Research Article
Mitra Ghanbarzadeh; Nasrin Hozarmoghadam
Abstract
To increase the share of life insurance from the written insurance premium of commercial insurance and also considering the necessity of keeping life insurance customers by insurance companies, it is necessary to investigate the causes of surrendering life insurance and provide solutions to prevent it. ...
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To increase the share of life insurance from the written insurance premium of commercial insurance and also considering the necessity of keeping life insurance customers by insurance companies, it is necessary to investigate the causes of surrendering life insurance and provide solutions to prevent it. Based on this, the aim of this paper is to investigate the surrender of life insurance and analyze the corporate and economic factors affecting it in Iran's insurance industry. In order to respond to this goal, the effort is to first identify the micro-corporate factors as well as the economic factors affecting the surrender of life insurance and evaluate and analyze the effectiveness of each factor. Then, the information available in this field is examined separately for Iran's insurance companies and analyzed at micro and macro levels, and finally, operational solutions and necessary measures to reduce surrendering the life insurance are presented.
Research Article
Kartikay Goyle
Abstract
This paper compares stochastic models for simulating leveraged Exchange-Traded Funds (LETFs) price paths, focusing on their applications in risk management and option pricing. Using TQQQ (a 3x leveraged ETF tracking NASDAQ-100) as our case study, we evaluate Geometric Brownian Motion (GBM), Generalized ...
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This paper compares stochastic models for simulating leveraged Exchange-Traded Funds (LETFs) price paths, focusing on their applications in risk management and option pricing. Using TQQQ (a 3x leveraged ETF tracking NASDAQ-100) as our case study, we evaluate Geometric Brownian Motion (GBM), Generalized Autoregressive Conditional Heteroskedasticity (GARCH), Heston stochastic volatility, Stochastic Volatility with Jumps (SVJD), and propose a novel Multi-Scale Volatility with Jumps (MSVJ) model that captures both fast and slow volatility components. Furthermore, we develop a comprehensive evaluation framework that examines both price and volatility characteristics of the simulated paths against the actual TQQQ data. Our analysis spans different market conditions, including the COVID-19 crash and the 2022 market drawdown. While our proposed MSVJ model excels in capturing volatility dynamics and price range estimation, we find that each model exhibits unique strengths in different aspects of LETFs’ behavior. The choice of most appropriate model depends on specific considerations for different applications, such as risk assessment, options pricing, or portfolio management.
Research Article
Mohammad Reza Haddadi; Hossein Nasrollahi
Abstract
In order to reduce the risk of financial markets, various tools have emerged, and option contracts are the most common tools in this regard. The Black-Scholes model is used to price a wide range of options contracts. The basic assumption in this model is to follow the normal distribution of returns. ...
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In order to reduce the risk of financial markets, various tools have emerged, and option contracts are the most common tools in this regard. The Black-Scholes model is used to price a wide range of options contracts. The basic assumption in this model is to follow the normal distribution of returns. But the reality of the market indicates the skewness and kurtosis of the data, which reduces the accuracy of calculating the option price. The Gram-Charlie model has more flexibility than Black-Scholes model with abnormal skewness and kurtosis. The main purpose of this research is to determine the European call option price using non-normal data. In this regard, we present new models, fractional Gram-Charlier model and mixed fractional Gram-Charlier model, for option pricing. For this purpose, the data of Shasta and Khodro symbols have been selected from Iran Stock Exchange that Khodro in the period 2020-07-27 to 2023-11-1 and Shasta in the period 2022-7-25 to 2023-11-1 have been used. The results of this research show that Shasta has more abnormal skewness and kurtosis than Khodro. The option price calculated with the Gram-Charlier and extended models of Gram-Charlier are shown a smaller error compared to other models in the Shasta. Also, the results show that under abnormal skewness and kurtosis, our new models have more flexibility than the Black-Scholes model and fractional models.
