM consists of a feature‐extraction frontend, a multifrequency data fusion transformer, and a fusion regression layer, offering a novel methodological approach in forecasting studies. The model's validity was tested in Guangdong, China's largest carbon trading pilot market. The results demonstrated that the MFF‐CPPM outperformed baseline models in terms of carbon price‐prediction accuracy and trend forecasting. Additional trials conducted in Hubei and Beijing confirmed the model's robustness and generalization capabilities, providing valuable evidence of its effectiveness and reliability across varying market contexts. This study presents a novel predictive model for carbon trading prices, with a unique capability to harness data at differing frequencies. The MFF‐CPPM not only enhances forecasting accuracy but also offers an innovative approach to effectively incorporate multifrequency information. This advancement paves the way for flexible forecasting models in any scenario where data arrive at differing frequencies.

Modelling of Claim and Pricing of Motor Insurance Based on Bonus-Malus System Considering the Frequency and Severity of Claims

This article proposes new distributions for claim frequency and severity, specifically tailored for a bonus-malus system in automobile insurance. The mixed Poisson with weighted quasi Lindley distribution is recommended for modeling claim frequency, while the mixed exponential with weighted quasi Lindley distribution is suggested for modeling claim severity. To estimate insurance premiums, the Bayesian method is employed, incorporating both frequency and severity distributions. The study validates the proposed models using real data from an Australian insurance company, which includes 67,856 policies. The assessment of model adequacy indicates that the Poisson-weighted quasi Lindley distribution is a suitable fit for modeling claim frequency, while the exponential-weighted quasi Lindley distribution is appropriate for modeling claim severity. Overall, the results suggest that the proposed models offer optimal premium estimations, considering both claim frequency and severity, which can lead to fairer pricing and increased customer appeal during claim occurrences compared to conventional models.

An equilibrium model of reinsurance pricing

Reinsurance is the insurance of insurance companies. We develop a dynamic equilibrium model for reinsurance pricing based on the supply and demand of reinsurance. In the model, an insurance company and a reinsurance company make decisions on the optimal reinsurance policy as a demander and a supplier of reinsurance, respectively. The reinsurance price is determined when the demand matches the supply. We incorporate the proportional reinsurance policy as well as investment opportunities into the framework. The high flexibility of this equilibrium pricing model paves a new way for studies of optimal reinsurance and reinsurance pricing.

Ambiguity, information processing, and financial intermediation

This paper incorporates ambiguity and information processing constraints into the He and Krishnamurthy (2012) model of intermediary asset pricing. Financial intermediaries possess greater information processing capacity than households. In response, households optimally choose to delegate their investment decisions. The contractual relationship between households and intermediaries is subject to a moral hazard friction, which results in a financial constraint. We show that ambiguity aversion not only amplifies households' incentives to delegate but also tightens the financial constraint. The calibrated model can quantitatively explain both the unconditional and time-varying moments of observed asset prices while endogenously generating an empirically consistent crisis frequency.

Drilling Deeper: Non-Linear, Non-Parametric Natural Gas Price and Volatility Forecasting

This paper studies the forecast accuracy and explainability of a battery of dayahead (Henry Hub and Title Transfer Facility (TTF)) natural gas price and volatility models. The results demonstrate the dominance of non-linear, non-parametric models with deep structure relative to various competing model specifications. By employing the explainable artificial intelligence (XAI) approach, we document that the price of natural gas is formed strategically based on crude oil and electricity prices. While the conditional volatility of natural gas returns is driven by long-memory dynamics and crude oil volatility, the informativeness of the electricity predictor has improved over the most recent volatile time period. Although we reveal that predictive non-linear relationships are inherently complex and time-varying, our findings in general support the notion that natural gas, crude oil and electricity are interconnected. Focusing on the periods when markets experienced sharp structural breaks and extreme volatility (e.g., the COVID-19 pandemic and the Russia-Ukraine conflict), we show that deep learning models provide better adaptability and lead to significantly more accurate forecast performance.

Stock market responses to economic sanctions: Evaluating the roles of national reserves and financial market access

Economic sanctions significantly influence stock market capitalization and have critical implications for the national economy. This study investigates the impact of economic sanctions on stock market capitalization, focusing on the roles of national reserves and financial market access in mitigating these effects, utilizing a sample of 87 countries from 2000 to 2021 and employing a two-step system generalized method of moments (GMM) approach to analyze panel data. The findings reveal that economic sanctions, including trade and financial sanctions, significantly reduce stock market capitalization. However, this negative impact diminishes over time as markets adapt. Moreover, higher national reserves and enhanced financial market accessibility can mitigate the adverse effects of sanctions. Case studies in Russia, Iran, China, and Ukraine further validate these results. This study refines economic models, such as the capital asset pricing model (CAPM) and country risk model (CRM), by including sanction intensity, national reserves, and financial market accessibility, offering valuable insights for policymakers and investors dealing with international sanctions.

