Financial Economics
Iman Dadashi; Vahid Omidi
Abstract
Considering the impact of global variables on stock market industries, the present study relied on the Quantile-on-Quantile Connectedness (QQC) and Structural Vector Autoregression (SVAR) to examine the impact of geopolitical risk fluctuations on the volatility of the petroleum products, chemical products, ...
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Considering the impact of global variables on stock market industries, the present study relied on the Quantile-on-Quantile Connectedness (QQC) and Structural Vector Autoregression (SVAR) to examine the impact of geopolitical risk fluctuations on the volatility of the petroleum products, chemical products, metal ores, and basic metals sectors in the Tehran Stock Exchange. The analysis focused on the period from January 1, 2020, to December 24, 2024. The results from the QQC model revealed that fluctuations in geopolitical risk exhibited the strongest correlation with the volatility of the petroleum products industry index at extreme deciles, indicating a significant impact. In other industries, the highest susceptibility to geopolitical risk fluctuations had occurred when their volatility was in the 9th and 10th deciles. In addition, the SVAR model results indicated that the immediate response of industry index volatility to geopolitical risk shocks was positive across all cases. Over 360 periods, this response converged to a positive value, reflecting the persistence of the shock. The cumulative response analysis further demonstrated an exponential increase in all industries, suggesting a rising trend in the effect of geopolitical risk over time. Specifically, after 360 periods, the volatility of the petroleum products industry index increased by 0.34, chemical products by 0.06, metal ores by 0.03, and basic metals by 0.06.IntroductionRecently, the Tehran Stock Exchange (TSE) has been grappling with various risk factors, including the government budget, uncertainties in domestic and foreign policies, the Al-Aqsa Storm and Promise Fulfilled operations, interest rates, the exchange rate, and inflation. Notably, the TSE has not consistently mirrored the behavior of global markets across different periods. For instance, at the height of the COVID–19 pandemic, when most global stock markets experienced significant downturns, the TSE reached historic record highs. Conversely, at times when global markets were on the rise and commodity prices increased, the TSE entered a decline. This divergence was primarily due to internal risks unique to the TSE, which prevented the domestic market from benefiting from global market growth. The present study aimed to examine the impact of geopolitical risk fluctuations on the price index volatility of selected industries listed in the TSE. The industries were selected based on their specific characteristics and their sensitivity to geopolitical risks.Materials and MethodsThe study employed the Quantile-on-Quantile Connectedness (QQC) model to examine the relationship between the overall stock index and Islamic Treasury Bonds (Sukuk). To this end, the QVAR(P) model, which enables the estimation of relationships across different quantiles, is utilized as follows: (1)In this equation, and represent the vector of endogenous variables with a dimension of . The vector τ denotes the quantiles within the range [0,1], while P indicates the lag order of the QVAR model. Additionally, μ(τ) is the vector of conditional means, is the coefficient matrix, and is the vector of error terms. Subsequently, the Generalized Forecast Error Variance Decomposition (GFEVD) for an F-step-ahead forecasting, which represents the impact of a shock in series j on series i, is expressed as follows: (2)In this equation, denotes the variance-covariance matrix of the error terms. The vector is the standard basis vector or unit vector of dimension , with its the i-th element equal to one and all other elements set to zero.In this case, the rows of do not sum to one. Therefore, is standardized to obtain the scaled GFEVD: (3)Using this, the overall adjusted connectedness index (quantile-to-quantile) is calculated as follows: (4)In Equation (4), the higher the Total Connectedness Index (TCI), the higher the market risk.The analysis also used the Structural Vector Autoregression (SVAR) model. In the QQC model, the volatility of geopolitical risk was analyzed in relation to each of the other variables in the model, with results extracted accordingly. The SVAR model followed the same principle. Consequently, four models were estimated.The VAR model in this study is represented in its general form as follows: (5)Where is a vector containing the volatility of geopolitical risk and the index of each industry analyzed individually. The matrices to contain the coefficients of the lagged variables, and represents the residuals, which follow a normal distribution with zero mean and covariance . However, the shocks derived from Model (5) are not structural. To address this, the following model is used, allowing constraints to be imposed on matrices A and B: (6)In Equation (10), represents the structural error terms. The relationship between the VAR and SVAR models is expressed as .Results and DiscussionThe results indicated that geopolitical risk had a significant and varying impact on different industries within the TSE. This impact is influenced not only by each industry’s volatility level but also by the distribution of risk quantiles and industry indices. The QQC results revealed that the petroleum products industry was the