Research Article
Reza Taleblou
Abstract
In this paper, we evaluate the performance of two machine learning architectures— Recurrent Neural Networks (RNN) and Transformer-based models—on four commodity-based company indices from the Tehran Stock Exchange. The Transformer-based models used in this study include AutoFormer, FEDformer, ...
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In this paper, we evaluate the performance of two machine learning architectures— Recurrent Neural Networks (RNN) and Transformer-based models—on four commodity-based company indices from the Tehran Stock Exchange. The Transformer-based models used in this study include AutoFormer, FEDformer, Informer, and PatchTST, while the RNN-based models consist of GRU and LSTM. The dataset comprises daily observations collected from April 20, 2020, to November 20, 2024. To enhance the generalization power of the models and prevent overfitting, we employ two techniques: splitting the training and test samples, and applying regularization methods such as dropout. Hyperparameters for all models were selected using a visual method. Our results indicate that the PatchTST model outperforms other methods in terms of Root Mean Squared Error (RMSE) for both 1-day and 5-day (1-week) forecasting horizons. The FEDformer model also demonstrates promising performance, particularly for forecasting the MetalOre time series. In contrast, the AutoFormer model performs relatively poorly for longer forecasting horizons, while the GRU and LSTM models yield mixed results. These findings underscore the significant impact of model selection and forecasting horizon on the accuracy of time series forecasts, emphasizing the importance of careful model choice and hyperparameter tuning for achieving optimal performance.
Research Article
Hasan Bayati; Saeid Tajdini; Seung Wook Jung; Majid Lotfi Ghahroud
Abstract
This study presents an enhanced framework for portfolio performance evaluation by refining Jensens alpha to incorporate dynamic conditional beta. Traditional models rely on static beta assumptions, often overlooking the time-varying nature of portfolio risk and its influence on performance metrics. By ...
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This study presents an enhanced framework for portfolio performance evaluation by refining Jensens alpha to incorporate dynamic conditional beta. Traditional models rely on static beta assumptions, often overlooking the time-varying nature of portfolio risk and its influence on performance metrics. By integrating dynamic conditional beta, this research provides a more precise measure of risk-adjusted returns, offering deeper insights into investment performance. The methodology is applied to subsidiaries of the Golrang Industrial GroupKimiatek, Padideh, Ofoghe Kourosh, and Pakshoo-analyzing their financial performance under varying market conditions. The results demonstrate the superiority of adjusted dynamic conditional Jensens alpha, particularly during periods of heightened market volatility. This advancement equips investors and portfolio managers with more reliable performance assessment tools, supporting strategic decision-making and improving risk-return analysis. By addressing limitations in traditional evaluation models, this study contributes to the development of robust financial metrics and emphasizes the importance of incorporating time-sensitive risk factors for comprehensive portfolio analysis.
Research Article
Rexon Nainggolan; Hendri Sembiring; Clarijun Quimada Montebon
Abstract
The primary objective of this research is to measure the information asymmetry before, during, and after earnings announcements and how it relates to the drift in post-earnings announcements over an extended period. The study uses the bid-ask spread as an information asymmetry proxy and employs a market ...
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The primary objective of this research is to measure the information asymmetry before, during, and after earnings announcements and how it relates to the drift in post-earnings announcements over an extended period. The study uses the bid-ask spread as an information asymmetry proxy and employs a market model to assess daily data on Indonesia’s equity market before, during, and after the earnings announcement. Data were analyzed using the t test and least squares regression. The study provides empirical evidence showing that the bid-ask spread increases significantly before the earnings announcement, indicating information uncertainties between sellers and buyers. The findings show that the market reacts to accounting information indicated by a significantly reduced bid-ask spread soon after the market digests the information, following the concept of semi-strong market efficiency. The study shows a cumulative abnormal return and bid-ask spread strongly correlated a few days following earnings. However, the analysis found no long-term association between bid-ask spread and post-earnings announcement drift. The study found that stock market sellers and buyers use accounting data to set prices and that earnings releases reduce the bid-ask difference. The study suggests that the market regulator supports timely disclosure of this information.
