Dynamic GAS Mathematical Based Modeling for Predicting and Assessing the Memory Free Value at Risk of Tehran Stock Exchange Total Index
محورهای موضوعی : Mathematical Engineering
Mohammad Ebrahim Samavi
1
,
Hashem Nikoomaram
2
,
Mahdi Madanchi zaj
3
,
Ahmad Yaghobnezhad
4
1 - Department of Financial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - Department of Financial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
3 - Department of Financial Management, Electronic Campus, Islamic Azad University Tehran, Iran
4 - Department of Accounting, Central Tehran Branch, Islamic Azad University, Tehran, Iran
کلید واژه: Value at Risk, Capital Market, gold, Mathematical Modeling for Finance, GAS model,
چکیده مقاله :
In recent decades, especially since 2000, advanced mathematical methods for financial modeling have been widely used so that the basic challenges of financial science can be overcome by using these methods. 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. 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 TSE 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. For TSE only two models, GAS and GARCH, are suitable for estimating value at risk and GAS model is preferable. Also, the duration of risk of value at risk errors for all three models for gold and TSE lacks long-term memory, indicating its reliance on financial turmoil.
In recent decades, especially since 2000, advanced mathematical methods for financial modeling have been widely used so that the basic challenges of financial science can be overcome by using these methods. 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. 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 TSE 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. For TSE only two models, GAS and GARCH, are suitable for estimating value at risk and GAS model is preferable. Also, the duration of risk of value at risk errors for all three models for gold and TSE lacks long-term memory, indicating its reliance on financial turmoil.