most sensitive to geopolitical risk, particularly in extreme quantiles, where its connection to geopolitical risk reaches its peak. This finding suggests that during periods of high volatility, risk transmission accelerates. Similarly, in the chemical, metal ore, and basic metals industries, increased volatility heightened their susceptibility to geopolitical risk shocks. Notably, when these industries experience higher volatility quantiles, their connection to geopolitical risk strengthens across all levels. Structural shock analysis using the SVAR model indicated that all industries exhibited a positive immediate response to geopolitical risk volatility shocks. This reaction is strongest in the short term and gradually weakens over time. Among the industries analyzed, the petroleum products sector displayed the highest sensitivity, with an increase of 1 unit, while the impact on the chemical products, metal ore, and basic metals industries was 0.6, 0.3, and 0.5 units, respectively.ConclusionAccording to the findings, the relationship between geopolitical risk and the petroleum products industry is strongest in extreme quantiles. For other industries, the QQC model identifies two key patterns: first, when geopolitical risk volatility is in the 9th and 10th quantiles, it has the greatest impact on these industries; second, when the industries’ own volatility is in the 9th and 10th quantiles, they show the highest susceptibility to geopolitical risk across all quantiles. In addition, the results from the SVAR model indicated that the impact of geopolitical risk shocks on these industries would remain positive even after 360 periods. In other words, geopolitical risk shocks have a lasting effect on the volatility of the industries analyzed in this study.
Financial Economics
Nazanin Ghasemdokht; Hamideh Razavi
Abstract
Overdue claims resulting from the lending process can pose a significant credit risk to financial institutions. To mitigate this risk, institutions often acquire guarantees. However, borrowers may encounter challenges when providing adequate and valid guarantees, particularly guarantees with lower risk. ...
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Overdue claims resulting from the lending process can pose a significant credit risk to financial institutions. To mitigate this risk, institutions often acquire guarantees. However, borrowers may encounter challenges when providing adequate and valid guarantees, particularly guarantees with lower risk. The present research focused on loan credit risk, borrower utility, and liquidity risk of guarantees within a private fund. First, data mining and classification methods were applied to a dataset of loans. The random forest algorithm, with a prediction accuracy of 0.986, was found to be optimal for constructing a guarantees composition model. The guarantees composition involves using multiple types of guarantees to secure a loan. Two models were established to generate guarantee compositions with a maximum default rate of 10%. In testing scenarios, the average risk of total default for acceptable combinations stands at 3.94%, a significant improvement compared to the fund loans’ overall default rate of 6.3%. Furthermore, the proposed model increases borrower utility from 4.22 to 4.6, not only reducing the default rate but also enhancing borrower utility.IntroductionWhen providing loans to customers, banks require guarantees due to insufficient knowledge of customers and the default risk. Obtaining guarantees from borrowers is recognized as a solution to reduce default risk in banks, but its impact on risk reduction depends on various factors. The combination and type of guarantees are among these factors, which have received less attention in the literature.The current state of overdue bank claims in Iran is unfavorable, and if conditions persist, it will lead to significant monetary and financial crises with negative effects on various sectors of the economy. In recent years, the ratio of non-performing loans to total disbursed facilities in Iran has been consistently higher, averaging around 5% to 10% higher than the global average. Reduction of the default risk in loans can decrease the ratio of non-performing loans to total disbursed facilities.The present study first intended to create various combinations of guarantees for each loan, followed by predicting the probability of default for each combination. In line with their priorities, borrowers can then select their desired guarantee composition from a list of acceptable combinations.MethodologyTo address the research problem, the study identified common classification methods in data mining by relying on published articles in the field of credit risk. Then, a sample dataset of loans from a financial institution was examined, and the data mining process based on classification methods was applied to the dataset. The random forest method, with a prediction accuracy of 0.986, was ultimately chosen as the approach for constructing the guarantee composition model. Using the previous guarantee compositions, the study developed two models by relying on machine learning techniques. These compositions take into account the perspectives of both the financial institution and the borrower.Final ResultThe two models generate guarantee compositions with a maximum acceptable default rate of 10%. Considering their own priorities circumstances, borrowers can select their desired guarantee composition from the available combinations, which contributes to a reduction in the default rate in the financial institution.