Higher order expectations, learning, and sentiment pricing dynamics

We present a dynamic asset pricing model combining individual investor sentiment, higher order expectations with learning. In the basic model, the forward-looking expectation of individual investors is distorted by individual sentiment and higher order expectations, so prices react more sluggishly to changes in fundamentals of the asset. We find that investor sentiment plays a significant role in the effect of higher order expectations on asset pricing. Investor sentiment not only makes the price tightly anchor to the initial price, but also increases the sentiment drift of the price. Higher order expectations exhibit inertia, therefore aggravating the anchor to the initial price. With the increase of the order, more and more investor sentiment is integrated into the prices, amplifying the bias of pubic signal relative to fundamentals. When individual sentiment investors learn valuable public information through price system in the long term, the information component of the equilibrium price increases, thus drawing the asset price back toward the rational expected value. The model could offer a partial explanation to the inertia and drift in the price path

Winning Seats: Contingency Selling in Markets with Limited Capacity and Diverse Preferences

This paper examines an emerging pricing model, contingency selling, for markets where the product is sold in advance with attribute uncertainty and capacity constraint. A typical example is tournament ticket sales for sports events where it is uncertain whether a particular team will appear in the game. The model enables the firm to sell tickets in a contingent fashion, i.e., some tickets will be valid only if a certain team gets into the game. When consumers (fans) have sufficiently different preferences for different products (games with different teams), selling these “contingent tickets” better matches the price with the willingness-to-pay of particular consumer segments (fans of specific teams). Exploiting state-based price discrimination and belief heterogeneity, contingent tickets generate a higher margin per seat than the traditional way of selling general tickets, especially when there are supply constraints. We further show that contingency selling continues to be advantageous in the presence of secondary markets where consumers purchase tickets in advance but can trade tickets once they know which team will be in the game. We extend our model to examine the impacts of consumer optimism bias and fan base asymmetry. Finally, we compare contingency selling with alternative selling mechanisms to demonstrate the robustness of our results and illustrate the situations where this new selling model is more (or less) advantageous.

Hedging irrigated maize crop yields using temperature derivatives in Malawi

Agriculture production yield varies with weather changes. This causes farmers incur loses. For instance, extreme temperature leads to low maize yield. This study describes incomplete temperature weather derivatives in agriculture markets and applies risk management hedging techniques. It focuses on hedging crop yield against extreme temperatures during irrigation, farming which is done without greenhouse. This study's primary goal is to hedge irrigated maize crop yields using temperature derivatives. This is achieved firstly by modelling a daily average temperature stochastical model. Then, deriving statistical properties of the model based on 1990–2020 Kasungu District Temperature historical data. Lastly, pricing temperature derivatives to hedge maize crop yield. This study uses 1990–2020 Kasungu District Temperature historical data. Then a stochastically Ornstein-Uhlenbeck process with the time-varying speed of reversion, seasonal mean, and local volatility that depends on the local average temperature was proposed. Based on the average temperature model, down and output barrier option pricing models for average temperature and growing degree day (GDD) are applied. The study results shows that when the GDD is above the barrier level the barrier option does not knock out. Hence gives the holder the right to buy the underlying asset since it does not reach or fall below a predefined level over the option's lifetime. If the GDD does not exceed the barrier level, then the farmer will not have to pay the premium because the option is invalid. On the same note, the farmer will have to exercise his right by paying the premium of calculated premium when the GDD exceeds the barrier level. In return for this, the farmer gets paid off which happens to be the difference between GDD and barrier multiplied $1. In line with Malawi's 2063 Millennium Development Goals (MDGs), this study acts as an eye opener for the government to put a policy on whether derivatives should be practised in our country hence, increase cash holding by improving the situation of the farmer and country.

Which factors in China? A pre-registered report

We evaluate the ability of the asset pricing models of Liu, Stambaugh and Yuan (2019) and Fama and French (2018) to price Chinese stocks. Our model comparisons are conducted using spanning tests following Barillas and Shanken (2017) and maximum squared Sharpe ratios following Barillas, Kan, Robotti and Shanken (2020). We will first examine the models of Liu et al. (2019) and Fama and French (2018) in isolation. Next, we will conduct horse race tests between the models. The superior model will be determined based on spanning regressions and a comparison of the maximum squared Sharpe ratios derived from the model's factors.

