Financial Model Research Articles

The Impact of Stochastic Volatility on Option Pricing Using the Black-Scholes Model: Empirical Evidence from NVIDIA

This paper investigates the impact of volatility on option pricing using the Black-Scholes model. Utilizing historical stock and option data from NVIDIA, this study demonstrates the limitations of the Black-Scholes model in capturing dynamic market conditions. The empirical analysis reveals significant discrepancies between model predictions and actual market prices, highlighting the importance of accurate volatility estimation. The findings suggest that incorporating better volatility measures can improve the accuracy of option pricing models. Additionally, the implications of these findings for traders and risk managers are discussed, emphasizing the need for more sophisticated approaches to volatility estimation in financial markets. This study adds to the body of knowledge by presenting actual data regarding the efficiency of different volatility metrics in option pricing. Moreover, it underscores the necessity of continuous improvement in financial modeling techniques to adapt to changing market dynamics. The study's results align with previous research that has shown the limitations of the Black-Scholes model and the benefits of alternative volatility estimation methods. This paper also discusses the potential application of these findings to improving trading strategies and risk management practices. The practical implications are significant, suggesting that traders and risk managers should consider incorporating advanced volatility measures into their models to enhance decision-making processes and financial outcomes. The conclusions drawn from this study highlight the critical role of accurate volatility estimation in achieving more reliable option pricing and underscore the need for ongoing research and innovation in this field.

Analyzing the Influential Factors of Valuation: A Case Study on Taiwan Semiconductor Manufacturing Company

During the Artificial Intelligence (AI) revolution, the semiconductor industry saw unprecedented demand, driven mostly by the need for more powerful computational technology. This study examines the value impact of AI breakthroughs on Taiwan Semiconductor Manufacturing Company (TSMC) using a rigorous Discounted Cash Flow (DCF) analysis. With AI technologies largely driving demand in the semiconductor business, this study adapts classic financial modeling approaches to capture the subtle implications of technical advancements on firm valuation. Using substantial financial data from TSMC, the research modifies growth rates to reflect AI-driven market potential, while also incorporating cautious forecasts to allow for industry volatility. The findings indicate that the market may undervalue TSMC, implying an upbeat forecast for the firm in the middle of the AI boom. The research contributes to financial academic knowledge by illustrating the use of DCF to evaluate technology influences on business values, providing important insights for investors and corporate strategists. Future refinements to the DCF model are proposed, with an emphasis on incorporating dynamic market data and policy effect research to improve investment assessments in technology.

La actividad periodística y los desafíos de su gestión en los Estados Unidos de América y Europa

The digital age has opened the doors to numerous opportunities for publishing organizations and journalists, and has increased the number of challenges, particularly those related to the sustainability of the business model. The management of media companies faces a number of profound and disruptive challenges that even significantly defy the logic of various social media industries. This paper aims to understand the sustainability and challenges of media business models and how managers are reacting and adapting their practices in this industry in the field of digital transformation, and in an increasingly competitive market. Thus, the central research questions of this work are: RQ1. What are the main strategies and management practices applied by journalistic companies to achieve the sustainability of their business model and funding? RQ2. To what extent are the revenue streams of digital newspapers growing and how has the funding model of US newspapers differed from that of European ones? This article is based on 6 interviews (out of a total of 90 from a larger study that included executives from media companies based on all continents) with executives from 3 North American (United States and Canada) and 3 European newspaper companies. (Ireland, UK and France).

A generalization of the Topological Tail Dependence theory: From indices to individual stocks

This study investigates the Topological Tail Dependence (TTD) theory’s applicability to individual stock volatility and high dimensions. Utilizing a comprehensive dataset from the S&P 100, the research employs various methodologies to test the predictions and implications of the TTD theory. The theory’s main prediction of Wasserstein Distance’s predictive utility, particularly in nonlinear models during volatile periods, is confirmed. The research suggests extending the TTD theory’s application to various financial instruments and incorporating dynamic topological features to enhance understanding market dynamics. This study validates the TTD theory for individual stocks and highlights the necessity of topological considerations in financial modeling, promising advancements in financial econometrics and risk management strategies.

The Integrated Care World is a Stage: Applying Goffman's Theory of Dramaturgy to the Activities of Integrated Care.

