Research Article
Behzad Abbasi; Kazem Nouri
Abstract
Option pricing is a fundamental issue in financial markets, and barrier options are a popular type of options that can become valuable or worthless when the underlying asset price reaches a predetermined level. A double barrier option consist two barriers, one situated above and the other below the prevailing ...
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Option pricing is a fundamental issue in financial markets, and barrier options are a popular type of options that can become valuable or worthless when the underlying asset price reaches a predetermined level. A double barrier option consist two barriers, one situated above and the other below the prevailing stock price. This particular option is categorized as path dependent because the return for the holder is influenced by the stock price’s breach of the two barriers. The double barrier option contract stipulates three specific payoffs, depending on whether the up-barrier or down-barrier is touched, or if there is no breach of either barrier during the entire duration of the option. In this paper, pricing of the double barrier options when the underlying asset price follows the exponential Ornstein-Uhlenbeck model is investigated, and also pricing formulas for different types of double barrier options (knock-in and knock-out) are derived by α-paths of uncertain differential equations in the uncertain environment.
Research Article
Kimiya Tavakoli; Abdolsadeh Neisy; Alireza Zamanpour
Abstract
Modeling and pricing European options are crucial tasks for financial companies seeking to determine the fair value of these instruments. Conventional methods, such as using Black-Scholes partial differential equations (PDEs), face challenges due to the high complexity involved and lack of data. To address ...
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Modeling and pricing European options are crucial tasks for financial companies seeking to determine the fair value of these instruments. Conventional methods, such as using Black-Scholes partial differential equations (PDEs), face challenges due to the high complexity involved and lack of data. To address these challenges, PINNs have recently emerged as a promising approach to solving the Black-Scholes PDEs for European options. In this paper, we tackle the two-dimensional Black-Scholes model to determine the price of a European exchange option. We employ a kind of ANNs (PINN) that is specifically designed to learn the option’s value by minimizing an appropriately defined loss function. The data for our study were generated through simulations conducted in Python. Our results demonstrate the efficacy of the PINN approach by comparing the computed fair value of a European exchange option with the traditional solutions. The findings underscore the potential of PINNs in providing accurate and efficient pricing for complex financial derivatives.
Research Article
Abbas Raad; Reza Ofoghi; Ghadir Mahdavi
Abstract
This study aims to examine the function of blockchain technology to detect fraud in health insurance. we consider the literature on fraud in health insurance, blockchain, and smart contracts to to test a newly structured software system based on blockchain technology for this purpose. Different blockchain ...
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This study aims to examine the function of blockchain technology to detect fraud in health insurance. we consider the literature on fraud in health insurance, blockchain, and smart contracts to to test a newly structured software system based on blockchain technology for this purpose. Different blockchain platforms, consensus algorithms, and structures have been used to pick the proposed system’s best structure based on blockchain. Eventually, the best techniques to put the system to the test and evaluate the findings were assessed. we propose a standardized system, where blockchain is applied to store data and smart contracts are used to automate insurance policies. Furthermore, a web-based application, which acts as core insurance software, is proposed for all stakeholders to communicate with the blockchain and smart contracts. Therefore, the proposed system comprises a blockchain, web app, and standardized smart contracts. The proposed system mainly focuses on fraud detection in insurance claims while maintaining a standard data storage and transfer structure. The system proved to be thriving once claim data can be created, read, and analyzed (i.e. fraudulent data are caught) effectively in a standard way. The web app consists of a front-end and back-end section. The front-end enables users to interact with the proposed system, and the back-end allows the insurance company to store records on the blockchain and increase the chances of detecting fraud in insurance claims, especially Digital Insurance Claims. Finally, a blockchain-based web application that can be used as core insurance software for any health insurance company is proposed.
Research Article
Shokoofeh Banihashemi; Parto Karimi
Abstract
The portfolio optimization problem, including portfolio selection, typically aims to maximize return and minimize risk. In this paper, we discuss about increasing use of stochastic portfolios in investments and aim to create optimal portfolios. It follows the relative wealth process of these ...
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The portfolio optimization problem, including portfolio selection, typically aims to maximize return and minimize risk. In this paper, we discuss about increasing use of stochastic portfolios in investments and aim to create optimal portfolios. It follows the relative wealth process of these portfolios, outperforms the market portfolio over sufficiently long time-horizons. In this regard, initially, a model of the market is presented by the stochastic portfolio theory (SPT) and features like Growth rate, Excess growth rate are mentioned. Then, functionally-generated portfolios are defined by using diversity weighted portfolios with parameters p ∈ (0, 1), p < 0 and combination of them. Finally, by obtaining the daily closing price of 10 stocks in Tehran Stock Exchange (TSE) ,the performance of diversity weighted portfolios is investigated.
Research Article
Seyed Jalal Tabatabaei
Abstract
In recent years, there has been growing interest in the application of stochastic processes to model financial markets, particularly in the pricing and prediction of derivative instruments such as options. One of the more advanced models that has emerged for capturing the dynamics of financial time series ...
