Econometrics
Mohammad Feghhi Kashani; Teymor Mohammadi; zahra Aghighi
Abstract
One of the key challenges in empirical studies relates to the identification of the dynamics of bubbles that periodically run up and collapse. This study is an attempt in this field, which initially examines some limitations of one of the relatively new methods in the economic literature as to the identification ...
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One of the key challenges in empirical studies relates to the identification of the dynamics of bubbles that periodically run up and collapse. This study is an attempt in this field, which initially examines some limitations of one of the relatively new methods in the economic literature as to the identification of rational bubbles in the Tehran Stock Exchange for the period of 2009-2020. Then, by assuming the Markov switching regime approach in this area, we have extended the conventional method by taking into account the dynamic interaction of asset prices in the market with the latent factor in the process of bubbles expansion and collapse. It is shown how this framework, while improving the efficiency of detecting financial bubbles through mitigating the specification error of dynamic models compared to existing alternative methods, is capable of incorporating the feature of traders' interactions in the market with no specific assumptions on how they interact, especially with regard to the coordination of their expectations and pursuant trading behavior. The findings resulting from this method indicate the existence of a bubble in asset prices only for the period 2018-2020, as opposed to the use of the conventional method, which implies either no bubble or the existence of two bubbly periods 2012-2014 and 2018-2020. in the Tehran Stock Exchange.
Econometrics
Mohammad Reza Salehi Rad; Manijeh Mahmoodi
Abstract
The modeling is a very important topic in economic and financial research and it has a basic role in the analyzes, decisions, the policies and planning. In the modeling, assumptions have an important role in estimation and forecasting, because they can affect the results of models and analyses. The one ...
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The modeling is a very important topic in economic and financial research and it has a basic role in the analyzes, decisions, the policies and planning. In the modeling, assumptions have an important role in estimation and forecasting, because they can affect the results of models and analyses. The one of the most widely used classical time series models is the autoregressive model, where the current values are the finite linear combination of its past values. On the other hand, in real problems, many variables affect each other. For this reason, the vector time series models are used, which are part of the multivariate time series. The Vector autoregressive models are used in economic and financial modeling. The vector autoregressive (VAR) models are usually considered with the normal distribution for the shocks (noises). Since, in economic and financial issues, especially macroeconomics, the shocks don’t have symmetric distribution. In this paper, the VAR model with the Multivariate Skew Normal (MSN) distribution for the shocks is considered and since, the estimation of the parameters is an important step in modeling, the parameters of the model are estimated by using the Expectation Conditional Maximization (ECM) algorithm. Finally, by using the real data sets of Canada and Iran where the shocks have skewness and the evaluation criteria of the models, it is shown that the VAR model with MSN distribution for shocks in these data is more efficient than the VAR model with the multivariate normal distribution for shocks.
Econometrics
Morteza Khorsandi; Teymor Mohammadi; Hamidreza Arbab; Emadodin Sakhaei
Abstract
Macroeconomic policy analysis and risk management require taking account of the increasing interdependencies across markets and economies. National economic issues need to be considered from global as well as domestic perspectives. This invariably means that many different channels of transmission must ...
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Macroeconomic policy analysis and risk management require taking account of the increasing interdependencies across markets and economies. National economic issues need to be considered from global as well as domestic perspectives. This invariably means that many different channels of transmission must be taken into account. This paper investigates the effect of global economic shocks on Iran’s economy. The Global Vector Autoregressive (GVAR) model for the first quarter of 1990 to the fourth quarter of 2019 is used for 34 countries, which cover about 90% of world gross domestic products. According to previous studies and the results of this study, it is found that only the shocks of the United States, China and the global shock affect the macroeconomic variables of other countries and oil prices, and as a result, the effect of these three shocks on the Iranian economy is investigated. Ceteris paribus, the results show that China's shock affects the variables of GDP and Iran's inflation: with a 1 percent increase in China's GDP, Iran's GDP increases by 0.08 percent and inflation by 1.2 percent and has no effect on interest rates. The US shock has an indirect effect on oil prices. Due to the isolation of the economy, foreign variables do not have significant effects on the Iranian macroeconomic variables. In general, Iran's economy, due to the size of the economy and the volume of trade shocks of other trading partners through the foreign trade channel do not affect the Iranian economy.
Econometrics
Mojtaba Rostami; Seyed Nezamuddin Makiyan
Abstract
Stock returns forecasting is very crucial for investors, share-holders and arbiters. Different methods have been developed for this purpose. In general, there are four methods of forecasting in stock markets, which are; Technical Analysis, Fundamental Analysis, Traditional Time Series and Machine Learning. ...
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Stock returns forecasting is very crucial for investors, share-holders and arbiters. Different methods have been developed for this purpose. In general, there are four methods of forecasting in stock markets, which are; Technical Analysis, Fundamental Analysis, Traditional Time Series and Machine Learning. This study is classified in the third category that is a time series prediction in which the values of a variable are predicted over time. Studies which have been done so far indicate that most of them concentrate on Neural Networks and Genetic Algorithm which are in Machine Learning class and none of them uses Bayesian approach or Exponential Smoothing and Box Jenkins techniques placed in the group of time series forecasting. This paper focuses on forecasting with time series methodology for predicting and comparing the results of the Bayesian, Exponential Smoothing and Box Jenkins methods together. In fact, the difference between this study and others is the comparison of the mentioned methods for stock return forecasting. The period of investigation was 2018- 2020, which covers daily frequency structure. Results, indicated that Bayesian method, based on the Root Mean Square Error (RMSE) criterion is the best technique for the prediction of stock returns. This is because, in addition to information derived from data, this method also uses other sources of information such as non-sample information or vague prior density as well for forecasting. Results illustrate the importance of considering the Bayesian approach in predicting stock market returns.
Econometrics
Abbas Shakeri; Teymor Mohammadi; Zinat Zakeri
Abstract
The expansion of the globalization process has increased the relationships among financial markets in different countries, which itself has motivated investors to move among them to make more profit. Given the situation in Iran after sanctions, the possibility of investing in well-known financial markets ...
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The expansion of the globalization process has increased the relationships among financial markets in different countries, which itself has motivated investors to move among them to make more profit. Given the situation in Iran after sanctions, the possibility of investing in well-known financial markets is facing with the risk of sanctions. The present study aims to evaluate the existence of volatility spillover among the financial markets of Iran and Islamic oil exporters countries. To this aim, a multivariate factor stochastic volatility (SV) model and stock price index data were used with daily frequency for the period 12/05/2008-02/19/2020. Based on the results, the main hypothesis that the volatility spillover among the financial markets of OPEC oil-exporting Islamic countries follows a common and uniform random trend is accepted for the United Arab Emirates, Saudi Arabia, and Qatar, but not for Iran and Nigeria. Therefore, diversifying the portfolio for Iranian investors in the financial markets of OPEC Islamic oil exporters can reduce the investment risk in the long run which make such economies an appropriate investment destination for Iranians due to the conditions of sanctions.
