GARCH models

Open Access Article

1 - Risk Measurement in Value at Risk (VaR): Application of Levy GARCH models (Study of Chemical industries in Tehran Stock Exchange)
hossein amiri mahmood najafi nezhad mohammad sayadi

Given that investing in the stock market is associated with risk, measuring it is one of the most important issues for investors. The present study measures risk measurement by the measure of risk. In this study, the value at risk, using the GARCH, APARCH and GJR models More

Given that investing in the stock market is associated with risk, measuring it is one of the most important issues for investors. The present study measures risk measurement by the measure of risk. In this study, the value at risk, using the GARCH, APARCH and GJR models with normal distributions, T-stents, T-stents, strings and strings, including string distributions; the reverse distribution of normal GIG (NIG) and generalized hyperbolic distribution (GHyp) is estimated. In this study, to measure risk, the efficiency of Tehran Stock Exchange index in chemical industries and total index has been used. The time period in this study includes a seven-year period with a daily frequency during the period of 05/01/1392 to 28/12/1398. The results showed that the Garc models were more accurate with the Levy distribution, and among the Garc models, the GJR model was more accurate, considering the Lou distribution and the Skewed-t distribution used among the other models. Manuscript profile

Open Access Article

2 - Forecasting Petroleum Futures Markets Volatility with GARCH and Markov Regime-Switching GARCH Models
مرتضی بکی حسکوئی فاطمه خواجوند

In this paper we compare a set of different standard GARCH models with a group ofMarkov Regime-Switching GARCH (MRS-GARCH) in terms of their ability to forecastthe petroleum futures markets volatility at horizons that range from one day to onemonth. To take into account More

In this paper we compare a set of different standard GARCH models with a group ofMarkov Regime-Switching GARCH (MRS-GARCH) in terms of their ability to forecastthe petroleum futures markets volatility at horizons that range from one day to onemonth. To take into account the excessive persistence usually found in GARCH modelsthat implies too smooth and too high volatility forecasts, MRS-GARCH models, wherethe parameters are allowed to switch between a low and a high volatility regime, areanalyzed. Both gaussian and fat-tailed conditional distributions for the residuals areassumed, and the degrees of freedom can also be state-dependent to capture possibletime-varying kurtosis. The forecasting performances of the competing models areevaluated with statistical loss functions. Under statistical losses, we use both tests ofequal predictive ability of the Diebold-Mariano-type and test of superior predictiveability, such as White􀀀s Reality Check and Hansen􀀀s SPA test. The empirical analysisdemonstrates that MRS-GARCH models do really outperform all standard GARCHmodels in forecasting volatility at shorter horizons according to a broad set of statisticalloss functions. At longer horizons standard asymmetric GARCH models fare the best.All this tests reject the presence of a better model than the MRS-GARCH-t in thisresearch Manuscript profile

Open Access Article

3 - Dependency structure between the markets of Iran, Turkey, China and the United Arab Emirates, according the approach of Copula – Markov Switching
S. Mozaffar Mirbargkar Maryam Sohrabi

Studying, and analyzing the dependency structure between the markets at the economic boom and bust have been suggested by the researchers and theorists of different areas. Furthermore, there have been various models to explain the correlation between the financial marke More

Studying, and analyzing the dependency structure between the markets at the economic boom and bust have been suggested by the researchers and theorists of different areas. Furthermore, there have been various models to explain the correlation between the financial markets. Among them, the Copula model has a high ability to recognize the asymmetric dependence structure. The present research is going to study the dependency structure in the financial markets of four countries; Iran, the United Arab Emirates, Turkey and China at the boom and bust cycling in the period of 2014-2017, applying conditional heterogeneity variance model (GARCH), the Markov switching approach, and the Copula functions. The results illustrate that there is an asymmetric structure in every regime, as at the recession time, the correlation between these markets and Iranian market would be higher than the boom time. Manuscript profile

Open Access Article

4 - Efficiency comparison among GARCH models in modeling and liquidity measurement. Case study: Tehran Stock Exchange
Mirfeyz Fallah Shams Yagoub Panahi

In investors' opinion, liquidity is a critical item for a market to be chosen by investors. This paper aim is a comparison among efficiency of 5 different GARCH models for modeling and liquidity risk measurement. Due to do that, a time series of data belong stock market More

In investors' opinion, liquidity is a critical item for a market to be chosen by investors. This paper aim is a comparison among efficiency of 5 different GARCH models for modeling and liquidity risk measurement. Due to do that, a time series of data belong stock market for a period of 1381-1390 were gathered.  Then liquidity risk was modeled by some GARCH models. Moreover, an Amihood criterion was calculated in accordance with TEPIX.  The results show that M-Arch is the best model among other GARCH models Manuscript profile

Open Access Article

5 - Effects oF Accruals Qulity on Conditional Volatility
سلاله فیض اللهی کسینی مریم لشکری زاده

AbstractBy demonstrating the inability of standard financial models that are based on perfect rationality, behavioral finance school turned to psychology and behavioral decision knowledge. Behavioral finance means the study of investment behavior by using the ideas and More