Among the challenges in delivering integrated health and social care services is the need to attend to the coordination of tasks, roles, activities, and operations, while considering how these efforts are experienced by patients, carers and communities. The literature has noted an important disconnect between how providers and leaders view their efforts to coordinate service delivery, and how patients perceive these efforts on the receiving end. Our team has provided guidance to integrated care efforts in Ontario, Canada by drawing on Goffman's theory of Dramaturgy to help classify the actions of integrated care delivery as linked to the roles individuals play in the delivery of care. Using this framing helps to uncover how "backstage" processes (such as team-functioning, funding models, and digital infrastructures) create a necessary foundation on which "frontstage" actions (or performances) can be effectively delivered.

Efficiency of Healthcare Financing: Case of European Countries

Global turbulence and uncertainty force civil servants and executors to optimise public finance distribution. The COVID-19 pandemic aligned with the necessity of assessing the efficiency of healthcare financing due to its capability in overcoming the negative consequences. The paper analyses the peculiarities of healthcare financing in 34 European countries and points out trends and changes in its structure and dynamics. It also realises cluster analysis to reveal models of healthcare financing and their specific features. Panel data regression analysis was used to assess the efficiency of healthcare financing within each cluster by clarifying the relationship between healthcare expenditures and public health outcome—life expectancy. The distributed lag model was also used to test for time lags between financial inflows in healthcare and its outcome. Empirical results highlight key tips for optimising healthcare financing and creating the benchmark model.

Zakat and Waqf Model in Reducing Poverty among Vulnerable Groups During the Pandemic in Nigeria

The crisis of COVID-19 pandemic has already crushed and affected almost everyone in the world. In terms of economy, the vulnerable group was the most affected group in the society as many of them lose their jobs and thus poverty rate rose. Poverty is a worldwide spread problem that results pains and loss of confidence to many people in the world where vulnerable citizens of Bauchi state-Nigeria are not excluded. The menace of poverty during COVID-19 pandemic can be alleviated through proper Zakat and Waqf administrations. This study explores the possible uses of Zakat and Waqf as financing tools for poverty alleviation during COVID-19 pandemic on the vulnerable in the society. The qualitative approach is adopted which includes review of related literatures and interview with relevant bodies and individuals. The findings of the study reveals that Zakat and Waqf as an Islamic finance models have relevant potential to fight any contemporary crisis and pandemic in respect of geographical location, era, people or religion. Zakat and Waqf if properly organized, maintained, collected, well managed and truthfully distributed to the people concerned can help in fighting the adverse effect of COVID-19 pandemic and alleviate poverty among the vulnerable in Bauchi state Nigeria.

Feasibility of green roofs in the mediterranean region: A stochastic study using a Monte-Carlo financial model

Green roofs, also known as vegetated roofs or living roofs, have gained increasing attention in recent years due to their potential to address various environmental challenges in urban areas. These innovative roofing systems offer a promising solution to mitigate urban heat island effects, manage stormwater runoff, reduce energy consumption, improve air quality, enhance biodiversity, and contribute to cities' sustainability and liveability. This paper presents a comprehensive analysis of the feasibility and sustainability of green roofs in the Mediterranean zone, with an Italian location as a case study. The analysis considers intensive or extensive green roofs, applicable tax incentives, and different cost/benefit considerations. A probabilistic study of the financial model was performed using a Monte-Carlo based method. The results show that green roofs provide private and social benefits and are financially feasible. Critical factors for increasing sustainability and financial feasibility are increment of energy saving potential, long lifetime, and reduction of maintenance. The payback period is sensitive to scenarios where all the benefits become applicable, with regulatory incentives and tax reductions. Policymakers should encourage the application of green roof technology in new buildings and retrofitting old ones.

