Journal of Risk modeling and Financial Engineering2538-53721120160922Systemic Risk in TSE Banking SectorSystemic Risk in TSE Banking Sector11940549FAMohammad AliRastegarAssistant Prof., Faculty of Industrial & Systems Engineering, Tarbiat Modares University, Tehran, IranNasrinKarimiMSc. Student, Financial Engineering, Khatam University, Tehran, IranJournal Article20160609Systemic risk is the risk of collapse in the financial system. Due to the financial crisis that hit the world economy in 2008, the study of systemic risk in the banking sector became more attractive for researchers. In this research we study systemic risk in the Iranian banking sector by using a conventional systemic risk measure, ∆CoVaR. To compute the measure, we employ dynamic conditional correlation model. For this purpose, we estimate the mentioned systemic risk measure of the seven Iranian banks from March of 2010 to March of 2015. Then using panel data regression, we investigate the relationships between the systemic risk measure and certain bank characteristic variables (i.e. VaR, size-Log of Equity, Leverage ratio). Our empirical findings shows that, the systemic risk contributions of banking sector is high and studied banks has different ranking based on ∆CoVaR. finally, the systemic risk contribution is closely related to mentioned bank characteristic variables <br />JEL: G11, G21, G32 <br />How to cite this paper: Rastegar, M. & Karimi, N. (2016). Systemic Risk in TSE Banking Sector. Quarterly Journal of Risk Modeling and Financial Engineering, 1(1), 1–19. (In Persian)Systemic risk is the risk of collapse in the financial system. Due to the financial crisis that hit the world economy in 2008, the study of systemic risk in the banking sector became more attractive for researchers. In this research we study systemic risk in the Iranian banking sector by using a conventional systemic risk measure, ∆CoVaR. To compute the measure, we employ dynamic conditional correlation model. For this purpose, we estimate the mentioned systemic risk measure of the seven Iranian banks from March of 2010 to March of 2015. Then using panel data regression, we investigate the relationships between the systemic risk measure and certain bank characteristic variables (i.e. VaR, size-Log of Equity, Leverage ratio). Our empirical findings shows that, the systemic risk contributions of banking sector is high and studied banks has different ranking based on ∆CoVaR. finally, the systemic risk contribution is closely related to mentioned bank characteristic variables <br />JEL: G11, G21, G32 <br />How to cite this paper: Rastegar, M. & Karimi, N. (2016). Systemic Risk in TSE Banking Sector. Quarterly Journal of Risk Modeling and Financial Engineering, 1(1), 1–19. (In Persian)https://jferm.khatam.ac.ir/article_40549_ede19f40cd703dbe1de4e8ee121b4d46.pdfJournal of Risk modeling and Financial Engineering2538-53721120160922The Effects of Size and Revenue Diversification on Systemic Risk for Listed Banks in TSEThe Effects of Size and Revenue Diversification on Systemic Risk for Listed Banks in TSE203640583FASeyed FarhangHoseiniPhD Candidate, Finance, University of Tehran, IranSeyedeh FatemehMostafaviMSc. Student, Financial Management, Ershad Damavand Higher Education Institute,Tehran, IranJournal Article20160519This study aims to examine the relationship between size, revenue diversity, and their interactive effects on systemic risk in private banking. The systemic risk can be measured bases on the Marginal Expected Shortfall (MES). The MES is the average return on bank stocks on days when the return in the banking industry falls below the value at risk (VaR). The analysis of combined data from eight banks listed on the Tehran Stock Exchange during the period 1999-2012 is used to estimate the regression equation. The results reveal that the bank's revenue diversification measured by the non-interest income (NII) has a reverse impact on the systemic risk. In other words, banks with a higher share of interest income in their revenue portfolio show higher systemic risk and they are more risky for the financial system if a crisis occurs. Furthermore, larger banks will have a higher impact of revenue diversity on reduced systematic risks. In fact, such banks will benefit much more from revenue diversification, and any increase in the NII can lead to a significant reduction in the systemic risk. The results indicate that it is difficult to completely accept the size effect on the systemic risks for the banks under study.
JEL: G20، G32
How to cite this paper: Hosseini, S. F., & Mostafavi, S. F. (2016). The Effects of Size and Revenue Diversification on Systemic Risk for Listed Banks in TSE. Quarterly Journal of Risk Modeling and Financial Engineering, 1(1), 20–36. (In Persian)This study aims to examine the relationship between size, revenue diversity, and their interactive effects on systemic risk in private banking. The systemic risk can be measured bases on the Marginal Expected Shortfall (MES). The MES is the average return on bank stocks on days when the return in the banking industry falls below the value at risk (VaR). The analysis of combined data from eight banks listed on the Tehran Stock Exchange during the period 1999-2012 is used to estimate the regression equation. The results reveal that the bank's revenue diversification measured by the non-interest income (NII) has a reverse impact on the systemic risk. In other words, banks with a higher share of interest income in their revenue portfolio show higher systemic risk and they are more risky for the financial system if a crisis occurs. Furthermore, larger banks will have a higher impact of revenue diversity on reduced systematic risks. In fact, such banks will benefit much more from revenue diversification, and any increase in the NII can lead to a significant reduction in the systemic risk. The results indicate that it is difficult to completely accept the size effect on the systemic risks for the banks under study.
