Presenting an Entropy-Based Systemic Risk Warning Model
محورهای موضوعی : Risk Management
Mahmood Nasrollahi
1
,
Majid Zanjirdar
2
,
Majid Davoudi Nasr
3
1 - Department of Finance ,Arak Branch, Islamic Azad University, Arak, Iran
2 - Department of Finance ,Arak Branch, Islamic Azad University, Arak, Iran
3 - Department of Accounting , Arak Branch, Islamic Azad University, Arak, Iran
کلید واژه: Systemic Risk, Entropy, Financial Crisis , Risk Contagion,
چکیده مقاله :
Systemic risk is a type of financial instability that disrupts the functioning of the financial system and affects economic growth. The present study was developed with the aim of presenting a systemic risk warning model based on the entropy criterion in the financial markets of Iran. In terms of direction, the present research is of applied type and in terms of explanatory purpose, and the data collection method is library method. The statistical population of the country's financial markets research includes the capital market, money market, etc., and the time frame of this research is the data related to variables affecting systemic risk in the years 1998 to 2022. In this research, firstly, the identified criteria and indicators affecting systemic risk were ranked using Shannon entropy, Rennie entropy, and Tsallis entropy, and then systemic risk was measured with the MES criterion. The results of this research show that the most systemic risk is caused by the variable of banks' debt to the central bank, and the two variables of government debt and the ratio of government debt to GDP also have the highest systemic risk. According to the general theory of systems and the effectiveness and influence of financial markets on each other, the government and policy makers of the economic and financial sectors must take the necessary measures in order to create a systemic supervisory institution.
Systemic risk is a type of financial instability that disrupts the functioning of the financial system and affects economic growth. The present study was developed with the aim of presenting a systemic risk warning model based on the entropy criterion in the financial markets of Iran. In terms of direction, the present research is of applied type and in terms of explanatory purpose, and the data collection method is library method. The statistical population of the country's financial markets research includes the capital market, money market, etc., and the time frame of this research is the data related to variables affecting systemic risk in the years 1998 to 2022. In this research, firstly, the identified criteria and indicators affecting systemic risk were ranked using Shannon entropy, Rennie entropy, and Tsallis entropy, and then systemic risk was measured with the MES criterion. The results of this research show that the most systemic risk is caused by the variable of banks' debt to the central bank, and the two variables of government debt and the ratio of government debt to GDP also have the highest systemic risk. According to the general theory of systems and the effectiveness and influence of financial markets on each other, the government and policy makers of the economic and financial sectors must take the necessary measures in order to create a systemic supervisory institution.
[1] Ahmadi, Z., Farhanian, M, J., Systemic Risk Measuring in Tehran Stock Exchange with CoVaR and MES Approaches, Stock Exchange Quarterly,2014; 7 (26): 3-27. (In Persian)
[2] Ang, A., Chen, J., Xing, Y. Downside Risk. Review of Financial Studies, 2006; 19(4): 1191–1239. doi: https://doi.org/10.1093/rfs/hhj035.
[3] Asayesh, K., Fallah Shams, M., Jahangirnia, H., Gholami Jamkarani R., Explaining the Systemic Risk Model Using the Marginal Expected Shortfall Approach (MES) for the Banks Listed on the Tehran Stock Exchange, Planning and Budgeting Quarterly, 2020; 25 (2): 115-138. doi: 10.52547/jpbud.25.2.115. (In Persian)
[4] Barati, L., Fallah Shams, M., Ghafari, F., Heydarzadeh Hanzaei, A., The pervasive risk of the financial crisis in the Iranian banking system with the ARFIMA-FIGARCH-Delta CoVaR approach and the expected marginal Shortfall, Financial Engineering and Securities Management Quarterly, 2020; 11(45): 587-611. DOR: 20.1001.1.22519165.1399.11.45.25.2. (In Persian)
[5] Bevilacqua, M, Tunaru, R, Vioto, R., Options-based systemic risk, financial distress, and macroeco-nomic downturns, Journal of Financial Markets, 2023; Elsevier, vol. 65(C). doi: 10.1016/j.finmar.2023.100834.
[6] Billio, M, Casarin, R, Costola, M, Pasqualini, A. An entropy-based early warning indicator for systemic risk, Journal of International Financial Markets, Institutions and Money, 2016; 45: 42-59. DOI: 10.1016/j.intfin .2016.05.008.
[7] Billio, M., Getmansky M, Lo A. W, Pelizzon, L. Econometric measures of connectedness and systemic risk in the finance and insurance sectors, Journal of Financial Economics. 2012; 104(3): 535-559. doi: 10.1016/j.jfineco.2011.12.010 .
[8] Bisias, D, Flood, M, Lo, A, Valavanis, S., A Survey of systemic risk analytics, Annual Review of Finan-cial Economics, 2012; 4(1): 255-296. doi:10.2139/ssrn.1983602.
[9] Chavoshi, K., Shirmohammadi, F., Identifying, measuring and managing the systemic risk of the coun-try's financial system as a requirement of resilience economy, comprehensive and international confer-ence of resilience economy, Babolsar, North Tarood Industrial Research Company. November 30, 2015, (In Persian)
[10] De Bandt, O., & Hartmann, P. Systemic risk: a survey: European Central Bank Working Paper, 2001; No.35.
[11] Ebrahimi, B., Aghaei Sheikh Razi, M., Mohibi, N., Estimating Portfolio Value-at-Risk and Expected Shortfall by Possibility and Necessity Theory, Financial Research, 2017; 7(26): 193-216. doi:10.22059/jfr.2017.218621.1006298. (In Persian).
[12] Fadaei Vahed, M., Dehghan Dehnavi, M, A., Divandari, A., Amiri M., Investigating the impact of banks' risk and competitive indicators on systemic risk with the final expected loss (MES) approach using the GMM model, Investment Knowledge, 2019; 9 (36): 317-334. (In Persian).
[13] Fatemi, A., Sorting of one-variable distributions by entropy and variance, Master's thesis, Tarbiat Moalem University, Department of Mathematics and Statistics, Tehran, Iran, 2001, (In Persian).
[14] Gaspar, V., Systemic risk: Too important to ignore. Conference organized by APB, Lisbon, Governo de Portugal, 2012; 0001: 1-5.
