Behavioral economics
Taha Shishegari; Farhad Ghaffari
Abstract
Conventional economics posits that the presence of arbitrage in financial markets forces market participants to act rationally in order to maximize profits. This assumption underpins the efficient market hypothesis (EMH). However, in recent years, behavioral economics has challenged the assumption of ...
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Conventional economics posits that the presence of arbitrage in financial markets forces market participants to act rationally in order to maximize profits. This assumption underpins the efficient market hypothesis (EMH). However, in recent years, behavioral economics has challenged the assumption of market efficiency and rational behavior by demonstrating the significant impact of seemingly irrelevant factors (e.g., weather conditions, air temperature, and pollution) on financial markets. The present research aimed to compare the explanatory power of these two perspectives by analyzing daily data from the Tehran Stock Exchange index over two periods: February 20th, 2022 to February 19th, 2023, and February 20th, 2023 to February 19th, 2024. The study relied on the daily data on the growth rate of the dollar as an explanatory variable for the total stock market index growth from a conventional economics perspective. From a behavioral economics viewpoint, the analysis incorporated variables such as air temperature, weather conditions, and the pollution index. Given the nature of financial markets, the study used the EGARCH method for analysis. The results indicated that during the period from February 20th, 2022 to February 19th, 2023, when the dollar rate exhibited a significant upward trend, the explanatory power of behavioral variables decreased, with some even losing their significance in explaining the total stock market index. However, during the period from February 20th, 2023 to February 19th, 2024-when the exchange rate remained relatively stable-behavioral variables had a significant impact on the total stock market index. Introduction In the conventional economics perspective, which has long dominated the analysis of financial markets, actors are assumed to behave rationally. This means that they adjust their beliefs accurately (according to Bayes’ rule), aligning their subjective probabilities with reality and making decisions based on expected utility. However, in recent decades, the deviation of conventional economics theories from empirical data—along with the emergence of large, persistent, and severe price bubbles in financial markets—has led a group of economists to question the explanatory power of conventional theories and the assumption of rational behavior in financial markets. The present study aimed to address the duality between conventional and behavioral economics within the context of Iran’s developing economy. Focusing the country’s unique economic conditions, the study sought to determine which perspective—conventional or behavioral economics—provides a better explanation for stock market behavior. Two distinct time periods were analyzed: 1) from February 20th, 2022 to February 19th, 2023, during which the exchange rate (U.S. dollar) nearly doubled as a representative variable of the macroeconomic situation; and 2) from February 20th, 2023 to February 19th, 2024, when the exchange rate remained relatively stable, increasing by about 40%. The impact of behavioral variables on stock market returns was examined in two scenarios: one characterized by significant changes in macroeconomic variables and the other by more moderate changes. Conventional economics suggests that humans act rationally when the data is clear and the analysis is straightforward. However, as complexity increases and data becomes less clear, individuals tend to deviate from rational behavior due to limited rationality (Thaler, 2009). This hypothesis was tested by focusing on the two time periods of the study. In the first period, when macroeconomic variables exhibited a clear and specific trend, conventional economic theories were expected to provide a more accurate explanation of stock market behavior, with the influence of behavioral variables likely to decrease. Conversely, in the second period, when macroeconomic variables lacked a clear direction, it was anticipated that behavioral economics—along with variables rooted in psychological influences and the internal states of actors—would offer a better explanation for stock market performance. In this study, environmental variables such as air temperature, atmospheric conditions, and air pollution were considered representative of behavioral variables. The analysis investigated the impact of behavioral variables on the Tehran Stock Exchange index. Materials and Methods Financial sector data often exhibit heteroskedasticity, which makes the use of linear structures for estimation and modeling problematic. Additionally, fluctuations in financial data tend to cluster, indicating that the variance is self-explanatory. These characteristics make ARCH and GARCH models particularly suitable for modeling in this context. When using ARCH and GARCH models, it is essential for the estimated coefficients to be non-negative, which can present challenges