JEL: G20، G32
How to cite this paper: Hosseini, S. F., & Mostafavi, S. F. (2016). The Effects of Size and Revenue Diversification on Systemic Risk for Listed Banks in TSE. Quarterly Journal of Risk Modeling and Financial Engineering, 1(1), 20–36. (In Persian)https://jferm.khatam.ac.ir/article_40583_519127d603560f8834e310e3b4bad1e7.pdfJournal of Risk modeling and Financial Engineering2538-53721120160922Determinants of Non-Performing Loans among Iranian BanksDeterminants of Non-Performing Loans among Iranian Banks375740450FAReza EyvazloEyvazloAssistant Prof., Faculty of Management, University of Tehran, IranMohammadRezaAghamohammad SemsarPhD Candidate, Finance, Faculty of Management, University of Tehran, IranMehdiRameshgMSc., Finance, Faculty of Management, University of Tehran, IranJournal Article20160709Non-Performing Loans (NPLs) is considered as an index to assess asset quality of depository institutions and mostly is related with financial crisis and failures. An Increase in NPLs resulted risk enhancement in banks and expose them to credit risk and liquidity risk seriously which could lead to bank failure. This paper distinguished interbank and macroeconomic factors affecting NPLs among Iranian banks. This research uses GMM panel data to study sample banks between 2006 and 2014 results show that there is a significant relationship between bank specific factors and macroeconomic factors with non performing loans among Iranian Banks. <br /><span>JEL: C61, G11</span> <br />How to cite this paper: Eyvazloo, R., Aghamohammad Semsar, M., & Rameshg, M. (2016). Determinants of Non-Performing Loans among Iranian Banks. Quarterly Journal of Risk Modeling and Financial Engineering, 1(1), 37–57. (In Persian)Non-Performing Loans (NPLs) is considered as an index to assess asset quality of depository institutions and mostly is related with financial crisis and failures. An Increase in NPLs resulted risk enhancement in banks and expose them to credit risk and liquidity risk seriously which could lead to bank failure. This paper distinguished interbank and macroeconomic factors affecting NPLs among Iranian banks. This research uses GMM panel data to study sample banks between 2006 and 2014 results show that there is a significant relationship between bank specific factors and macroeconomic factors with non performing loans among Iranian Banks. <br /><span>JEL: C61, G11</span> <br />How to cite this paper: Eyvazloo, R., Aghamohammad Semsar, M., & Rameshg, M. (2016). Determinants of Non-Performing Loans among Iranian Banks. Quarterly Journal of Risk Modeling and Financial Engineering, 1(1), 37–57. (In Persian)https://jferm.khatam.ac.ir/article_40450_bca4eb633440fb01269f3c3d5b91faaf.pdfJournal of Risk modeling and Financial Engineering2538-53721120160922Profitability of Maskan Bank Credit Cards: Markov Decision ProcessProfitability of Maskan Bank Credit Cards: Markov Decision Process587540542FARezaHabibiAssistant Prof. Iran Banking Institute, Central Bank of Iran, Tehran, IranHasanKouhiAssistant Prof. Iran Banking Institute, Central Bank of Iran, Tehran, IranMohammadShamaniMSc., Banking, Iran Banking Institute, Central Bank of Iran, Tehran, IranJournal Article20160612Credit card is one of the most profitable instrument in almost every banking system. Indeed, the widely use of credit cards, during the last three decades, among consumers and businessman's is a sign of this claim. Thus, the profitability of credit cards of bank maskan is studied, in this paper. This paper considers an application of Bellman equation in determing the credit card caps. Under a Markov decision process (MDP) setting, the transition matrix are computed and they are used in Bellman equation. The time period of study is the starting day of 2011 to the last day of 2012. To this aim, first, using the Markov chain process, the transition probability matrix of consumers who have credit cards is computed, using their behavior score and after that the added value induced by consumers is calculated. This matrix is useful in determining the optimal policy of credit cards caps. Finally, by using the MDP, a suitable policy is chosen regarding the change of credit cards caps.
<span>JEL</span><span>: C61; C65</span>
How to cite this paper: Habibi, R., Kouhi, H., & Shamani, M. (2016). Profitability of Maskan Bank Credit Cards: Markov Decision Process. Quarterly Journal of Risk Modeling and Financial Engineering, 1(1), 58–75. (In Persian)Credit card is one of the most profitable instrument in almost every banking system. Indeed, the widely use of credit cards, during the last three decades, among consumers and businessman's is a sign of this claim. Thus, the profitability of credit cards of bank maskan is studied, in this paper. This paper considers an application of Bellman equation in determing the credit card caps. Under a Markov decision process (MDP) setting, the transition matrix are computed and they are used in Bellman equation. The time period of study is the starting day of 2011 to the last day of 2012. To this aim, first, using the Markov chain process, the transition probability matrix of consumers who have credit cards is computed, using their behavior score and after that the added value induced by consumers is calculated. This matrix is useful in determining the optimal policy of credit cards caps. Finally, by using the MDP, a suitable policy is chosen regarding the change of credit cards caps.