An analytical study on value chain finance to tomato and mango as a component of high value agriculture in the Eastern Uttar Pradesh of India

The study conducted an extensive field survey utilizing pre-tested interview schedules with tomato and mango growers and chain actors. A multi-stage purposive-cum-stratified random sampling method was employed to select sample units. The findings of this study highlight the cost orientation of tomato and mango production, indicating a greater need for consequential credit. The research revealed that growers of both commodities within fragmented value chains had a higher proportionate share in the value addition (price) than growers in integrated chains, primarily due to direct sales of produce to consumers. Additionally, downstream actors such as commission agents, wholesalers, and retailers received a comparatively higher proportionate share in the value additions (price) compared to upward actors, indicating that producers could not effectively compete with increased demand in the marketplace. Observations within the chain indicated that finance for tomato and mango production circulated through a product commitment relationship established between chain participants. Downstream actors provided financing to upstream actors from either their surplus funds or external finance received from financial institutions. The study suggests developing a producer-driven chain financing model as an alternative to the buyer-driven financing model for tomato and mango production. This could be achieved by promoting farmers' organizations as long-term financing strategies for financing institutions.

Improving the explainability of autoencoder factors for commodities through forecast-based Shapley values

Autoencoders are dimension reduction models in the field of machine learning which can be thought of as a neural network counterpart of principal components analysis (PCA). Due to their flexibility and good performance, autoencoders have been recently used for estimating nonlinear factor models in finance. The main weakness of autoencoders is that the results are less explainable than those obtained with the PCA. In this paper, we propose the adoption of the Shapley value to improve the explainability of autoencoders in the context of nonlinear factor models. In particular, we measure the relevance of nonlinear latent factors using a forecast-based Shapley value approach that measures each latent factor’s contributions in determining the out-of-sample accuracy in factor-augmented models. Considering the interesting empirical instance of the commodity market, we identify the most relevant latent factors for each commodity based on their out-of-sample forecasting ability.

Optimization of Weibull Distribution Parameters with Application to Short-Term Risk Assessment and Strategic Investment Decision-Making

Accurate parameter estimation is fundamental in financial modeling, especially in investment analysis, where the Modified Internal Rate of Return (MIRR) plays a key role in evaluating investment performance. This study aims to enhance risk and return predictions in Sharia-compliant property investments by exploring the efficacy of various optimization techniques for estimating Weibull distribution parameters within the MIRR framework. To achieve this, we employed a comparative analysis of optimization methods, including Simulated Annealing (SA), Differential Evolution (DE), Genetic Algorithm (GA), and traditional Numerical Methods (NM). Performance was assessed through metrics such as Root Mean Squared Error (RMSE), Akaike Information Criterion (AIC), R-squared (R2) values, and Kolmogorov-Smirnov (KS) statistics. The results reveal that metaheuristic algorithms (SA, DE, GA) significantly outperform traditional numerical methods in terms of parameter estimation accuracy. Specifically, SA achieved the lowest RMSE of 0.042, with a Weibull shape parameter estimate of 1.254 and variance of 0.004, followed closely by DE with an RMSE of 0.048, and GA with 0.046. In contrast, NM exhibited a higher RMSE of 0.067, with a shape parameter estimate of 1.310 and a variance of 0.006. The AIC values for metaheuristic methods ranged from 14.25 to 14.68, compared to 15.12 for NM, and R2 values for metaheuristic methods ranged from 0.932 to 0.945, compared to 0.910 for NM. KS statistics further underscored the superior model fit of metaheuristics, with SA showing the lowest KS value of 0.045. The study underscores the critical role of metaheuristic optimization in improving the accuracy of parameter estimation based on MIRR models. This enhancement provides more reliable risk assessments and returns predictions, offering valuable insights for informed investment decision-making and contributing to optimized financial outcomes in the property sector.

Financing a Capital-Constrained Supply Chain under Risk Regulations: Traditional Finance versus Platform Finance