[15] Gu, R & Shao, Y., How long the singular value decomposed entropy predicts the stock market? — Evidence from the Dow Jones Industrial Average Index, Physica A: Statistical Mechanics and its Applica-tions, Elsevier, 2016; 453(C): 150-161. DOI: 10.1016/j.physa.2016.02.030.
[16] Guerra, S. M., Silva, T. C., Tabak, B. M., de Souza Penaloza, R. A., & de Castro
Miranda, R. C. Systemic Risk Measures, Physica A: Statistical Mechanics and Its
Applications, 2016; 442(1): 329-342. doi: 10.1016/j.physa.2015.09.013.
[17] Gurji, M., Sajjad, R., Estimating multi-period value at risk based on simulation and parametric meth-ods. Financial Research, 2015; 18(1): 167-184. doi: 10.22059/jfr.2016.59624. (In Persian)
[18] Hansen, L. P., Challenges in identifying and measuring systemic risk. NBER Chapters, in: Risk Topog-raphy: Systemic Risk and Macro Modeling, 2013; 15-30, National Bureau of Economic Research, Inc.
[19] Hekmati Farid, S., Rezazadeh, A., Malek, A., The Estimation of Systematic Risk in Iranian Financial Sectors (ΔCoVaR Approach), Economic Modeling Quarterly, 2018; 12 (43): 99-122. (In Persian).
[20] Krygier, D. Measuring systemic risk in the Nordic countries an
application of CoVaR, M.A Thesis, Supervisor: Professor Hans Byström, lund
university, 2014.
[21] Liu, F, Gao, J, Hou, Y., Identifying systemic risks and policy-induced shocks in stock markets by relative entropy, Proceedings,2020; 46(1). DOI: https://doi.org/10.3390/ecea-5-06689.
[22] Lupu, R, Cantemir Calin, A, Georgiana Zeldea, C, Lupu, I. A Bayesian entropy approach to sectoral systemic risk modeling, Entropy, 2020; 22 (12). doi: 10.3390/e22121371.
[23] Masoumi, F. and Kerachiyan, R., Optimal Design of Groundwater Quality Monitoring Systems Using Discrete Entropy Theory Case Study: Tehran Aquife. Third National Congress of Civil Engineering, Sha-hid Beheshti, Tehran, Iran. 2007. (In Persian). doi : 10.1007/s10661-008-0742-3.
[24] Mike K. P. So, Anson S. W. Mak, Anson S. W. Mak , Amanda M. Y. Chu . Assessing systemic risk in financial markets using dynamic topic networks, Scientific Reports , 2022; 12(1):2668. doi:10.1038/s41598-022-06399-x.
[25] Mohammad Alizadeh, S., Fallah Shams, M, F., Minoui M., Pervasive (systemic) risk spillover between OPEC crude oil, Tehran Stock Exchange index and exchange rate with regime change approach with time-varying coefficients (MS-TVP), Stock Exchange Quarterly,2022; ready for publication, doi: 10.22034/jse. 2022.11844.1922. (In Persian)
[26] Mohammadi Aghdam, S., Qavam, M, H., Fallah Shams, M., Assessment of the Systemic Risk Originat-ed from the Currency Shocks in the Financial Markets of Iran , Financial Research, 2017; 19 (3): 475-504. doi: 10.22059/jfr.2018.246456.1006557. (In Persian)
[27] Muradmand Jalali, M., Hasanlou, Kh., Evaluation of the share of banks, insurance and investment companies in systemic risk, Quarterly Journal of Financial Studies and Islamic Banking, 2016; 2 (4): 67-92. (In Persian).
[28] Namaki, A., Abbasian, A., Shafiei, A., Analyzing the level of systemic risk of Tehran Stock Exchange companies using the complex systems approach, Financial Management Strategy, 2022; 10 (1): 91-112, doi:10.22051/jfm.2020.30910.2360. (In Persian).
[29] Nili, F., An introduction to financial stability, Economic Research Trends Quarterly, 2005; 15(4): 25-57. (In Persian).
[30] Ostad Hashemi, A., Suri, A., Sadeghi Sharif, J, The impact of macroeconomic variables on the system-ic risk of the banking system with the vector auto regression model, Quarterly Journal of Innovation and Entrepreneurship, 2021;10(19): 55-68. DOR: 20.1001.1.27170454.1400.10.19.3.4. (In Persian)
[31] Rahimi Baghi, A., Arab Salehi Nasrabadi, M., Barzani, M., Systemic risk assessment in the country's financial subsystems using the non-linear Granger method, Asset Management and Financing, 2019; 7(25): 59-80. doi: 10.22108/amf.2019.112209.1281. (In Persian).
[32] Rastgar, M, A., Karimi, N., Systemic risk in the banking sector, Risk Management and Financial En-gineering Quarterly, 2016; 1 (1):1-19. (In Persian)
[33] Rezaian, A., System analysis and design, Tehran, Samt Publications, 2005,436 pages. (In Persian).
[34] Schwarcz S. L. Systemic risk. Duke Law School Legal Studies Research Paper Series, Research Paper No. 163. Georgetown Law Journal, 2008; 97(1):193-249.
[35] Smaga, P. Concept of systemic risk. SRC Special Paper, published by Systemic Risk Center Special, 2014, Paper No 5, The London School of Economics and Political Science.
[36] Vahabzadeh, S., Falah Shams, M, F., Madanchi, M., Kiqbadi, A, M., Systemic risk contagion in Iran's financial markets, Investment Knowledge, 2022; 11 (41): 443-429. (In Persian).
[37] Yin, L, Feng, J, Han, L., Systemic risk in international stock markets: Role of the oil market. Interna-tional Review of Economics & Finance, 2021; 71: 592-619. doi: 10.1016/j.iref.2020.09.024.