Stakeholder perceptions of issues and possible solutions to access to oncology medicines: a case study of multi-stakeholder engagement

Aim This case study reports on a series of multi-stakeholder dialogues on challenges to access to oncology medicines in both the public and private sectors. Methods Key stakeholders, identified from various points in the pharmaceutical supply chain, were invited to attend a series of virtual discussion sessions to address specific medicines access challenges to the provision of cancer care in South Africa. For each session, a case scenario, based on a real world situation/event was presented for discussion, and viable solutions and areas requiring further research or legislative changes were identified. Topics covered in these discussions included registration pathways and obstacles; limitations of current medicine pricing policies; alternate reimbursement models; funders’ initiatives to ensure access to oncology medicines; and essential medicine selection and procurement in the public sector. Findings The key issues raised in the discussions included: non-transparency in the approval and pricing of unregistered medicines accessed in terms of section 21 of the Medicines and Related Substances Act of 1965 (as amended); commercial decisions related to the small market for oncology medicines in South Africa; inequities between public and private sector access; alternate forms of price negotiation in the private sector; the restrictions imposed by the single exit price model; and the role of co-payments for those medicines not included as prescribed minimum benefits. Conclusions There is a clear need for a forum where all stakeholders and actors in this field can engage in discussions to find solutions to the oncology medicines access conundrum, including the development of legislative amendments.

Energy shocks as Keynesian supply shocks: Implications for fiscal policy

This paper analyses the economic impact of and the optimal policy response to energy supply shocks in a flexible price model with heterogeneous households. We introduce energy as a consumption good on the demand side and as an input to production on the supply side. A distinguishing feature is that, in line with empirical evidence, we allow households’ energy demand to be non-homothetic. The model provides three main insights. First, (negative) energy supply shocks act as a (negative) demand shock, or Keynesian supply shock, when two conditions are met: On the demand side household income inequality needs to be large, while on the supply side, the price elasticity of consumption goods needs to be high. Second, the social planner can implement the first-best allocation by subsidising firms and poor households while taxing rich households. Energy shocks then act as standard supply shocks. Last, issuing public debt can help implement the first best allocation when energy shocks are large and/or the economy’s overall energy intensity is low.

Stock price delay and the cross-section of expected returns: A story of night and day

We examine how individual stock price reacts to intraday and overnight market information. By examining stock betas, we document a stark gap between day beta and night beta and nontrivial asynchronous beta, which captures the slow diffusion of information. We provide evidence that this information delay can predict future stock returns over a one-month period. Long-short portfolios sorted on the gap between day beta and night beta and asynchronous beta generate raw returns of 0.8% and 0.39% and risk-adjusted alphas of 0.77% and 0.28% per month. These results are robust to alternative asset pricing models and when controlling for firm characteristics, such as size, book-to-market ratios, liquidity, investor recognition and limits-to-arbitrage characteristics. We also explore the heterogeneity of the information delay premium and find that the predictive power of information delay for future return is stronger among small, value firms, less visible firms, illiquid firms and firms with higher limit-to-arbitrage. We also provide evidence that our measure of price delay indeed offers incremental information for cross-section return prediction after we explicitly control for the conventional measure of Hou and Moskowitz (2005)’s price delay.

Pricing Intellectual Capital in Asset Pricing Models: A study on Shanghai Stock Exchange

This study aims to examine the validity of intellectual capital as a priced risk factor in the asset pricing model. We have used the data of companies listed on the Shanghai Stock Exchange from 2000 to 2021. To test the models, time series as well as cross sectional analysis have been done using the Fama-MacBeth method. The results show that intellectual capital is a significant risk factor and after augmenting IC into asset pricing models improves the explanatory power to explain the variation in the stock returns. It further suggests that IC can be priced in the Chinese market, over and above what can be explained by covariance risk, size, value, investment and profitability factors.

The influence of urban green and recreational areas on the price of housing in Zagreb

Urban green and recreational spaces are important for residents and one of the foundations for the functioningof cities. They offer numerous benefits, including the influence on the price of real estate as an economic benefit.In this paper, the influence of green and recreational areas in Zagreb on the price of apartments in residential buildings is analysed using the hedonic pricing model. The data for the study was processed and analysed using a geographic information system. A local hedonic price model was developed. The results show that some of the observed green and recreational areas increase the price of housing due to their proximity, while others decrease it. Some areas have no influence on the price of housing. The influence of the observed areas on housing prices was heterogeneous. Spatial differences were also found in the influence of the observed areas on housing prices. The most attractive factors when choosing a place to live are Jarun and the parks in the city centre. The price of apartments that near the Jarun increases by 41 EUR/m2 if the apartment is 100 m closer to the Jarun. The price of the apartment increases by 91 EUR/m2 the closer the apartment is to the parks in the city centre (per 100 m). Apartments near Jarun and the parks in the city centre are not available for residents with lower socio-economic status. The forest areas, Maksimir Park and the banks of the Sava river are not favoured when choosing a place to live. Neighbourhoods near these areas are available to the lower socioeconomic status population. Bundek raises the price of one part of the apartments and lowers the price of the other. The results of this study can contribute to the study of green gentrification in Zagreb, but further research is needed on socio-economic indicators and other changes in the region.