in the estimation process. To address this issue, EGARCH models, which is the logarithmic form of the GARCH model, can be employed. This approach eliminates the need to impose the non-negativity condition on the variance coefficients. The current study estimated the daily growth rate of the total index of Tehran Stock Exchange over two separate time periods: from February 20th, 2022 to February 19th, 2023, and from February 20th, 2023 to February 19th, 2024. The analysis applied the AR(1) model and incorporated both behavioral and conventional variables into the variance component of the model to explain fluctuations in the total index efficiency. Results and Discussion During the first period (February 20th, 2022 to February 19th, 2023), the exchange rate experienced a clear and significant increase of 100%. Market players, adhering closely to conventional economic theories, operated under the assumption of rational and optimizing behavior. As a result, the exchange rate variable became more effective in explaining market fluctuations, while some behavioral variables, such as climate and air pollution, lost their explanatory power in the variance equation. In the second period (February 20th, 2023 to February 19th, 2024), the conventional variable (currency growth rate) became less significant and transparent. Market players increasingly relied on behavioral variables, which offered a better explanation for fluctuations in the total stock market index. The estimated coefficient for the conventional variable (foreign exchange growth rate) lost its significance during this period. The results showed that air temperature had a negative and significant impact on fluctuations in the growth index during both periods, consistent with the findings of previous studies. Conclusion This study analyzed two distinct economic periods: one marked by significant growth in foreign exchange rates, and the other characterized by relative stability in the foreign exchange market. The objective was to examine the behavior of financial actors and compare the explanations provided by conventional and behavioral perspectives on financial markets using the available data. According to the results from the two estimated models, the exchange rate growth (as the representative variable of the conventional view) had a significant and positive impact on stock index fluctuations during the first period, when exchange rates exhibited a clearly upward movement. However, this variable lost its significance in the second period, when exchange rates remained relatively stable. During the second time, the explanatory power shifted to behavioral variables such as weather conditions, pollution, and air temperature.
international trading
Seyedeh Marveh Nasersadrabadi; Farhad Ghaffari; Teymour Mohammadi; Abbas Memarnejad
Abstract
The negative consequences of financial crises require the attention of economic policymakers and decision making centers.Therefore, considering the importance of the subject, the present study has investigated the effects of global financial crises on the trade patterns of Iran and its partners during ...
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The negative consequences of financial crises require the attention of economic policymakers and decision making centers.Therefore, considering the importance of the subject, the present study has investigated the effects of global financial crises on the trade patterns of Iran and its partners during the years 1995-2018.The variables have been estimated in the framework of the gravity model using the pseudo poisson maximum likelihood method.The findings show that the Asian financial crisis (1997) has an effective role in reducing the volume of trade but this result is the opposite in relation to the American financial crisis (2007); Because instead of a threat, it has become an opportunity for the movement of business flows. In this situation, it seems that the difference in the intensity and type of impact of financial crises on trade patterns can be affected by the nature of the crisis or the region where the crisis started.1.IntroductionCountries consistently grapple with economic and financial turmoil, which at specific junctures, can escalate into full-fledged financial crises (Moshiri & Nadali, 2013). These crises manifest as conditions where a significant number of financial institutions suddenly experience a substantial decline in the nominal value of their financial assets (Bonis et al., 1998). Given the historical recurrence of financial crises worldwide, it becomes imperative for economic policymakers and decision making centers to address and mitigate their adverse effects. This necessity stems from the detrimental impact that financial crises have on the real sector of economy (Kord-Zangeneh et al., 2019). Compounding the issue is the transnational nature of these crises, as they may transfer from one country to another. The transmission of financial crises occurs through various channels, including trade flows, foreign direct investment, commercial loans and financial aid (Massa & Velde, 2008). Among these channels, trade relations emerge as crucial communication pathways on a global scale, playing a pivotal role in influencing the performance of