Small- and medium-sized enterprises (SMEs) frequently face challenges in obtaining financial assistance from traditional banks. Platform Supply Chain Finance (PSCF) has emerged as a promising solution for financing issues among SMEs, with an added focus on integrating sustainability aspects. This study focused on a two-tier supply chain as its primary research topic to find strategies to enhance supplier financial viability and improve the efficiency and profitability of the main manufacturing enterprise. In this study, we establish three distinct hypotheses corresponding to the three models involving supplier and manufacturer participation, encompassing parameters such as production batch size, pricing, and supply chain profit. First, it examined financing decisions through the lens of core enterprise-led platform finance. Second, it applied the Stackelberg game theory to investigate financing decisions in three distinct modes: traditional finance, platform internal finance, and external platform finance. Suppliers, manufacturers, and banks can be seen as participants in a Stackelberg game. In this game, suppliers act as leaders, making production and procurement decisions first, while manufacturers and banks act as followers, adjusting their behavior based on the suppliers’ decisions. Finally, it performed a comparative analysis of decisions and supply chain efficiency across these modes. When the risk regulation cost coefficient falls below a certain threshold, suppliers are willing to set up their own PSCF and there is an optimal level of risk regulation effort within the interval (0, 1). We compare platform finance with traditional finance and find that the traditional finance model maximizes profits for suppliers, while the external financing model maximizes profits for manufacturers and the overall supply chain profit. Findings provide insights for platforms, suppliers, manufacturers, and banks to implement optimal financing and channel structures to increase their profits and promote the sustainable development of the financial supply chain. In addition, future research on blockchain platform models would be highly meaningful.

Garar and Maissar are Two Phenomena Prohibited in the Islamic Financing Model, but Accepted in the Conventional

The article describes two phenomena that are widespread in the conventional economy, some of them are legal in non-Islamic countries, and some are illegal even in them, but both phenomena are prohibited in the Islamic financial model, which is based on the moral postulates of Islam. The article shows the economic and social meaning of these religious restrictions. В статье описываются два явления, которые широко распространены в обычной экономике, часть из них законна в неисламских странах, а часть противозаконна даже в них, но оба явления запрещены в исламской финансовой модели, которая базируется на моральных постулатах ислама. В статье показан экономический и социальный смысл этих религиозных ограничений.

Explore the Specific Applications of Mathematical Models in Finance

The financial sector has always played an indispensable role in the world economy. With increasing globalization and market complexity, financial decision-making has become more intricate and laden with risks. In this highly competitive environment, decision-makers need to rely on scientific methods and tools to address various challenges and make informed decisions. By delving into these issues and methods, we can better understand the irreplaceable role of mathematical models in finance and provide valuable insights and guidance to finance professionals and students. The changes and uncertainties in the financial world will always persist, and mathematical models will continue to play a crucial role in this field, aiding in the understanding and management of complex financial challenges.

Random Processes and Their Applications in Financial Mathematics

Random processes are fundamental tools in modern financial mathematics, widely applied in fields such as financial derivatives pricing, risk management, and interest rate models. This paper systematically introduces the basic theory of random processes, including definitions and classifications, Brownian motion and geometric Brownian motion, and stochastic differential equations. Furthermore, the paper explores specific applications of random processes in financial mathematics, focusing on financial derivatives pricing, risk management and portfolio optimization, and applications in interest rate models and credit risk. Through theoretical and empirical research, this paper reveals the significant role and development prospects of random processes in financial mathematics, providing new ideas and methods for risk management and pricing models in financial markets.

Challenges and Considerations in Transitioning from Cobalt-60 to Linear Accelerators for Radiotherapy in LMICs Healthcare Settings: A Case Study of Nepal.

Nepal's move from Cobalt-60 machines to modern linear accelerators in radiation therapy presents significant challenges. Infrastructure limitations demand substantial investments for facility upgrades, particularly in retrofitting existing cobalt-60 bunkers and ensuring stable power supplies. The scarcity of skilled personnel underscores the need for comprehensive education initiatives and international collaborations. Regulatory compliance requires the establishment of robust frameworks, emphasizing the urgency of an independent regulatory authority. Collaborations with international institutions offer expertise and resources crucial for capacity building. Financial constraints necessitate innovative financing models to mitigate procurement and maintenance costs. Unforeseen circumstances, exemplified by the Covid-19 pandemic, highlight the importance of adaptability in implementation strategies. Despite challenges, Nepal's transition signifies an opportunity to enhance cancer care, emphasizing the need for a tailored, multidimensional approach addressing infrastructure, workforce, regulatory, financial, and collaborative aspects.