[38] Zanjirdar, M., Madahi, M., Khaleghi Kasbi, K. , Comparative analysis of sticky SGA costs and cost of goods sold: Evidence from Tehran Stock Exchange, Management Science Letters,2014;4(3): 521-526
[39] Nekounam , J. , Zanjirdar, M., Davoodi Nasr,M. , Study of relationship between ownership structure liquidity of stocks of companies accepted in Tehran Stock Exchange, Indian Journal of Science and Technology,2012;5(6): 2840-2845
[40] Mohamadi,M., Zanjirdar,M., On The Relationship Between Different Types Of Institutional Owners And Accounting Conservatism With Cost Stickiness, Journal of Management Accounting and Auditing Knowledge, 2018;7(28): 201-214
Adv. Math. Fin. App., 2025, 10(2), P. 218-234 | |
| Advances in Mathematical Finance & Applications www.amfa.iau-arak.ac.ir Print ISSN: 2538-5569 Online ISSN: 2645-4610
|
Original Research
Presenting an Entropy-Based Systemic Risk Warning Model
Mahmood Nasrolahia, Majid Zanjirdar a,*, Majid Davoudinasrb
aDepartment of Finance, Arak Branch, Islamic Azad University, Arak, Iran bDepartment of Accounting, Arak Branch, Islamic Azad University, Arak, Iran
| |||
Article Info Article history: Received 2023-08-04 Accepted 2024-02-23
Keywords: Systemic Risk Entropy Financial Crisis Risk Contagion |
| Abstract | |
Systemic risk is a type of financial instability that disrupts the functioning of the financial system and affects economic growth. The present study was developed with the aim of presenting a systemic risk warning model based on the entropy criterion in the financial markets of Iran. In terms of direction, the present research is of applied type and in terms of explanatory purpose, and the data collection method is library method. The statistical population of the country's financial markets research includes the capital market, money market, etc., and the time frame of this research is the data related to variables affecting systemic risk in the years 1998 to 2022. In this research, firstly, the identified criteria and indicators affecting systemic risk were ranked using Shannon entropy, Rennie entropy, and Tsallis entropy, and then systemic risk was measured with the MES criterion. The results of this research show that the most systemic risk is caused by the variable of banks' debt to the central bank, and the two variables of government debt and the ratio of government debt to GDP also have the highest systemic risk. According to the general theory of systems and the effectiveness and influence of financial markets on each other, the government and policy makers of the economic and financial sectors must take the necessary measures in order to create a systemic supervisory institution.
| |||
1 Introduction
Investigating the financial crisis requires analyzing the contagion and shocks that other factors affect the creation of the financial crisis. In general, contagion and fundamental shocks that are present in the acceleration of the financial crisis can be summarized in the discussion of systemic risk. Systemic risk, which not only refers to the structure of the company and the market, but also arises from the discussion of the global economy [16]. In short, it should be stated that systemic risk is made up of internal and external factors in such a way that a prediction model should be provided in order to reduce its effects [7]. In recent years, globalization and increased communication and innovations in financial markets have led to increased convergence and the creation of an interwoven financial system, and new channels of shock transmission have emerged due to the interconnectedness of institutions [20]. Today, there is a lot of evidence that shows that the price fluctuations of financial assets spread to other assets and markets. It is a proven fact that financial markets move closer to each other in times of crisis. Conditional correlation between assets is much higher during periods of financial crisis when market returns are low [2]. Financial markets have faced various uncertainties such as financial crisis, oil shocks, changes in currency policies and similar cases. The occurrence of shock, which is a mild degree of crisis, is always accompanied by macro and micro effects that may not be limited to the target market and may spread to other markets as well. Hence, examining the intensity and direction of fluctuations from one market to another is of particular importance. The heaviness of the costs arising from the financial crisis requires that the policy makers of the market take the necessary precautionary measures to prevent the occurrence of the crisis. Applying accurate and timely preventive measures at the macro level requires accurate identification of systemic risk in the financial sector. This type of risk actually shows the possible positions of the crisis and guides the policy makers in applying the necessary precautionary measures [29]. Systemic risk occurs when a failure or crisis in one part of the market spreads to other parts and turns into a widespread crisis; in such a way that the loss of one or more important and effective financial institutions is transferred to other institutions as well. In the financial system of any country, accurate and timely identification of systemic risk with the aim of preventing financial crisis is an undeniable necessity [31]. Today, the concept of systemic risk is considered as a fully accepted concept in the study of the phenomenon of financial instability and possible policy responses, especially in the field of macro-precautionary policy [10]. This concept is effective in understanding crises, especially the financial crisis, and based on the definition provided, it deals with various aspects of systemic risk. Regarding the background of the research, it should be noted that systemic risk is a new topic in the financial literature of the world and it has been less than a decade. But despite the newness of this issue, a large volume of studies on this issue have been formed in foreign countries, especially countries that have faced financial crisis. The conducted studies proposed many methods to calculate systemic risk. Despite this large volume of studies, until now Iranian researchers have not significantly addressed this issue [9]. Although systemic risk is known as the focus of the recent financial crisis, there is no single and consensus definition for it. For example, based on a basic definition, a set of conditions that threaten stability and stability and public trust in the financial system is known as systemic risk [7]. However, the recent financial crisis is a perfect case example for the definition of systemic risk, which shows how the creation of a crisis in one of the financial sectors causes widespread financial instability and, by expanding its scope outside the financial markets, disrupts the activities of the real sector of the economy [14]. To measure different dimensions of systemic risk, several metrics have been provided; however, determining which measure shows the relationship between financial markets and macroeconomics is still an open and relevant issue. The relationship between systemic risk and macroeconomic indicators is considered a virgin research topic, and studies regarding the mechanism of transferring systemic risk from the financial sector to the macroeconomics are at the beginning [18]. Choosing the right index to measure systemic risk is one of the most important and challenging issues. Considering the introduction of a wide range of criteria for measuring systemic risk, it can be seen that understanding the complex and compatible nature of the financial system requires choosing the appropriate method and index for measuring systemic risk. So far, various methods have been presented to calculate this risk, but in the end, it can be said that choosing a single method that is agreed upon by everyone is neither possible nor desirable. As a result, in order for the systematic risk measurement method to be reliable, relevant economic and financial concepts should be included in its practical application; For example, it is important to decide which variables and indicators from which institutions or markets are used in estimating the results, or which frequency of observations should be chosen with which confidence level [8]. For example, one of the criteria for measuring systemic risk is value at risk, which, in addition to its simple calculation, has problems and shortcomings such as incoherence. Therefore, the standard of conditional risk value or expected systemic leakage, supplements have been introduced for it. Using value-at-risk criteria will lead to overestimation [17]. While the two mentioned criteria provide more conservative estimates than the value at risk criterion [11]. The results of this study can help managers, decision makers, brokers and capital market activists in understanding the state of financial markets, including the capital market, currency market, etc. Next, in the second part, research literature and a review of previous studies will be discussed. In the third part, the research methodology is presented. In the fourth part, the research model will be reported. Finally, the last part is dedicated to the conclusion.
