Showing 10 results for Garch
Dr Hassan Heidari, Sahar Bashiri,
Volume 3, Issue 9 (10-2012)
Abstract
This paper investigates the relationship between real exchange rate uncertainty and stock price index in Tehran stock exchange for the period of 1995-2009 by using monthly data and applying Bivariate Generalized Autoregressive Conditional Heteroskedasticity model (Bivariate GARCH). The results show that there is a negative and significant relationship between real exchange rate uncertainty and stock price index. However, the relationship between stock price uncertainty and real exchange rate is insignificant. Therefore, our results recommend that the policies which cause more volatility in the exchange market and also more volatility in the real exchange rate should be avoided to ensure the sustainable growth of the stock market and its price index.
Minoo Nazifi Naeini, Dr Shahram Fatahi, Dr Saeed Samadi,
Volume 3, Issue 9 (10-2012)
Abstract
In this study we compare a set of Markov Regime-Switching GARCH models in terms of their ability to forecast the Tehran stock market volatility at different time intervals. SW-GARCH models have been used to avoid the excessive persistence that usually found in GARCH models. In SW-GARCH models all parameters are allowed to switch between a low or high volatility regimes. Both Gaussian and fat-tailed conditional distributions are assumed for the residuals, and the degrees of freedom can also be state-dependent to capture possible time-varying kurtosis. Using stationary bootstrap and re-sampling, the forecasting performances of the competing models are evaluated by statistical loss functions. The empirical analysis demonstrates that SW-GARCH models outperform all standard GARCH models in forecasting volatility. Also, the SW-GARCH model with the t distribution for errors has the best performance in fitting a model and estimation.
Dr Nader Mehregan, Dr Parviz Mohammadzadeh, Dr Mahmoud Haghani, Yunes Salmani,
Volume 4, Issue 12 (7-2013)
Abstract
Price shocks lead to oil price volatility in world oil markets. In response to this volatility, economic growth may take different regime and behavior patterns in different situation. Investigating this multi behavior patterns can be useful for policymakers to reduce the effect of oil price volatility. In this study, an EGARCH model has developed using the seasonal data of OPEC oil basket nominal prices during 1367:Q1-1389:Q4. Markov switching models is also applied to investigate the multi behavior patterns of economic growth in response to oil price volatility in Iran.
The results show that positive oil price shocks sharply lead to formation of oil price volatility, but, the negative price shocks will slightly reduce oil price volatility. Iranian economic growth is affected by this volatility under three different behavior regimes. If the economy switch to one of the regimes (low, medium, high economic growth), the probability of transition between these regimes and their duration is different. So, oil price volatility as a reason for low economic growth in Iran may cause the economy switch to its lower situation.
Abbass Memarzadeh, Ali Emami Meibodi, Hamid Amadeh, Amin Ghasemi Nejad,
Volume 4, Issue 14 (12-2013)
Abstract
Abstract
Forecasting of crude oil price plays a crucial role in optimization of production, marketing and market strategies. Furthermore, it plays a significant role in government’s policies, because the government sets and implements its policies not only according to the current situation but also according to short run and long run predictions of important economic variables like oil price. The main purpose of this study is modeling and forecasting spot oil price of Iran by using GARCH model and A Gravitational Search Algorithm. Performed forecasts of this study are based in static and out-of-sample forecasting and each subseries data is divided in to two parts: data for estimation and data for forecasting. The forecast horizon is next leading period and its length is one month. In this study the selected models for forecasting spot oil of Iran are GARCH(2,1) and a Cobb Douglas function which is functional of prices of 5 days ago. Finally, the performances of these models are compared. For comparison of these models MSE, RMSE, MAE, and MAPE criteria are used and the results indicate that except in MAPE criterion, the mentioned criteria are smaller for GARCH model in comparison to GSA algorithm.
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Volume 5, Issue 16 (7-2014)
Abstract
Iran’s share of world exports has not been great in recent years and the development of non-oil exports such as exports of industrial goods in order to reduce the economy's dependence on oil revenues made necessary. The real exchange rate is one of the most important variables affecting exports. In this context, investigate the effect of the real exchange rate volatility on different variables such as the export is important. The main objective of this paper is to investigate the impact of real exchange rate volatility on exports of Iran Industrial goods over the period of 1968-2010. To that end, The real exchange rate volatility index has been estimated incorporating with EGARCH (0,1) model than we using co-integration of Saikkonen & Lutkepohl and FMOLS to investigate the impact of the real exchange rate volatility index, along with other variables of model exports of industrial goods have been evaluated.
The main empirical finding of this paper show that the real exchange rate volatility variables and export prices have negative and significant effects on exports of industrial goods and variables GDP’s world, GDP’s Iran and trade of openness have positive and significant effects on exports of industrial goods. The empirical findings of this paper, The beneficial implications for investors and Policy makers needs to recognize the exact effects of exchange rate volatility on exports of industrial goods are provided.