Pricing balancing ancillary services for low-inertia power systems under uncertainty and nonconvexity

In renewable-rich power systems, declining rotational inertia and unpredictable power fluctuations make the system vulnerable to contingencies. Recently, this issue has garnered significant attention in practice and academia, aiming to enhance power system reliability through market mechanisms. This paper proposes a day-ahead joint market that integrates energy, reserve, inertia, and frequency response services. A frequency-constrained unit commitment (UC) model is developed employing a distributionally robust chance-constrained (DRCC) approach to account for the uncertainties in wind farm outputs. Additionally, a convex hull pricing (CHP) model is introduced to minimize uplift payments associated with the nonconvex nature of commitment variables. Case studies conducted on a 5-bus and a 118-bus power system demonstrate the efficacy of the proposed market design in reducing operational costs and promoting renewable energy utilization. Furthermore, the results highlight the effectiveness of the CHP model in minimizing the uplifts and enhancing market transparency.

Analysis of the Application of the Capital Asset Pricing Model (CAPM) Method in Making Islamic Stock Investment Decisions

This study aims to analyze the application of the Capital Asset Pricing Model (CAPM) in sharia stock investment decision making, especially JII70. The research design used is a quantitative approach. The type of research used is descriptive research. This research leads to the shares of companies included in the Jakarta Islamic Index 70 (JII70) on the Indonesia Stock Exchange. The population in this study were all shares of companies listed on the Jakarta Islamic Index 70 (JII70) in the period January 2019-January 2024. The research sample was 32 JII70 stock issuers who consistently entered JII70 during the period January 2019-January 2024. The results showed that there were 14 issuers of JII70 stocks in the efficient category because they had individual stock returns greater than the expected rate of return [Ri > E(Ri)] with positive individual returns.

Effect of Transfer Pricing on Corporation Tax Compliance Among Multinational Corporations in Nairobi Kenya

Purpose: Tax is a compulsory contribution that is made by the citizens of a country to the government for the provision of public utilities. It is evident from the National Tax Policy, 2024 that the Kenya Revenue Authority (KRA) and the government at large have recently shown a lot of interest in multinationals due to the complexity of their operations and the tendency for them to come up with complex tax avoidance mechanisms. This has also been complicated by the widespread abuse of transfer pricing models adopted when dealing with multinational corporations operating in Kenya. This study looked into effect of transfer pricing on corporate tax compliance among multinational corporation companies in Kenya. The study was guided by the following theories, AS theory, the Slippery Slope Framework, and the theory of the planned behavior. Methodology: The study employed an explanatory research design. The target population was 221 multinational corporations’ companies and a sample size of 142 respondents. Questionnaires were used to collect primary data and analysis was descriptive statistics include frequency, percentage, and mean while inferential statistics included correlation and multiple regression. Results: The study findings found out most companies in Nairobi, Kenya have transfer pricing policies that are moderately but not adequately in line with domestic tax statutes. Most companies will use transfer pricing as a tool to reduce their tax liabilities. Further, KRA is moderately but not adequately efficient in monitoring and regulating transfer pricing practices in our company. Conclusion: The study results concluded that transfer pricing affects corporation tax compliance Based on the findings, the study recommends there is a need for exchange of information and more international collaborative efforts from a policy, legal, and academic front to address aggressive tax avoidance practices from MNCs. Therefore, future studies can be conducted on the impact of Dispute resolution mechanism on corporation tax performance.

A Comparative Analysis of Microsoft Fabric and Snowflake: Architectural Paradigms and Performance Metrics in Modern Cloud Data Platforms

Abstract: This article presents a comprehensive comparative analysis of two leading cloud data platforms: Microsoft Fabric and Snowflake. As organizations increasingly rely on cloud-based solutions for data management and analytics, understanding the strengths and limitations of these platforms becomes crucial. This article examines the architectural foundations, functional capabilities, performance metrics, and cost considerations of both platforms. We explore Microsoft Fabric's integrated service model within the Azure ecosystem and Snowflake's multi-cluster shared data architecture, assessing their approaches to data integration, storage, analytics, and machine learning integration. Through empirical performance comparisons and an evaluation of scalability mechanisms, we provide insights into the operational efficiencies of each platform. Additionally, we analyze the security features, compliance standards, and pricing models to offer a holistic view of the total cost of ownership. Our findings reveal distinct advantages in Microsoft Fabric's end-to-end integration and Snowflake's performance in multicloud environments, while also highlighting areas for potential improvement in both platforms. This article aims to serve as a valuable resource for organizations navigating the complex landscape of cloud data solutions, offering evidence-based criteria for platform selection aligned with specific business needs and technological ecosystems