2 Theoretical Foundations and Research Background
Ludwig von Bertalanffy, an Austrian biologist and thinker, is considered one of the founders of the general theory of systems in the early 20th century, who for the first time presented the concept of thinking in 1928 which became known as the general theory of systems, and during the years 1930-1932, he presented the organic theory and made great strides in expanding and establishing it in other fields. According to his opinion, an organism is not just a sum of separate elements, but a system with system and totality. The goal of the general theory of systems is to discover the laws and the inherent order of phenomena, hence it can be considered the most fluid system theory, because no decisive theory has been presented in its theoretical framework. The special feature of this attitude is to pay attention to the whole or holism [33]. Systemic risk indicates the possibility of the entire financial system becoming critical in a situation where one or more parts of the market are in crisis. Market collapse often happens when one or more important and influential companies in the whole system suffer a crisis and start going bankrupt. Due to the strong relationship with other companies, this bankruptcy has a negative effect on other companies and will also involve them in crisis. As a result, these chain reactions lead to market anxiety and expose the market to a widespread crisis [1]. For this reason, it is called a system that has a pervasive effect on the entire system and quickly spreads throughout the entire capital market or the entire economy of the country and turns into a crisis [35]. In other words, systemic risk refers to the risks imposed by internal communication and dependence in a system or market, where the default of a single entity or a set of entities can cause a cascade default and potentially cause the bankruptcy or collapse of the entire system or market [34]. Entropy is a random variable that is defined in terms of the probability distribution of that variable and can be used as a suitable measure for randomness or uncertainty indicating the number of bytes required to transmit a message. The entropy of the system is related to the amount of information in it. A more ordered system can be described with fewer bytes of information, while a less ordered system needs more bytes of information to be described [13]. Shannon in 1948 showed that events with a high probability of occurrence show less information. On the contrary, the lower the probability of an event, the more information it provides. New information reduces uncertainties. Hence, the value of new information is proportional to the amount of uncertainty that is reduced. According to the order of uncertainty and parameter information, they are interdependent. Entropy theory is used as an index to quantify the relevant items [23]. The word entropy has different meanings in different disciplines. In some sciences, it is used in the sense of disorder and chaos, and in some sciences, it is used as the vector of time. Explaining exactly what entropy is not so simple; because entropy is not tangible and is often defined as a measure of disorder. In the field of finance, entropy represents a type of measurement of the uncertainty of the state of the system and as an indicator to show the extent of changes in the market [15]. Regarding the background of the research, it should be noted that systemic risk is a new topic in the financial literature of the world and it has a lifespan of less than a decade. But despite the novelty of this issue, a large amount of research has been done on this issue in foreign countries, especially countries that have faced a financial crisis [4]. Fadaei Vaheh et al. [12] in their article titled "Investigating the effect of banks' risk and competitiveness indicators on the systemic risk with the final expected decline approach of Marginal Expected Shortfall (MES) using the GMM model, the relationship between macroeconomic indicators (interest rate, economic growth rate and inflation), risk (liquidity and default risk) and competitiveness (Herfindahl-Hirschman index and asset size) of banks, the results of modeling show a significant relationship between the default (credit) risk index and the systemic risk of the banking industry. And there is direct. Ostad Hashemi et al. [30] in his article, investigated the impact of macroeconomic variable shocks on the systemic risk of the banking system in Iran. The results show that the positive oil price shock, inflation uncertainty, exchange rate and bank interest rate have an increasing effect on systemic risk. Vahabzadeh et al. [36] in his article entitled Systemic risk contagion in Iran's financial markets, have investigated the contagion and transmission of shocks in Iran's financial markets by using simultaneous multi-Garch models and covariance changes. The results show that the country's capital market has the largest share and the banking system has the smallest share in creating systemic risk. Namaki et al. [28] in their article analyzed the effect of the local topology structure of financial institutions in the financial network on the level of systemic risk by using the conditional differential value-at-risk measure. The results showed that financial institutions with greater centrality have lower node strength, and smaller node degrees have higher systemic risk. The results of the research of Mohammad Alizadeh and colleagues [25] It has been shown that there is a positive correlation between systemic risk in the stock, oil and currency markets and there is risk contagion between these markets. Billio et al. [6] presented an entropy-based early warning index for systemic risk for banking crises in Europe. In a study, Lupu et al. [22] investigated the systemic risk dynamics of European companies using the Bayesian entropy integration approach with systemic risk measures such as final expected shortfall, CoVaR, and delta CoVaR. The results of their research show that banks, financial companies, insurance and real estate have a high tendency to generate surplus. Liu et al. (2020) showed that relative entropy or Kolbeck-Leibler divergence can be used to identify systemic risks and policy-induced shocks in stock markets. Yin et al. [37] the systemic risk in the international stock markets with the role of the oil market state that the international systemic risk is significantly affected by oil price shocks. Mike et al. [24] in their study, proposed a dynamic topic network (DTN) approach that combined topic modeling and network analysis to assess systemic risk in financial markets. Bevilacqua et al. [5] using an implicit measure of systemic risk, have provided significant predictability for macroeconomic recessions as well as one-year recessions. As it is clear from the studies, in general, the measurement and evaluation of systemic risk, especially in Iran, is considered a nascent issue, and there is little empirical evidence about how disturbances created in a specific financial sector can spread to the entire economy, and whether a specific financial sector has more or less risk than another [27]. The results of some research on cost stickiness show that selling and general administration (SGA) costs, as well as costs of goods sold, have highly sticky behaviors [38]. The results of another research show that there a reverse (negative) relationship between institutional ownership level, managerial ownership level, and ownership concentration level with liquidity. Also, there is a direct (positive) relationship between corporative ownership level and liquidity [39]. Based on other research indicates that there is a positive and significant relationship between institutional investors and passive institutional investors with conservatism. Other findings suggest that cost stickiness has a positive impact on the relationship between institutional investors and passive institutional investors with conservatism [40].