AbstractBy demonstrating the inability of standard financial models that are based on perfect rationality, behavioral finance school turned to psychology and behavioral decision knowledge. Behavioral finance means the study of investment behavior by using the ideas and beliefs that investors may act irrationally. According to behavioral finance model, because many factors are involved in investors' decisions and only one of these factors is valuation models, so biases can be seen in investors’ behavior. Using the data from 155 listed firms in Tehran Stock Exchange .This study attempts to investigate the relations between accruals quality, and conditional volatility. The results showed that accruals quality have an inverse impact on conditional volatility in Tehran Stock Exchange. Manuscript profile

Open Access Article

6 - Modelling of appropriate pattern in order to forecast systemic liquidity risk of corporate stocks in capital market of Iran, by using multivariate GARCH models and Markov switching approach
Seied Hamid Reza Sadat Shekarab Fereydon Ohadi mohsen Seighaly Mirfaze Fallah

This research aims to model and present an appropriate pattern in order to forecast systemic liquidity risk of corporate stocks in capital market of Iran. For this purpose, 486 listed companies in Tehran stock exchange and OTC from 2011 to 2020 were sampled and then the More

This research aims to model and present an appropriate pattern in order to forecast systemic liquidity risk of corporate stocks in capital market of Iran. For this purpose, 486 listed companies in Tehran stock exchange and OTC from 2011 to 2020 were sampled and then the companies were divided into four groups (portfolios) according to combination of indicators and types of activites of companies. Then by using types of multivariate GARCH models and comparing them, finanlly the VAR(1)-DBEKK(1,2) was selected as an optimum pattern . The results of research showed significant relationships among of liquidity shocks and volatilities with all of subsections, and consequently the main hypothesis based on “presence of systemic liquidity risk of corporate stocks in capital market of Iran” was accepted. In a way that the portfolios of company stocks with a “low level of liquidity- industry section” and “low level of liquidity- financial section” respectively had maximum and minimum liquidity shocks transmission of effects on future returns of the other portfolios, as well as the portfolio with a “high level of liquidity- financial section” had maximum volatility persistence and liquidity risk transmission to other portfolios. Manuscript profile

Open Access Article

7 - Designing and Explaining the Systematic Risk Estimation Model using metaheuristic Method in Tehran Stock Exchange: Adaptive Approach to the Model of Econometrics and Artificial Intelligence
Nemat Rastgoo hosein panahian

Systematic risk is always one of the most important indicators that investors and financial analysts attach importance in their financial decision making. The purpose of this research is to provide a new model based on accounting variables for estimating the systematic More

Systematic risk is always one of the most important indicators that investors and financial analysts attach importance in their financial decision making. The purpose of this research is to provide a new model based on accounting variables for estimating the systematic risk index (β). The period of study is from 2006 to 2015. The statistical population of the research is the companies accepted in Tehran Stock Exchange. Using the Cochran formula, 174 companies are selected as the research sample. For this purpose, systematic risk beta is first calculated through ARFIMA-FIGARCH, and then, estimated models are compared using stepwise regression econometrics (forward selection) and artificial intelligence (through combination of genetic algorithms and flying birds algorithms in selecting effective factors and its modeling by combining and implementing an evolutionary dynamic data estimator algorithm on the above algorithms). In order to analyze the data, three software of Oxmetrics, Eviews, and MATLAB are used. The prediction accuracy of two models based on econometrics and artificial intelligence is evaluated by calculating the correlation coefficient between estimated betas and beta of ARFIMA-FIGARCH. The AI-based model with a correlation coefficient of 94 percent shows a higher predictive accuracy.   Manuscript profile

Open Access Article

8 - Modeling and Forecasting Evaluation of Different Models of Short-Term Memory, Long-Term Memory, Markov Switching and Hyperbolic GARCH in Forecasting OPEC Crude Oil Price Volatility
mahmood mohammadi alamuti Mohammadreza Haddadi Younes Nademi

Predictability in financial markets is very complex, and the reasons for this complexity can be summarized as non-standard data, nonlinear data flow, and large variations in data. Determining the proper pattern for forecasting volatility can play a significant role in d More

Predictability in financial markets is very complex, and the reasons for this complexity can be summarized as non-standard data, nonlinear data flow, and large variations in data. Determining the proper pattern for forecasting volatility can play a significant role in decision making. In the old econometric models it is assumed that the component of error constant during the sample period. But in many financial time series it is observed that during periods of volatility is very sever. Under these conditions, the assumption of the exictence of the equivalence of variance is no longer reasonable. In the present paper, the GARCH, IGARCH, EGARCH, GJR-GARCH, FIEGARCH, HYGARCH, and MRS-GARCH two-regime models were evaluated in prediction of OPEC crude oil price volatility during 2010-2016 based on their RMSE error criterion. The results of this evaluation show the superiority of the Markov Switching GARCH Model on the 5 and 22-day horizons. Also, the long-term FIEGARCH memory model in predicting horizons of 1 and 10 days has better performance in predicting oil price volatilities than other competing models.   Manuscript profile

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