List of Articles Dynamic Factor Model

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  1 - Identifying Banking Crisis Using Banking Stress Index in Iranian Economy (Dynamic Factor Model)
  samineh ghasemifar Abolfazl Shahabadi shamsollah shirinbakhsh mirhosien mousavi azam ahmadian
  By the fact that most of the public and private sector financing comes from the country's banking sector, It is important to maintain stability and prevent a crisis in the banking system. The purpose of this study is to identify the banking crisis using the Banking Stre More

  By the fact that most of the public and private sector financing comes from the country's banking sector, It is important to maintain stability and prevent a crisis in the banking system. The purpose of this study is to identify the banking crisis using the Banking Stress Index in the Iranian economy for the period of 1398-1388. The Banking Stress Index is the best benchmark for assessing the banking crisis that reflects uncertainty, instability and financial friction in the banking system. In this study, the design of a bank stress index was performed using a dynamic factor model. This model is estimated by the maximum likelihood method and the stochastic pattern of missing data. Using six variables determining the banking crisis in the country, two banking stress indices with two different natures have been estimated in time series to examine the stability of the banking system. Finally, both indices of stress showed estimation; there is a precise timing of the coincidence between the greatest amounts of bank stress and the shocks to the Iranian economy. It was also concluded that bank stress indicators reflect the effects of external factors, including sanctions on the banking system fundamental weaknesses of the banking system, as well as being able to predict banking crises Manuscript profile
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  2 - Investigation of the role of macroeconomic variables in Tehran Stock Exchange uncertainty using risk filtering, MCMC simulation and ARDL approaches.
  Amir Sarabadani Ali Baghani mohsen hamidian Ghodratollah Emamverdi Norooz Noroolahzadeh
  AbstractIn the present study a new total uncertainty criterion in Tehran Stock Exchange was estimated and the impact of macroeconomic variables on this uncertainty was addressed. Risk filtering with an approach to GDFM was first used to detect specific component of 25 t More

  AbstractIn the present study a new total uncertainty criterion in Tehran Stock Exchange was estimated and the impact of macroeconomic variables on this uncertainty was addressed. Risk filtering with an approach to GDFM was first used to detect specific component of 25 time series of the main indices of the Tehran Stock Exchange over 10 years. In the next step, the conditional volatility of the remaining time series’ specific components were estimated using Stochastic volatility (SV) model and finally conditional volatility simulated using Markov chain Monte Carlo (MCMC) approach was averaged to obtain total uncertainty of the Tehran Stock exchange. The ARDL results showed that Tehran Stock Exchange uncertainty is dependent on independent variables such as inflation rate, banks' real interest rate, exchange rate in free Exchange market, liquidity, tax revenue and oil price. According to the results, however, no significant correlation exists between unemployment rate and stock market uncertainty. Manuscript profile
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  3 - One-way and two-way risk filtering using generalized dynamic factor model in Tehran Stock Exchange
  amir sarabadani Ali Baghani Mohsen Hamidian Ghodratollah Emamverdi Norooz Noorolahzadea
  AbstractAccording to statistics, risk estimation makes unusual predictions without focusing on the relevant factors and only focusing on a set of equations. In this study, we used a spreadsheet data set of time series and a new method for risk estimation. This estimatio More

  AbstractAccording to statistics, risk estimation makes unusual predictions without focusing on the relevant factors and only focusing on a set of equations. In this study, we used a spreadsheet data set of time series and a new method for risk estimation. This estimation was based on a generalized dynamic factor model (GDFM) and daily data series obtained from different measures of Tehran Stock Exchange over a 10-year period during 2008 to 2018. we first utilized a generalized dynamic factor model proposed by Forni et al in order to determine statistic and dynamic factors. In the second step, by using MATLAB, we estimated the joint component of the study series as Tehran Stock Exchange risk. Next, using the generalized least squares (GLS) method, we examined the impact of each of the filtered risks on the index returns. The results showed that although both risks estimated through one-side and two-side filtering substantially and significantly explain the changes in the performance of the studied indices, but the risk estimated through two-side filtering using GDFM can explain the returns changes much better and more accurate than the one-side filter using the same model. Manuscript profile