3 Methodology
In order to provide a solution for a problem, the current research is of an applied type and in terms of explanatory purpose, and the data collection method is a library method. The statistical population of this research includes financial markets such as the capital market, currency market, energy market, commodity market, and money market.
Table 1: Research Variables and Their Measurement Methods
Row | Variable symbol | Index Title | Method of Measurement | ||||
1 | OP | Oil price returns |
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2 | GP | Gold price yield per ounce |
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3 | EX | Exchange rate returns |
| ||||
4 | TP | Return of the total capital market index |
| ||||
5 | IRI | Real interest rate | IRI= P * i* n | ||||
6 | IR | Inflation Rate |
| ||||
7 | GDP | Gross Domestic Product | GDP = Consumption (C) + Investment (I) + Government Expenditure(G) + (Exports (X) _ Imports (M)) | ||||
8 | BBC | Banknotes and bills in circulation | ------------------- | ||||
9 | VBM | Volume of bills and muskets to Volume of money | VBM/VM | ||||
10 | SD | Return on short-term investment deposits |
| ||||
11 | LD | Return on long term investment deposit |
| ||||
12 | RSL | The ratio of short-term to long-term deposits | SD/LD | ||||
13 | NGS | Non-government sector debt ratio to banks | ------------------- | ||||
14 | BCB | Banks' debt yield to the central bank |
| ||||
15 | SMC | Speed of money circulation | GDP / Money supply | ||||
16 | LG | Liquidity growth | ------------------- | ||||
17 | GD | Government debt yield |
| ||||
18 | GDG | Return on the ratio of government debt to GDP |
| ||||
19 | NEP | Net energy production | ------------- | ||||
20 | CAG | Ratio of current account to GDP | CA/GDP | ||||
21 | CA | Current account (annual) | ------------- | ||||
22 | PPI | Producer price index | Basic price of the product / current price of the product | ||||
23 | CPI | Consumer Price Index | The price of goods in the base year/ the price of goods in the current year | ||||
Variable | Minimum | Maximum | Median | Average | Std.dev | Jarque Bera Test | probability |
Return of the total capital market index (TP) | -89 | 679 | 34 | 16 | 73 | 22985 | 0.0000 |
Return on long-term deposits (LD) | -2 | 33 | 7 | 7 | 12 | 18247 | 0.0000 |
Return on government debt (GD) | -13 | 324 | 6 | 10 | 36 | 5860 | 0.0000 |
Exchange rate returns (EX) | -15 | 260 | 5 | 5 | 13 | 1077 | 0.0000 |
Return on the ratio of government debt to GDP (GDG) | -21 | 324 | 6 | 10 | 36 | 5391 | 0.0000 |
Return of banks' debts to the central bank (BCB) | -39 | 54 | 9 | 6 | 20 | 249 | 0.0000 |
Based on the results of the above Table, the average growth of the total capital market index in the studied period was 16%, in this period, the total capital market index had a maximum growth of 679%. Also, the exchange rate has an average yield of 5% and the debts of the banks to the central bank have an average yield of 6%. Government debts have experienced an average return of 10% during the period under review. Considering that the data of this research is seasonal, therefore, checking and performing seasonal unit root tests were used to avoid false regression, and to check the significance of the variables. In order to check the seasonal unit root, the Heggy unit root test was used. Due to the fact that most macroeconomic variables are insignificant and also the size of the variables is very different from each other, first the growth of each variable is calculated, then the significance of each variable is examined. The results indicated that the variables are significant in the scope of the research. In the Heggy test, the null hypothesis indicates the existence of a unit root.
Assumption H0: the existence of a single root among the variables
Assumption H1: There is no single root among the variables
After that, it was determined that the returns of the above variables are at the mana level and do not have an annual unit root or a seasonal unit root. Therefore, the relationship between variables can be checked using regression methods and correlation coefficient, and there is no need to differentiate explanatory variables in the model. In this section, the correlation coefficient between the growths of each of the above variables is observed.
Table 3: Examining the Significance of Model Variables
Heggy's test | |||
Variable | Annual unit root | Seasonal unit root | Result |
Return of the total capital market index (TP) | 0.0 | 0.0 | Not accepting the null hypothesis |
Return on long-term deposits (LD) | 0.0 | 0.0 | Not accepting the null hypothesis |
Return on government debt (GD) | 0.0 | 0.0 | Not accepting the null hypothesis |
Exchange rate returns (EX) | 0.0 | 0.0 | Not accepting the null hypothesis |
Return on the ratio of government debt to GDP (GDG) | 0.0 | 0.0 | Not accepting the null hypothesis |
Return of banks' debts to the central bank (BCB) | 0.0 | 0.0 | Not accepting the null hypothesis |
After that, it was determined that the returns of the above variables are at the mana level and do not have an annual unit root or a seasonal unit root. Therefore, the relationship between variables can be checked using regression methods and correlation coefficient, and there is no need to differentiate explanatory variables in the model. In this section, the correlation coefficient between the growths of each of the above variables is observed.
Table 4: Correlation Coefficient between Variables
Variable | TP | EX | LD | GD | GDG | BCB |
Return of the total capital market index (TP) | 1 |
|
|
|
|
|
Return on long-term deposits (LD) | 0.05 | 1 |
|
|
|
|
Return on government debt (GD) | 0.02 | -0.05 | 1 |
|
|
|
Exchange rate returns (EX) | -0.04 | 0/1 | 0/3 | 1 |
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Return on the ratio of government debt to GDP (GDG) | -0.06 | 0.09 | 0/28 | 0/9 | 1 |
|
Return of banks' debts to the central bank (BCB) | -0.09 | -0.05 | 0/49 | 0.08 | 0.09 | 1 |
As can be seen, the return of long-term investment deposits and exchange rate have a positive correlation with the return of the total capital market index, but other variables have a negative correlation with the total capital market index. Also, variables such as the return on government debt and the growth of the ratio of government debt to GDP have a positive correlation with the exchange rate, on the other hand, the return on long-term investment deposits has a negative correlation with the exchange rate.
