List of Articles GAS model

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  1 - Modeling and Forecasting Distribution of Return on the Tehran Stock Exchange Index and Bitcoin with the GAS Time Variable Method
  Mohammad Ebrahim Samavi hashem nikoomaram Mahdi Madanchi Zaj Ahmad Yaghobnezahd
  10.30495/jfksa.2022.21081
  Predicting returns with the least error is one of the most important issues in financial markets that has been considered by many researchers in recent decades .Traditional linear and nonlinear models due to the inefficiency of linear models in market turbulence, the la More

  Predicting returns with the least error is one of the most important issues in financial markets that has been considered by many researchers in recent decades .Traditional linear and nonlinear models due to the inefficiency of linear models in market turbulence, the lack of correct extraction of the conditional distribution form of data due to the failure to record the conditional distribution dynamics in nonlinear models and the existence of limiting assumptions, it lacks the ability to predict returns in different market conditions. In order to eliminate the disadvantages of traditional models, in the present study using a new time-variable method called generalized autoregressive score (GAS) in order to predict the distribution of return of the total index of the stock exchange during the period 2010 to 2020 and for Bitcoin during the period 2014 to 2020. The results of modeling for the two assets by the new GAS model are compared with the results of the GARCH and AR models and their performance is tested for inside and outside the sample. The results show that in order to predict the daily return, the overall index of the new GAS model has a better performance and in order to predict the daily return of bitcoin, the GARCH model has been preferred. Manuscript profile
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  2 - Identification of Erosion Severity Area with Study of Fargas Model (Case Study: Sangab Drainage Basin- Iran)
  H. Ahmadi A. A Mohammadi
  In order to identify critical sediment sources in large catchments, using easilyavailable terrain information at regional scale, a methodology has developed to obtaina qualitative assessment necessary for environmental management. So it has been triedto study and used F More

  In order to identify critical sediment sources in large catchments, using easilyavailable terrain information at regional scale, a methodology has developed to obtaina qualitative assessment necessary for environmental management. So it has been triedto study and used Fargas and etal, method in this research. This has been done in oneof the sub-basins of Hable Rood basin called Sangab with an area of 7684.71 hac inNE Iran, arid and semiarid climate of Iran. The main objective of this model is to usebasic terrain data related to the erosive processes that contribute to the production,transportation and accumulation of sediments through the main water paths in thewatershed. This model is based on the selection of homogeneous zones regardingdrainage density and lithology, achieved by joining the basic units by a rating system.The values of drainage density are rated according to an erosion class. The lithology israted by erosion indexes, adapted from FAO (1977). The combination andreclassification of the results brings about five qualitative classes of sediment riskaccording to Fargas and etal (1997). The privileges of this method is, it used only twomain factors of the erosion, that are lithology and drainage density, and this factors arein our geologic and topographic maps in Iran. The mapping scale was 1:50000 and themodel were implemented through a vector GIS (Arc GIS9.2). The tested methodologyhas been proved useful as an initial approach for erosion assessment and soilconservation planning at regional level and also to select priority areas where furtheranalyses can be developed and finally for environmental management. Manuscript profile
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  3 - مدلسازی ریاضی مبتنی بر GAS جهت برآورد ارزش در معرض ریسک فاقد حافظه برای شاخص کل بورس اوراق بهادار تهران
  محمدابراهیم سماوی هاشم نیکومرام مهدی معدنچی زاج احمد یعقوب نژاد
  در ده‌های اخیر، به صورت ویژه از سال 2000 میلادی روش‌های پیشرفته ریاضی جهت مدلسازی مالی کاربرد فراوانی پیدا کرده است به طوری که با استفاده از این روش‌های می‌توان به بسیاری از چالش‌های اساسی علوم مالی فائق آمد. اولین قدم در مدیریت ریسک در حوزه سرمایه گذاری، محاسبه متغیری More

