Financial Economics
Saman Hatamerad; Bahram Adrangi; Hossein Asgharpur; Jafar Haghighat
Abstract
The present research aimed to investigate the relationship between Iran’s stock price index and nine macroeconomic variables during 1996–2019. Three methods were employed to reduce uncertainty, namely three Bayesian averaging methods (BMA, BMS, BAS), weighted average least squares (WALS), ...
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The present research aimed to investigate the relationship between Iran’s stock price index and nine macroeconomic variables during 1996–2019. Three methods were employed to reduce uncertainty, namely three Bayesian averaging methods (BMA, BMS, BAS), weighted average least squares (WALS), and Vselect. The experimental results of the Bayesian methods and WALS showed that the exchange rate and the consumer price index are the most important variables among the nine macroeconomic variables considered in the model. Moreover, the results revealed that the exchange rate has a minor impact on the stock price index, while the stock price index exerts a substantial effect on the exchange rate. The findings of Vselect validated the conclusion that these two variables are the primary drivers of stock price estimation and are present in nearly all predictive modelsIntroductionThe harmonization of financial markets with the macroeconomic sector is crucial for stabilizing the economy and achieving the adopted policies. In recent years, several significant studies have been conducted on financial markets, particularly the stock market, highlighting their pivotal role in allocating capital resources efficiently in advanced economies. Empirical evidence supports the view that financial markets have evolved in tandem with all sectors of the economy. Therefore, it can be argued that financial markets constitute one of the most vital components of any country’s economy. Throughout history, major economic crises have resulted from the collapse of financial markets, which underscores their critical significance. The financial market comprises several components, with the stock market being a crucial part. Economists view it as a barometer of a country’s economic health due to its ability to reflect macroeconomic asset prices more accurately than other markets. The uncertainty surrounding stock prices in stock markets is a significant aspect of the entire economy, capable of generating and disrupting unsustainable growth. For investors, the risk of participating in an investment is a crucial consideration. To comprehend total risk, it is beneficial to examine two aspects: systematic and non-systematic risk.The present study aimed to examine the impact of economic factors on stock market prices in Iran with the high degree of risk involved. There is a consensus among economists that asset prices are responsive to economic news, and that stock prices and economic factors are strongly interconnected. Thus, this research investigated the potential impact of macroeconomic factors on the Iranian stock price index from 1996 to 2019 using Bayesian averaging methods, followed by an analysis of the effect size of each variable through the weighted average least square method (WALS).Materials and MethodsResearchers often draw conclusions based on the assumptions of their selected model, assuming that it can accurately predict real-world situations. However, this approach may overlook true uncertainty, leading to non-conservative conclusions. Statistical models comprise two parts: variables and assumptions, and the model selected based on these assumptions to estimate the variables. Uncertainty exists at both levels. For instance, a researcher estimating the impact of influential factors on an independent variable may choose a model based on their assumptions and report their estimates. But is this the best answer? Another researcher with different assumptions may opt for a different model with lower variance and error. In other words, numerous models may fit the sample data equally well but with different coefficient estimates and standard errors. Bayesian model averaging (BMA) is a robust method that aims to remove uncertainty. It assesses the robustness of results to alternative specifications by computing posterior distributions for coefficients and models. This study employed three models of BMA, BMS, and BAS, using various averaging methods to verify the reliability of the results. Moreover, two non-Bayesian methods, namely WALS and Vselect, were used to select the best variables for predicting the optimal models.ConclusionThis study tried to investigate the relationship between Iran’s stock market index and nine macroeconomic variables during 1996–2019 by using the models that identify and limit uncertainty. The models selected include three Bayesian averaging models as well as WALS and Vselect which were used to verify the results obtained. The results indicated that only two variables, the exchange rate and consumer price index, are statistically significant when assuming a uniform distribution of the prior distribution function, which is the assumption of the BMS method. The remaining variables are not statistically significant. Furthermore, the estimates derived from the BMA and BAS models were quite similar, with the exception of less important variables. However, the similarity decreased in the BAS method. Moreover, WALS and Vselect confirmed the results obtained from all the three methods.