4.1 Analysis of the First Question
Question: What are the factors affecting systemic risk based on internal and external empirical research?
In order to select the variables affecting the systemic risk, the indicators and variables mentioned in the past domestic and foreign experimental researches were examined.
Table 5: Systematic Review of Texts to Select Research Variables
Component | Research questions |
Study community | Various sources include all kinds of articles, theses, in which systemic risk has been identified and explained. |
Research purpose | What are the factors affecting systemic risk in Iran's financial markets? |
Research method | Identifying and prioritizing factors affecting systemic risk in Iran's financial markets |
Research period | All domestically published practical articles from between 2010 and 2022 and available sources and articles between 2010 and 2022 AD |
The study population was used to select the variables and indicators affecting systemic risk, domestically published practical articles from 2010 to 2022 and foreign articles published between 2010 and 2022 in scientific databases such as Google Scholar, Elsevier, Emerald, Noormags, Scientific Information Database (SID) and Civilica were used to search for research articles. At this stage, the researcher has systematically searched for articles published in foreign and domestic authoritative articles with the aim of determining valid, reliable and relevant documents in the appropriate time frame. First, relevant keywords are selected. These words are listed in a Table.
Table 6: Table of Selected Keywords for Searching in Databases
Row | Key Words |
1 | Systemic Risk |
2 | Financial Crisis |
3 | Systemic Crisis |
4 | Risk Contagion |
5 | Volatility Contagion |
6 | Financial Contagion |
7 | Expected systemic loss |
8 | Marginal Expected loss |
9 | Pervasive Risk |
Validation of used publications:
Table 7: List of Databases Used in the Research
Data bases | Title site | Type of Document |
Domestic | SID Civilica Noormags Magiran | Persian articles |
Foreign | Science direct Springer Scopus Google scholar | Foreign articles |
Based on the systematic review of the research literature, all macro components and variables affecting the systemic risk in the country's financial markets, which have been used in past domestic and foreign researches, were identified and identified as described in Table 1.
4.2 Analysis of the Second Question
Question: What is the state of entropy of the components affecting systemic risk?
Shannon's entropy state:
Shannon entropy is a probability measure P on a finite set X as follows:
| (1) | ||||||
Row | Component title |
1 | Return of the total capital market index (TP) |
2 | Return of banks' debts to the central bank (BCB) |
3 | Return on government debt (GD) |
4 | Exchange rate returns (EX) |
5 | Return on the ratio of government debt to GDP (GDG) |
6 | Return on long-term deposits (LD) |
Rennie entropy state:
| (2) | ||||||
Row | Component title |
1 | Return of the total capital market index (TP) |
2 | Return of banks' debts to the central bank (BCB) |
3 | Exchange rate returns (EX) |
4 | Return on long-term deposits (LD) |
5 | Return on government debt (GD) |
6 | Return on the ratio of government debt to GDP (GDG) |
Tsallis entropy state:
For any positive real number α, the alpha order entropy of a probability measure P on the finite set X is defined as follows:
| (3) | ||||||
Row | Component title |
1 | Return of the total capital market index (TP) |
2 | Return of banks' debts to the central bank (BCB) |
3 | Exchange rate returns (EX) |
4 | Return on long-term deposits (LD) |
5 | Return on government debt (GD) |
6 | Return on the ratio of government debt to GDP (GDG) |
4.3 Analysis of the Third Question
Question: How is systemic risk based on Marginal Expected Shortfall?
Final Expected Dropout Criterion
Based on the study of Brownless and Engel (2012), the MES criterion on day t is defined as follows;
| (4) | ||||||
| (5) | ||||||
| (6) | ||||||
| (7) | ||||||
| (8) | ||||||
| (9) | ||||||
| (10) | ||||||
| (11) | ||||||
| (12) | ||||||
| (13) | ||||||
| (14) | ||||||
Variable | MES |
Return of the total capital market index (TP) | 6/5 |
Exchange rate returns (EX) | 5/4 |
Return on long-term deposits (LD) | 13/4 |
Return on government debt (GD) | 14/8 |
Return on the ratio of government debt to GDP (GDG) | 14/8 |
Return of banks' debts to the central bank (BCB) | 19 |
Based on the results obtained, the highest systemic risk is caused by the variable of banks' debt to the central bank, and the average systemic risk of MES is the two variables of government debt and the ratio of government debt to GDP in the review period of 14.8, and the lowest systemic risk of MES is related to the exchange rate variable, which is equal to 4.5.
4.4 Analysis of the Fourth Question
How is the systemic risk warning model in Iran's financial markets?