Monte Carlo Estimation of CoVaR

CoVaR is an important measure of financial systemic risk due to its ability to capture tail dependence between the losses of different portfolios and its capacity to predict financial crises. Estimating CoVaR is challenging because its definition involves a zero-probability event, which is unobservable in the data. The existing model-based methods address this issue by assuming simplified structural models, which introduce biases that are difficult to eliminate. In “Monte Carlo Estimation of CoVaR,” Huang, Lin, and Hong propose using Monte Carlo methods to estimate CoVaR, leveraging the modeling flexibility of Monte Carlo simulation. Specifically, they introduce a batching estimator applicable to a wide range of financial models and prove that its best rate of convergence is [Formula: see text], where n is the sample size. Under the widely used delta-gamma approximation model, they further introduce an importance sampling–inspired estimator and prove that its best rate of convergence can be improved to [Formula: see text].

Beyond Risk: A Measure of Distribution Uncertainty

This paper addresses the critical yet often overlooked concept of distribution uncertainty (ambiguity) in decision making, emphasizing its importance alongside traditional outcome uncertainty (risk). It introduces a novel quantitative measure of ambiguity that accurately captures distribution uncertainty. This measure enhances empirical models, yielding more reliable parameter estimates and improving decision-making processes. The study demonstrates the practical value of this ambiguity measure using financial market decision making as an example. The measure helps identify and adjust for uncertainties in underlying distributions, supporting more robust financial models and better risk management. The findings advocate for integrating ambiguity considerations into data analytics models and developing more reliable methodologies for empirical research and practical applications. This study promotes a nuanced understanding of uncertainty, offering significant implications for research methodologies and practical risk management across various fields.

Primary care performance measurement in Brazil (Previne Brasil Program), 2022–2023

BackgroundPrevine Brasil is a new way of financing Primary Health Care (PHC) in Brazil that places users of public health services at the center of the system’s analysis and attention through the use of seven performance indicators. This study aims to analyze the performance of Primary Health Care in Brazil under the new financing model (Previne Brasil) in the period from the first four months of 2022 to the first four months of 2023.MethodsThis is a territorial ecological study using secondary data from the Primary Care Health Information System (SISAB). Seven performance indicators were included and analyzed according to Brazil, its regions, states and capitals. A comparative descriptive analysis was carried out between the spatial units.ResultsOf the seven indicators analyzed, only one (Proportion of pregnant women tested for syphilis and HIV) reached the proposed target considering the average of the four-month periods evaluated (target: 60%; Observed: 62.5%). The Northeast was the region with the best performance, surpassing the target in three of the seven indicators (prenatal consultations, tests for syphilis and HIV in pregnant women and dental care for pregnant women). The states and capitals showed progressive improvement in the performance indicators over the four quarters analyzed, although unevenly between the indicators. The indicators related to hypertension and diabetes were the worst. The Final Synthetic Indicator (FSI) showed an increase in the median over the four quarters (4.78; 5.65; 6.02 6.29), which represents an increase of 1.51 in the indicator between the first four months of 2022 and the first four months of 2023.ConclusionAlthough there have been improvements in the performance of Primary Health Care in Brazil, the achievement of the agreed targets for the indicators seems a long way off. The socio-spatial inequalities in the indicators reflect the country’s health complexity and reinforce the need for policies that take into account the local-regional context.

STATUS SOSIAL KELUARGA, LITERASI KEUANGAN TERHADAP MANAJEMEN KEUANGAN INDIVIDU DAN LIFESTYLE

The social status of a family plays a crucial role in shaping a student's ultimate career aspirations as an individual, as does financial literacy, which an individual acquires from their parents or through learning and experience. A family's social status naturally influences whether a child, as an individual, has better access to financial education or otherwise. Families with a higher social status will certainly provide different opportunities compared to those of lower economic standing. Similarly, strong financial literacy can help individuals manage their personal finances effectively. In this era of disruptive technology, a family's socioeconomic status, financial literacy, and individual financial management models can all impact one's lifestyle. This study aims to analyze the influence of a family's socioeconomic status, financial literacy, and individual financial management on lifestyle. The respondents of this study numbered 123. The results indicate that family social status affects individual financial management, financial literacy affects individual financial management, family social status affects lifestyle, financial literacy affects lifestyle, and individual financial management affects lifestyle. Additionally, individual financial management mediates the relationship between family social status and lifestyle, as well as between financial literacy and lifestyle. Keywords: Family social status, Financial literacy, Individual financial management, Lifestyle