diverse economic sectors affected by financial crises.Hence, understanding international trade patterns holds greater significance than any other phenomenon in the economy, particularly in times of crisis. Furthermore, drawing insights from the experiences of other nations aids in understanding how trade flows unfold in countries that have recently weathered financial crises (Santana-Gallego & Perez-Rodriguez, 2018). Financial crises impact on the trade in two ways. First, they exert a negative influence on trade by disrupting the trade balance. Then crises transfer from one affected country to another through interconnected trade links. Consequently, the extent to which different countries engage with the global economy dictates the degree to which they are affected by the repercussions of a financial crisis.As countries are interlinked through trade flows, in the event of a shock impacting one economy, it has the potential to extend to the entire network, indirectly influencing trade relations between countries. This connection is particularly crucial because a financial crisis can transfer to other economic sectors through fluctuations in exchange rate variables, exports, imports and changes in international commodity prices (Brave & Butters, 2011).2.Materials and MethodsThe gravity model, proposed by Tinbergen (1966) to explain bilateral trade flows, is distinctive for its emphasis on reflecting international relations. In the field of international trade studies, traditional challenges arise in estimating the gravity model. Specifically, when employing the ordinary least squares method for estimating the gravity model, there is a tendency to exclude zero statistical observations. This limitation stems from the conventional method’s inability to compute a logarithm for the trade variable when trade between countries is not realized in certain years. Consequently, the omission of statistical observations in such instances renders it impossible to generate a zero logarithm. Moreover, when the model is estimated using the non-linear least squares method, there is a potential issue with the heterogeneity of variance, which can compromise the accuracy of interpretations based on the coefficients. Recognizing this challenge, Santos-Silva and Tenreyro (2006) introduced the Poisson pseudo maximum likelihood method to address the estimation of such models. A noteworthy aspect of this method is the non-elimination of zero statistical observations, ensuring unbiased and reliable estimation of variable coefficients. This is achieved by assigning equal weight to all statistical observations. Therefore, the method not only increases the number of statistical observations but also enhances the efficiency of the estimator.The main estimation approach revolves around the Poisson pseudo maximum likelihood method, as explained by theoretical foundations and existing literature that detail the connection between financial crises and international trade. This approach draws inspiration from the works of Santos-Silva and Tenreyro (2006) as well as of Santana-Gallego and Perez-Rodriguez (2018). In this respect, the research model was delineated following the model proposed by Glick and Rose (2016), as shown in Equation (1). (1) The present study aimed to investigate trade patterns by examining the volume of bilateral trade (total export and import) between Iran and its twenty trading partners. The analysis used annual data spanning from 1995 to 2018. The study developed the proposed model, leveraging the flexibility inherent in the gravity model as well as incorporating variables such as the logarithm of the Linder economic similarity index, the logarithm of Iran’s and its trading partners’ populations, the logarithm of the nominal exchange rate, the logarithm of geographical distance and financial crises. This comprehensive formulation is defined as the generalized gravity model expressed in Equation (2). (2) 3.Results and DiscussionDiagnostic tests are imperative before model estimation. Initially, the Chow test was employed to determine the suitable regression method. Subsequently, the Hausman test was used to decide between the methods of fixed effects or random effects.Table 1. Diagnostic testsResultProbabilityStatisticsTestNull Hypothesis Rejected0.00023.04ChowNull Hypothesis Rejected0.000437.51HausmanSource: Research findings The results outlined in Table 1 demonstrate the rejection of the null hypothesis in both the Chow and Hausman tests. To control the multilateral resistance to trade, the study estimated the coefficients of the variables by considering the country’s annual fixed effects. The process was conducted within the framework of the gravity model, employing the Poisson pseudo maximum likelihood method (see below).Table 2. Model estimation resultsProbabilityStandard deviationCoefficientsVariables***0.0000.000-0.179CRIijt 1997***0.0000.0000.135CRIijt 2007***0.0000.184-32.358LnLINijt***0.0005.9153.005LnPOPit***0.0000.1000.111LnPOPjt***0.0000.0000.017LnERijt***0.0000.000-0.400LnDISijt***0.0000.113-52.430CNumber of obs = 240R-Squared = 0.81Pseudo Log Likelihood = -171.405*** Indicates the significance of the coefficients at the level of 1 percent.Source: Research findingsThe results provided in Table 2 