Fig. 2: Systemic Risk Warning Model in Financial Markets of Iran
5 Discussion and Conclusions
After the financial crisis of 2007-2009, the issue of systemic risk as a macro-level risk that can affect the stability of the entire financial system has received much attention. In this era, it became clear that the one-dimensional view of the financial systems regulatory bodies on the individual risks of each financial institution, including the value at risk (VaR), is not sufficient to prevent financial crises, and the forgotten part of financial risks, which is the systemic or systematic risks of financial institutions, should be given special attention in policies and legislation [4]. In general, systemic risk provides a suitable criterion for monitoring financial markets by the government. Based on the criteria for measuring and predicting the share of systemic risk, regulatory departments can evaluate the companies that have a high share of systemic risk in their monitoring and thus maintain the stability of the financial system. Therefore, it is suggested that the supervisory and policy-making authorities, including the Ministry of Economy and Finance, the Central Bank, the Deposit Guarantee Fund, and the Securities and Exchange Organization, use the methods presented in this research to measure and predict systemic risk and identify important systemic institutions. In this research, using the seasonal data of macro variables of Iran's economy in the period of 1998 to 2022, it was investigated to determine the systemic risk between the variables of total capital market index, long-term investment deposits, government debts, exchange rate, banks' debt to the central bank and the ratio of government debts to GDP during the period. The results showed that according to the MES measure, the highest systemic risk is caused by the variable of banks' debt to the central bank, and the two variables of government debt and the ratio of government debt to GDP according to the MES measure have the highest systemic risk, and the lowest systemic risk based on the MES measure is related to the exchange rate variable. During the period under review in 2011 and 2017, the final expected fall of the total capital market index has reached its highest value. According to the results obtained from the measurement of systemic risk, it is clear that the important factor of creating systemic risk in the financial markets of the country is the debt of the banks to the central bank and the debts of the government. When the gap between the government's current expenditures is not covered by the government's revenues, the government will inevitably use the resources of the central bank. Borrowing from the central bank is one of the most common approaches to compensate a part of the government's budget deficit. As the government's debts to the central bank increase, the monetary base increases, and as a result, the money supply will increase under the influence of the government's behavior. As the money supply changes, the demand of the entire economy changes. As aggregate demand in the economy changes, the general level of prices in the economy will change. It is obvious that these actions of the government will affect the demand for foreign exchange and consequently the nominal exchange rate of the market. Finally, government debts through the demand and supply gap, the exchange rate and the general level of prices will lead to changes in the supply of the entire economy and as a result changes in economic growth. The results of the present research are consistent with the findings of the research of Ostad Hashemi et al. [30] and Fadai Vahed et al. [12] and the research of Billio et al [6]. Using the obtained results, it is suggested that the government and the policy makers of the economic and financial sectors should create a system monitoring institution. Due to the fact that the collapse and bankruptcy of the markets are slightly different from individual financial companies, while the lack of liquidity and the resulting spread of risk from one financial institution to another may be worrisome, but the main concern is from the shocks that spread the collapse of the financial market and the liquidity crisis to other financial sectors and the macro economy. It is also suggested to the investors of the financial markets, due to the regime dependence between the financial markets and the spillover of turbulence between the said markets, the uncertainty of investing in these markets has increased, and in order to form a portfolio and in order to reduce risk, it is necessary for investors to have more groups of financial assets in their portfolios in long-term periods of time. It is also suggested to the researchers in the economic and financial fields to examine the transmission mechanism of uncertainty shocks in different sectors of the economy as well as different industries of the capital market using other econometric models and other time-varying parameter models. Future researchers are suggested to use other entropy measurement methods such as: joint entropy, approximate entropy, etc. in the ranking of variables.
References
[1] Ahmadi, Z., Farhanian, M, J., Systemic Risk Measuring in Tehran Stock Exchange with CoVaR and MES Approaches, Stock Exchange Quarterly,2014; 7 (26): 3-27. (In Persian)
[2] Ang, A., Chen, J., Xing, Y. Downside Risk. Review of Financial Studies, 2006; 19(4): 1191–1239. doi: https://doi.org/10.1093/rfs/hhj035.
[3] Asayesh, K., Fallah Shams, M., Jahangirnia, H., Gholami Jamkarani R., Explaining the Systemic Risk Model Using the Marginal Expected Shortfall Approach (MES) for the Banks Listed on the Tehran Stock Exchange, Planning and Budgeting Quarterly, 2020; 25 (2): 115-138. doi: 10.52547/jpbud.25.2.115. (In Persian)
[4] Barati, L., Fallah Shams, M., Ghafari, F., Heydarzadeh Hanzaei, A., The pervasive risk of the financial crisis in the Iranian banking system with the ARFIMA-FIGARCH-Delta CoVaR approach and the expected marginal Shortfall, Financial Engineering and Securities Management Quarterly, 2020; 11(45): 587-611. DOR: 20.1001.1.22519165.1399.11.45.25.2. (In Persian)
[5] Bevilacqua, M, Tunaru, R, Vioto, R., Options-based systemic risk, financial distress, and macroeconomic downturns, Journal of Financial Markets, 2023; Elsevier, vol. 65(C). doi: 10.1016/j.finmar.2023.100834.
[6] Billio, M, Casarin, R, Costola, M, Pasqualini, A. An entropy-based early warning indicator for systemic risk, Journal of International Financial Markets, Institutions and Money, 2016; 45: 42-59. DOI: 10.1016/j.intfin .2016.05.008.
[7] Billio, M., Getmansky M, Lo A. W, Pelizzon, L. Econometric measures of connectedness and systemic risk in the finance and insurance sectors, Journal of Financial Economics. 2012; 104(3): 535-559. doi: 10.1016/j.jfineco.2011.12.010 .
[8] Bisias, D, Flood, M, Lo, A, Valavanis, S., A Survey of systemic risk analytics, Annual Review of Financial Economics, 2012; 4(1): 255-296. doi:10.2139/ssrn.1983602.
[9] Chavoshi, K., Shirmohammadi, F., Identifying, measuring and managing the systemic risk of the country's financial system as a requirement of resilience economy, comprehensive and international conference of resilience economy, Babolsar, North Tarood Industrial Research Company. November 30, 2015, (In Persian)
[10] De Bandt, O., & Hartmann, P. Systemic risk: a survey: European Central Bank Working Paper, 2001; No.35.
[11] Ebrahimi, B., Aghaei Sheikh Razi, M., Mohibi, N., Estimating Portfolio Value-at-Risk and Expected Shortfall by Possibility and Necessity Theory, Financial Research, 2017; 7(26): 193-216. doi:10.22059/jfr.2017.218621.1006298. (In Persian).
[12] Fadaei Vahed, M., Dehghan Dehnavi, M, A., Divandari, A., Amiri M., Investigating the impact of banks' risk and competitive indicators on systemic risk with the final expected loss (MES) approach using the GMM model, Investment Knowledge, 2019; 9 (36): 317-334. (In Persian).
[13] Fatemi, A., Sorting of one-variable distributions by entropy and variance, Master's thesis, Tarbiat Moalem University, Department of Mathematics and Statistics, Tehran, Iran, 2001, (In Persian).
[14] Gaspar, V., Systemic risk: Too important to ignore. Conference organized by APB, Lisbon, Governo de Portugal, 2012; 0001: 1-5.
[15] Gu, R & Shao, Y., How long the singular value decomposed entropy predicts the stock market? — Evidence from the Dow Jones Industrial Average Index, Physica A: Statistical Mechanics and its Applications, Elsevier, 2016; 453(C): 150-161. DOI: 10.1016/j.physa.2016.02.030.