  در ده‌های اخیر، به صورت ویژه از سال 2000 میلادی روش‌های پیشرفته ریاضی جهت مدلسازی مالی کاربرد فراوانی پیدا کرده است به طوری که با استفاده از این روش‌های می‌توان به بسیاری از چالش‌های اساسی علوم مالی فائق آمد. اولین قدم در مدیریت ریسک در حوزه سرمایه گذاری، محاسبه متغیری است که ریسک را به طور دقیق توضیح می دهد. یکی از پرکاربردترین معیارها برای محاسبه ریسک، ارزش در معرض ریسک است که در سه دهه گذشته مورد توجه محققان مالی بوده است. هدف مطالعه حاضر مدلسازی پویا و زمان متغیر با استفاده از تکنیکی به نام امتیاز خودرگرسیون تعمیم یافته (GAS) برای تخمین ارزش در معرض ریسک شاخص کل با استفاده از داده های روزانه از سال 1390 الی 1399 و با فرض توزیع t-student است. نتایج آن با نتایج مدل های AR و GARCH شناخته شده مقایسه شده است. برای TSE تنها دو مدل GAS و GARCH برای تخمین ارزش در معرض ریسک مناسب هستند و مدل GAS ارجحیت دارد. همچنین، مدت زمان ریسک خطای ارزش در معرض ریسک برای هر سه مدل فاقد حافظه بلندمدت است که نشان دهنده اتکای آن در بحران های مالی است. Manuscript profile
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  4 - Dynamic GAS Based Modeling for Predicting and Assessing the Value at Risk of Bitcoin and Gold
  Mohammad Ebrahim Samavi Hashem Nikoomaram Mahdi Madanchi Zaj Ahmad Yaghobnezhad
  The first step in risk management in the field of investment is to calculate the variable that explains the risk accurately. One of the most widely used criteria for calculating risk is the value at risk, which has been the focus of financial researchers for the past th More

  The first step in risk management in the field of investment is to calculate the variable that explains the risk accurately. One of the most widely used criteria for calculating risk is the value at risk, which has been the focus of financial researchers for the past three decades. Therefore, the aim of the present study is dynamic modeling and variable time using a technique called Generalized Autoregressive Score (GAS) to estimate value at risk in bitcoin and gold by using daily data since 2010 to 2020 and assuming the distribution of t-student. its results are compared with the results of known AR and GARCH models. The results showed that for gold models such as GAS, GARCH and AR were able to estimate the value at risk at 5% error level. Among them, the GAS model had the best performance. For Bitcoin only two models, GAS and GARCH, are suitable for estimating value at risk and GARCH model is preferable. Also, the duration of risk of value at risk errors for all three models for gold and bitcoin lacks long-term memory, indicating its reliance on financial turmoil. Manuscript profile
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  5 - Dynamic GAS Based Modeling for Predicting and Assessing the Value at Risk of Tehran Stock Exchange Index and Bitcoin
  Mohammad Ebrahim Samavi Hashem Nikoomaram Mahdi Madanchi Zaj Ahmad Yaghoobnezhad
  10.30495/afi.2022.1956046.1119
  Purpose: This research has been written with the aim of modeling a new criterion for measuring risk in order to eliminate the shortcomings of traditional models in the field of investment risk management.Methodology: In the present study, with a practical purpose, to es More

  Purpose: This research has been written with the aim of modeling a new criterion for measuring risk in order to eliminate the shortcomings of traditional models in the field of investment risk management.Methodology: In the present study, with a practical purpose, to estimate the value at risk of daily bitcoin price data (2,707 views) in the years 2013 to 2020 and the data of the total stock exchange index (2,753 views) 2011 to 2020 has been used in two groups of education and test (500 views). In order to estimate the value at risk using the nonlinear method and the generalized variable self-fitting time (GAS) method, modeling was performed by learning from the data of the training group and the accuracy of the model was determined by the data of the experimental group.Findings: The results showed that for the total stock index, only two models, GAS and GARCH, are suitable risk estimators. On the other hand, for Bitcoin cryptocurrencies, only two models, GAS and GARCH, are suitable risk estimators, which GARCH model is more preferable.Originality / Value: Findings showed that the new GAS model is a preferential estimator for the total stock market index than other nonlinear models. This is due to the variable time feature as well as the dynamics of the GAS model, which is able to respond to market turbulence conditions unlike traditional models in the short run. These results also help investors and active financial institutions to manage risk in their trading systems. Manuscript profile