reveal that global financial crises exerted a significant impact on the trade volume, yet the nature of their influence on trade patterns varies. Specifically, the findings indicated that the Asian financial crisis of 1997 played a substantial role in reducing the trade volume, while the outcome was opposite in the case of the American financial crisis of 2007.The negative coefficient in the logarithm of the Linder economic similarity index indicates that the volume of trade increases as the per capita income difference decreases. Consequently, the countries with similar tastes or demand structures become the optimal markets for a country’s export goods.Conversely, the positive coefficient in the logarithm of the population of Iran and its trading partners signifies that an increase in population correlated with a rise in the trade volume. This association can be attributed to the utilization of a larger labor force, inherent in higher population figures, which positively affects the production of goods. The outcome is manifested in an increase in the trade volume.The positive coefficient in the logarithm of the nominal exchange rate indicates an increase in trade volume corresponding to an increase in this variable. This pattern emerges because foreign goods become more expensive compared to domestic ones. Consequently, both domestic and foreign consumers are inclined to substitute Iranian goods with foreign alternatives. Conversely, the negative coefficient in the logarithm of geographical distance reveals that this variable exerted a negative impact on the trade volume. In other words, the greater the distance between countries, the higher the transportation costs. As a result, distant markets become less attractive for establishing trade relations.4.ConclusionThe present study examined the effects of global financial crises on the trade patterns of Iran in relation its key trading partners. In this respect, the research used annual data from the studied countries during 1995–2018, then the coefficients of the variables were estimated within the framework of the gravity model as well as the Poisson pseudo maximum likelihood method.According to the findings, the examined countries experience the repercussions of financial crises, yet the magnitude and nature of their impact differ based on the specific characteristics of each crisis. In this context, the Asian financial crisis of 1997 played a significant role in reducing the trade volume in the countries under consideration, while the outcome was opposite in the case of the American financial crisis of 2007. Moreover, the positive coefficients of the variables specifically the logarithm of the Linder economic similarity index, the logarithm of the nominal exchange rate and the logarithm of the population of Iran and its trading partners underscore their favorable impact on the trade volume aligned with the increased trade flows in the countries. Given the negative coefficient in the logarithm of geographical distance, it is anticipated that trade with countries farther away from Iran will be comparatively lower. In fact, the majority of Iran’s trade relations are established with neighboring countries. In light of these findings, it is recommended to implement trade policies that support export-oriented domestic production in the country. This approach, in addition to generating foreign currency income, can serve as a mitigating factor against the adverse effects of financial crises.
zahra sadat raeisi gavgani; Teimour Mohammadi; farhad qhaffari; Abas Memar Nejhad
Abstract
The purpose of this paper is to investigate the existence of nonlinear effects of the fiscal policy. Specifically, the asymmetric effects of equal fiscal shocks (of government spending) on macroeconomic production variables are studied. In this regard, a Dynamic Stochastic General Equilibrium (DSGE) ...
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The purpose of this paper is to investigate the existence of nonlinear effects of the fiscal policy. Specifically, the asymmetric effects of equal fiscal shocks (of government spending) on macroeconomic production variables are studied. In this regard, a Dynamic Stochastic General Equilibrium (DSGE) model consistent with the conditions of the Iranian economy during the period 1369 – 1393 is used. We present theoretical foundations of the asymmetric effects of fiscal shocks on macroeconomic variables, then we refer to two strands of studies; the first one emphasizes nonlinearity in the effect of fiscal policy, and argues that nonlinear effects are associated with large and persistent fiscal impetus for industrial and developing countries. The second strand of studies emphasizes expectations about fiscal adjustment for debt sustainability during large fiscal adjustments rather than in normal times. The results show that the positive and negative impacts of government expenditures have asymmetric effects on macroeconomic variables. The effect of negative shock of government spending on consumption, investment and production of the private sector as well as total production is stronger, more stable, and larger. On the other hand the effect of positive government spending shock on these variables is smaller having more temporary impact.