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In recent years, there has been growing interest in the application of stochastic processes to model financial markets, particularly in the pricing and prediction of derivative instruments such as options. One of the more advanced models that has emerged for capturing the dynamics of financial time series is the Lévy process, which generalizes the traditional Brownian motion by incorporating jumps and heavy tails, features often observed in real financial data. This paper investigates the applicability of Lévy processes in predicting the evolution of financial series, with a specific focus on vanilla option pricing. In our methodology,By reviewing the theoretical underpinnings of Lévy processes, highlighting key aspects such as the characteristic function and the variance-gamma process, we calibrate a Lévy-based model to 77 mid-prices of a set of European call options on the S&P 500 Index at the close of the market on 11 April 2022. We employ maximum likelihood estimation (MLE) and the expectation-maximization (EM) algorithm to fit the parameters of the Lévy process. Our results indicate that the Lévy process model provides a significantly better fit to market data than the Black-Scholes model, particularly in capturing the heavy tails and jump behavior observed in option price movements. Additionally, the Lévy model demonstrates superior predictive performance in out-of-sample testing, improving the accuracy of option pricing and hedging strategies. These findings suggest that Lévy processes hold substantial promise for enhancing financial series prediction and derivative pricing in markets characterized by volatility clustering and sudden jumps.
Research Article
Farnaz Hooshmand; Mitra Ghanbarzadeh
Abstract
Asset-liability management (ALM) is a critical issue for insurance companies because the premiums received from policyholders should be invested according to regulatory frameworks while providing suitable profitability, and simultaneously, the insurer should fulfill its obligations to policyholders on ...
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Asset-liability management (ALM) is a critical issue for insurance companies because the premiums received from policyholders should be invested according to regulatory frameworks while providing suitable profitability, and simultaneously, the insurer should fulfill its obligations to policyholders on time. Our focus is on participating (with-profit) life insurance policies, where policyholders not only receive a guaranteed profit but also participate in the return of the insurer's investment-portfolio. Due to the risks of death and surrender, uncertainty in asset returns, the broad range of insurance products and regulations, it is difficult to make optimal decisions. In this paper, we aim to present a new multi-stage stochastic programming ALM model for with-profit life insurance policies. Compared to existing models that involve some simplifications, our model incorporates more details and is closer to reality. Specifically, our model is multi-stage and updates the amount of policies investment reserves based on the realized return of the investment-portfolio. Evaluation of the model across a variety of datasets confirms the effectiveness of the proposed model.
Research Article
Saba Yaghobipour; Majid Yarahmadi
Abstract
The aim of this paper is to propose a new method for solving a calss of stochasticfractional optimal control problems. To this end, we introduce an equivalent form for the presented stochastic-fractional optimal control problem and prove that these problems have the same solution. Therefore, the corresponding ...
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The aim of this paper is to propose a new method for solving a calss of stochasticfractional optimal control problems. To this end, we introduce an equivalent form for the presented stochastic-fractional optimal control problem and prove that these problems have the same solution. Therefore, the corresponding Hamilton– Jacobi–Bellman (HJB) equation to the equivalent stochastic-fractional optimal control problem is presented and then the Hamiltonian of the system is obtained. Finally, by considering Sharpe ratio as a performance index, Merton’s portfolio selection problem is solved by the presented stochastic-fractional optimal control method. Moreover, for indicating the advantages of the proposed method, optimal pairs trading problem is simulated.
Research Article
Mahdi Goldani; Soraya Asadi Tirvan
Abstract
In predictive modeling, overfitting poses a significant risk, particularly when the feature count surpasses the number of observations, a common scenario in highdimensional datasets. To mitigate this risk, feature selection is employed to enhance model generalizability by reducing the dimensionality ...
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In predictive modeling, overfitting poses a significant risk, particularly when the feature count surpasses the number of observations, a common scenario in highdimensional datasets. To mitigate this risk, feature selection is employed to enhance model generalizability by reducing the dimensionality of the data. This study evaluates the stability of feature selection techniques with respect to varying data volumes, focusing on time series similarity methods. Utilizing a comprehensive dataset that includes the closing, opening, high, and low prices of stocks from 100 high-income companies listed in the Fortune Global 500, this research compares several feature selection methods, including variance thresholds, edit distance, and Hausdorff distance metrics. Numerous feature selection methods were investigated in literature. Selecting the more accurate feature selection methods in order to forecast can be challenging [1]. So, this study examines the most well-known feature selection methods’ performance in different data sizes. The aim is to identify methods that show minimal sensitivity to the quantity of data, ensuring robustness and reliability in predictions, which is crucial for financial forecasting. Results indicate that among the tested feature selection strategies, the variance method, edit distance, and Hausdorff methods exhibit the least sensitivity to changes in data volume. These methods, therefore, provide a dependable approach to reducing feature space without significantly compromising predictive accuracy. This study highlights the effectiveness of time series similarity methods in feature selection and underlines their potential in applications involving fluctuating datasets, such as financial markets or dynamic economic conditions.