[16] Guerra, S. M., Silva, T. C., Tabak, B. M., de Souza Penaloza, R. A., & de Castro
Miranda, R. C. Systemic Risk Measures, Physica A: Statistical Mechanics and Its
Applications, 2016; 442(1): 329-342. doi: 10.1016/j.physa.2015.09.013.
[17] Gurji, M., Sajjad, R., Estimating multi-period value at risk based on simulation and parametric methods. Financial Research, 2015; 18(1): 167-184. doi: 10.22059/jfr.2016.59624. (In Persian)
[18] Hansen, L. P., Challenges in identifying and measuring systemic risk. NBER Chapters, in: Risk Topography: Systemic Risk and Macro Modeling, 2013; 15-30, National Bureau of Economic Research, Inc.
[19] Hekmati Farid, S., Rezazadeh, A., Malek, A., The Estimation of Systematic Risk in Iranian Financial Sectors (ΔCoVaR Approach), Economic Modeling Quarterly, 2018; 12 (43): 99-122. (In Persian).
[20] Krygier, D. Measuring systemic risk in the Nordic countries an
application of CoVaR, M.A Thesis, Supervisor: Professor Hans Byström, lund
university, 2014.
[21] Liu, F, Gao, J, Hou, Y., Identifying systemic risks and policy-induced shocks in stock markets by relative entropy, Proceedings,2020; 46(1). DOI: https://doi.org/10.3390/ecea-5-06689.
[22] Lupu, R, Cantemir Calin, A, Georgiana Zeldea, C, Lupu, I. A Bayesian entropy approach to sectoral systemic risk modeling, Entropy, 2020; 22 (12). doi: 10.3390/e22121371.
[23] Masoumi, F. and Kerachiyan, R., Optimal Design of Groundwater Quality Monitoring Systems Using Discrete Entropy Theory Case Study: Tehran Aquife. Third National Congress of Civil Engineering, Shahid Beheshti, Tehran, Iran. 2007. (In Persian). doi : 10.1007/s10661-008-0742-3.
[24] Mike K. P. So, Anson S. W. Mak, Anson S. W. Mak , Amanda M. Y. Chu . Assessing systemic risk in financial markets using dynamic topic networks, Scientific Reports , 2022; 12(1):2668. doi:10.1038/s41598-022-06399-x.
[25] Mohammad Alizadeh, S., Fallah Shams, M, F., Minoui M., Pervasive (systemic) risk spillover between OPEC crude oil, Tehran Stock Exchange index and exchange rate with regime change approach with time-varying coefficients (MS-TVP), Stock Exchange Quarterly,2022; ready for publication, doi: 10.22034/jse. 2022.11844.1922. (In Persian)
[26] Mohammadi Aghdam, S., Qavam, M, H., Fallah Shams, M., Assessment of the Systemic Risk Originated from the Currency Shocks in the Financial Markets of Iran , Financial Research, 2017; 19 (3): 475-504. doi: 10.22059/jfr.2018.246456.1006557. (In Persian)
[27] Muradmand Jalali, M., Hasanlou, Kh., Evaluation of the share of banks, insurance and investment companies in systemic risk, Quarterly Journal of Financial Studies and Islamic Banking, 2016; 2 (4): 67-92. (In Persian).
[28] Namaki, A., Abbasian, A., Shafiei, A., Analyzing the level of systemic risk of Tehran Stock Exchange companies using the complex systems approach, Financial Management Strategy, 2022; 10 (1): 91-112, doi:10.22051/jfm.2020.30910.2360. (In Persian).
[29] Nili, F., An introduction to financial stability, Economic Research Trends Quarterly, 2005; 15(4): 25-57. (In Persian).
[30] Ostad Hashemi, A., Suri, A., Sadeghi Sharif, J, The impact of macroeconomic variables on the systemic risk of the banking system with the vector auto regression model, Quarterly Journal of Innovation and Entrepreneurship, 2021;10(19): 55-68. DOR: 20.1001.1.27170454.1400.10.19.3.4. (In Persian)
[31] Rahimi Baghi, A., Arab Salehi Nasrabadi, M., Barzani, M., Systemic risk assessment in the country's financial subsystems using the non-linear Granger method, Asset Management and Financing, 2019; 7(25): 59-80. doi: 10.22108/amf.2019.112209.1281. (In Persian).
[32] Rastgar, M, A., Karimi, N., Systemic risk in the banking sector, Risk Management and Financial Engineering Quarterly, 2016; 1 (1):1-19. (In Persian)
[33] Rezaian, A., System analysis and design, Tehran, Samt Publications, 2005,436 pages. (In Persian).
[34] Schwarcz S. L. Systemic risk. Duke Law School Legal Studies Research Paper Series, Research Paper No. 163. Georgetown Law Journal, 2008; 97(1):193-249.
[35] Smaga, P. Concept of systemic risk. SRC Special Paper, published by Systemic Risk Center Special, 2014, Paper No 5, The London School of Economics and Political Science.
[36] Vahabzadeh, S., Falah Shams, M, F., Madanchi, M., Kiqbadi, A, M., Systemic risk contagion in Iran's financial markets, Investment Knowledge, 2022; 11 (41): 443-429. (In Persian).
[37] Yin, L, Feng, J, Han, L., Systemic risk in international stock markets: Role of the oil market. International Review of Economics & Finance, 2021; 71: 592-619. doi: 10.1016/j.iref.2020.09.024.
[38] Zanjirdar, M., Madahi, M., Khaleghi Kasbi, K. , Comparative analysis of sticky SGA costs and cost of goods sold: Evidence from Tehran Stock Exchange, Management Science Letters,2014;4(3): 521-526
[39] Nekounam , J. , Zanjirdar, M., Davoodi Nasr,M. , Study of relationship between ownership structure liquidity of stocks of companies accepted in Tehran Stock Exchange, Indian Journal of Science and Technology,2012;5(6): 2840-2845
[40] Mohamadi,M., Zanjirdar,M., On The Relationship Between Different Types Of Institutional Owners And Accounting Conservatism With Cost Stickiness, Journal of Management Accounting and Auditing Knowledge, 2018;7(28): 201-214
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* Corresponding author. Tel.: E-mail address: zanjirdar08@gmail.com |
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