Monetary economy
Seyed Saleh Akbar Mousavi; Behzad Salmani; Jafar Haghighat; Hossein Asgharpour
Abstract
The main purpose of this study is to estimate the probability of banking crisis using the second generation of early warning systems (logit models), for 13 selected high-middle income countries over the period of 1980-2016. In this regard, two types of logit models; binomial and multinomial, are estimated. ...
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The main purpose of this study is to estimate the probability of banking crisis using the second generation of early warning systems (logit models), for 13 selected high-middle income countries over the period of 1980-2016. In this regard, two types of logit models; binomial and multinomial, are estimated. The results of estimated binomial logit model show that three leading indicators of the crisis are broad liquidity ratio, stock price index and inflation, which are the main causes of crisis in the studied countries. These variables account for about 17 percent of the probability of a banking crisis. Then, to avoid post-crisis bias, the multinomial logit model is estimated. The empirical results confirm that above three leading indicators are warning. Also, among the above three variables, only stock price index variable with a probability of 12.68%, causes the economy to exit the banking crisis and change its situation from the crisis/recovery period to the tranquil period. The multinomial logit model exhibit significantly better in-sample predictive abilities than the binomial logit model.
Firouz Fallahi; Hossein Asgharpur; Sajjad Abdollahzadeh
Abstract
In this study, the continuous wavelet transformation approach is employed to test the dynamics of the causality between two principal inflation indices i.e. consumer price index (CPI) and producer price index (PPI) based on monthly data from 1990:5 to 2013:12 for the Iranian economy. Analyzing the dynamics ...
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In this study, the continuous wavelet transformation approach is employed to test the dynamics of the causality between two principal inflation indices i.e. consumer price index (CPI) and producer price index (PPI) based on monthly data from 1990:5 to 2013:12 for the Iranian economy. Analyzing the dynamics of causality between these two inflation indices could provide significant policy implications. One of the dominant aspects of this approach compared with the conventional causality tests is that it has a higher ability in analyzing dynamics of causality between time series. The wavelet, as a band pass filter to the time series, is stretched in time by varying its scale, which makes it possible to show the short run and long run causalities between the time series. In this paper, we apply the continuous wavelet transformation to study the inflationary cycles of the Iranian economy. The results confirm both demand-pull and cost-push nature of inflation by indicating bidirectional causality between the CPI and PPI. In fact, the causality directions vary over time depending on different economic conditions.
Parviz Mohammadzadeh; Hossein Asgharpur; Mohammad Bagher Beheshti; Ali Rezazadeh
Volume 16, Issue 49 , February 2012, , Pages 151-175
Abstract
The main objective of this paper is to examine of monetary approach to exchange rate determination in 14 MENA countries during 1975-2006. For this purpose, panel cointegration technique has been used to test the basic monetary model and flexible price monetary model of exchange rate determination. The ...
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The main objective of this paper is to examine of monetary approach to exchange rate determination in 14 MENA countries during 1975-2006. For this purpose, panel cointegration technique has been used to test the basic monetary model and flexible price monetary model of exchange rate determination. The empirical results indicate that there is a cointegration relationship between variables of basic and flexible price monetary models and therefore the monetary model is able to convincingly explain the exchange rates in MENA Countries. Hence, it could be concluded in the region the parity domestic currency vs. foreign currencies has been affected by mostly the amount of domestic money and increase (decrease) of amount of domestic liquidity leads to devaluation (evaluation) of domestic money. Also, the results show that there is negative significant relationship between exchange rate and output and positive significant relationship with expected inflation rate in Middle East and North Africa countries. This empirical evidence implies that in addition to liquidity, variables such as expected inflation rate and GDP are important in determination of the equilibrium exchange rate in MENA Countries.
