The purpose of this study is to investigate the clustering of fluctuations and its asymmetry in Tehran Stock Exchange. Large changes in prices tend to be large changes and small changes tend to be small changes that are called clustering of fluctuations. On the other ha More
The purpose of this study is to investigate the clustering of fluctuations and its asymmetry in Tehran Stock Exchange. Large changes in prices tend to be large changes and small changes tend to be small changes that are called clustering of fluctuations. On the other hand, higher volatility fluctuations, They tend to form more clusters than small fluctuations, which are referred to as clustering oscillations of oscillations. The volatility of return on assets can directly affect the price of transaction options and the risk of stocks and portfolios. This research is a practical and quantitative research. The statistical society of the time series of the index of Tehran Stock Exchange and the sample used in the time series of return on the total index in the period from the beginning of 2008 to August 2012 is. The index values are extracted from the new rational software and then the logarithmic yield is calculated and analyzed with the Eviews software. Based on the Box and Jenkins approach, the mean ARMA equation was obtained and ARCH test confirmed the existence of clustering fluctuations. The TGARCH model showed asymmetry in volatility and leverage effect. According to the AKIC statistic, the best GARCH model was used for extraction of fluctuations, ETGARCH was introduced.
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It has a significant negative impact on economic growth.One of the warning signs of the financial crisis is the increasing stress that is occurring in the financial markets, leading to increased uncertainty and instability in the economy. Therefore, the main purpose of More
It has a significant negative impact on economic growth.One of the warning signs of the financial crisis is the increasing stress that is occurring in the financial markets, leading to increased uncertainty and instability in the economy. Therefore, the main purpose of this study is to calculate financial stress index in Iranian financial markets and identify its effects on economic growth.This paper deals with the relationship between financial stress and recession and economic prosperity in three stages. at first, the effect of financial variables on financial stress has been measured by panel data using random data and random effects.Then by constructing a composite index of financial stress uncertainty using Arch & Garch model we are able to investigate the relationship between economic growth and financial stress uncertainty index. In the second the effect of financial stress on economic growth and recession by multilayer perceptron method shows that it is predicted that the economy will continue to be in recession from the year 1397 to the first quarter of 1399 and with the beginning of the second season of 1399 we will see economic prosperity.Finally, the effect of financial stress along with other variables of production function on economic growth were measured using Markov switching self-regression model. Based on the results, the index has a significant negative effect on economic growth in the long run and short run models.
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The purpose of this study was to design a new model for predicting the Tehran Stock Exchange index using pattern recognition in a combination of hidden Markov model and artificial intelligence. The present study is an applied type and mathematical analytical method. Its More
The purpose of this study was to design a new model for predicting the Tehran Stock Exchange index using pattern recognition in a combination of hidden Markov model and artificial intelligence. The present study is an applied type and mathematical analytical method. Its location is the Tehran Stock Exchange and during the years 2010 to 2020. Findings showed that the prediction error rate with artificial neural network has a higher accuracy than Markov's hidden model. Also, the prediction error of the hybrid model is much lower than the other two models for predicting the total stock index of Tehran Stock Exchange, so it has higher accuracy for forecasting stocks. According to the MAPE index, the hybrid model method could improve the predictive power of the artificial neural network by 0.044% and also improve the predictive power of the hidden Markov model by 0.70%.
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