Shahram Fattahi, Kiomars Sohaili, Hamed Abdolmaleki,
Volume 5, Issue 17 (10-2014)
Abstract
The fluctuations in the oil price with uncertainty, as an exogenous variable, is the most important factor affecting the fluctuations in the GDP of the countries especially OPEC. This study examines the effect of oil price uncertainty on the Iran’s GDP growth using the seasonal data for the period 1988(1)-2011(4). The model used in this study is the asymmetric VARMA, MVGARCH-M and the estimated method is quasi maximum likelihood (QML). The results indicated that there is a negative and significant relationship between oil price and economic growth over the period. Furthermore, the results show that the conditional variance-covariance process underlying output growth and change in oil price exhibits non-diagonality and asymmetry.
Hossein Asgharpur, Firouz Fallahi, Naser Sanoubar, Ali Rezazadeh,
Volume 5, Issue 17 (10-2014)
Abstract
The main goal of this research is to calculate VaR index with parametric Markov-Switching GARCH approach for accepted companies in Tehran Stock Exchange and also selecting the optimal portfolio of their stocks. To calculate the index, data and information of weekly stock price of 10 representative firms during the period 2008-2014 has been used which account for 332 working weeks.
The results from estimation of VaR and determination of optimal stock portfolio in the non-linear programming framework showed that optimal portfolio of food-industry companies stock, in the context of VaR has higher returns and risk in the first regime (Boom period) compared to the second regime ( recession period). On the other hand, it has had lower weight in both stock portfolios that had lower average returns compared to the rest of the stocks and compared to the stocks which had lower VaR relative to other stocks that has higher weights.
The Kupiec and Lopez back testing using 10 future week data, showed that both of approaches is valid but the parametric approach has better rank. Therefore the optimal portfolios of stocks under parametric VaR will be accepted as final optimal portfolio.
Nooshin Bordbar, Ebrahim Heidari,
Volume 8, Issue 27 (3-2017)
Abstract
The present article studies the interactive relationships between oil price volatility and industries stocks of basic metals, petroleum and chemical products by using Vector Auto Regressive (VAR) and Multivariate Generalized Autoregressive Conditional Heteroskedastisity (GARCH) models from March 2004 to March 2015 empirically . In this research, the VAR-GARCH model is proposed, which is developed by Ling and McAleer (2003). The model survives the return and volatility problems among the considered series and this is the VAR-GARCH advantage. The results show that there are Average effects between oil market and stocks market of basic metals and petroleum products, But this effects are not true for chemical industry market. The volatility effects between world oil price and chemical and basic metals industry markets is not existed, but between oil market volatility and petroleum products stock volatility, Significant negative relationship is existed. There for, the investors should reduce their portfolios basket dependences on oil price as much as possible.
Hamed Abdolmaleki, Hossein Asgharpur, Jafar Hghighat,
Volume 8, Issue 28 (7-2017)
Abstract
Money supply and velocity of money are important variables that affect inflation and product. Velocity of money is a key concept for economic policy, and it's getting more important since it is closely related to behavior of the demand for money. In this regard, Friedman believes that the volatility of money growth is the main factor of velocity of money, which in monetary economics literature is known as Friedman’s monetary volatility hypothesis. The purpose of this study is to explore and explain the fluctuations in the velocity of mony from the perspective of Monetarism. In this regard, using Iran’s economic quarterly data for the period 1988(3)-2015(1) and in the framework of causality test, the Friedman hypothesis based on the impact of volatility of money growth on velocity of money is tested for monetary aggregates (M1 and M2). The model used in this paper is extended VARMA, GARCH-M and the estimated method is quasi maximum likelihood (QML). The results support the Friedman hypothesis for the period under study; in other words, there is a causal relationship from money growth volatility to velocity of money.
Seyed Ali Naseri, Farkhondeh Jabal Ameli, Sajad Barkhordary Dorbash,
Volume 11, Issue 41 (10-2020)
Abstract
Systemic risk arises from simultaneous movement or correlations between market segments; Thus, systemic risk occurs when there is a high correlation between the risks and crises of different market segments or institutions operating in the economy, or when the risks of different segments in a market segment or a country are related to other segments and other countries. This paper presents a measure of systemic risk calculation to effectively describe the systemic importance of each financial institution in a system. The DCC-GARCH methodology with normal and t-student distributions has been used to examine the correlation of time-varying banks. The results of this section show that the application of DCC-GARCH-student-t model is preferable to DCC-GARCH-normal model. In order to investigate the presence of leverage effect, GJR-GARCH model was used and the results of estimation showed the presence of asymmetry and the absence of leverage effect in the data. In the study of dynamic conditional correlation between selected banks, it is also observed that α_C ,β_C are not significant for both estimation cases. Therefore, in both cases, it is estimated based on the normal distribution and t-student α_C=β_C=0 and the conditional correlation becomes constant. Based on the results of shapley value and in order to allocate the total risk between the banks in the sample, Parsian, Mellat, EN, Tejarat and Saderat banks have the most systemic importance for the period of June 17, 2009 to May 7, 2019.