<span>JEL</span><span>: C61; C65</span>
How to cite this paper: Habibi, R., Kouhi, H., & Shamani, M. (2016). Profitability of Maskan Bank Credit Cards: Markov Decision Process. Quarterly Journal of Risk Modeling and Financial Engineering, 1(1), 58–75. (In Persian)https://jferm.khatam.ac.ir/article_40542_3cae6caf17858f5e6e9f6d277f7af617.pdfJournal of Risk modeling and Financial Engineering2538-53721120160922Robust Portfolio Optimization using Contamination TechniqueRobust Portfolio Optimization using Contamination Technique769640431FAKhadijehHassanlouAssistant Prof., Faculty of Engineering, Khatam University, Tehran, Iran.Journal Article20161008In this paper the robust optimization is used for portfolio opimization problem. using estimates in the portfolio optimization process, will cause estimation risk. Therefore, methods should be used that have ability to decrease estimation risk and robust optimization can be one of the proper methods in addressing uncertainty. In this research the contamination technique is used for assessing robustness of portfolio. Due to sharp icrease in Iran stock exchange indexes in 2013, this technique is used and these sharp changes are intered in model as contamination scenarios. It is assumed that the probability distribution of return has fluctuated then the effects of this fluctuation on optimal portfolio are discussed. Conditional Value at Risk is used for measuring the risk of portfolio. Portfolio robustness and sensitivity analysis showed that the probability of contamination scenario affects the function of minimum risk so it needs to be controlled. Finally after efficiency test of given portfolio showed that it is not efficient therefore the new one with less risk is determined.
<span>JEL: C61, G11</span>
How to cite this paper: Hassanlou, K. (2016). Robust Portfolio Optimization using Contamination Technique. Quarterly Journal of Risk Modeling and Financial Engineering, 1(1), 76–96. (In Persian)In this paper the robust optimization is used for portfolio opimization problem. using estimates in the portfolio optimization process, will cause estimation risk. Therefore, methods should be used that have ability to decrease estimation risk and robust optimization can be one of the proper methods in addressing uncertainty. In this research the contamination technique is used for assessing robustness of portfolio. Due to sharp icrease in Iran stock exchange indexes in 2013, this technique is used and these sharp changes are intered in model as contamination scenarios. It is assumed that the probability distribution of return has fluctuated then the effects of this fluctuation on optimal portfolio are discussed. Conditional Value at Risk is used for measuring the risk of portfolio. Portfolio robustness and sensitivity analysis showed that the probability of contamination scenario affects the function of minimum risk so it needs to be controlled. Finally after efficiency test of given portfolio showed that it is not efficient therefore the new one with less risk is determined.
<span>JEL: C61, G11</span>
How to cite this paper: Hassanlou, K. (2016). Robust Portfolio Optimization using Contamination Technique. Quarterly Journal of Risk Modeling and Financial Engineering, 1(1), 76–96. (In Persian)https://jferm.khatam.ac.ir/article_40431_5ef59e8d087a235d7aebb290a7a9e3cd.pdfJournal of Risk modeling and Financial Engineering2538-53721120160922Liquidity in Iranian Stock Market, Predicting Market Depth Using Intraday DataLiquidity in Iranian Stock Market, Predicting Market Depth Using Intraday Data9711339358FASaeedRahimianPostdoctoral Researcher, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, IranJournal Article20160531Liquidity of an asset is a key concept in financial markets. Intuitively, liquidity can be interpreted as transacting an asset rapidly and at a low cost. Despite its importance, finding a precise measure for this concept is not an easy task. In this paper, by using tick-by-tick transaction and limit order book data, we calculate VNET measure of liquidity for 16 stocks in Iranian financial market. <br />This measure, introduced by Engle and Lang in 2001, measures the excess volume of buy and sell that leads to a specific price movement. The results show that the market depth for different stocks is time varying and the variation is significantly correlated with volatility. This is consistent with the prediction of asymmetric information models, in which higher volatility corresponds to higher probability of the presence of informed traders. <br /><span>JEL: D82, G1 </span> <br />How to cite this paper: Rahimian, S. (2016). Liquidity in Iranian Stock Market, Predicting Market Depth Using Intraday Data. Quarterly Journal of Risk Modeling and Financial Engineering, 1(1), 67–113. (In Persian)Liquidity of an asset is a key concept in financial markets. Intuitively, liquidity can be interpreted as transacting an asset rapidly and at a low cost. Despite its importance, finding a precise measure for this concept is not an easy task. In this paper, by using tick-by-tick transaction and limit order book data, we calculate VNET measure of liquidity for 16 stocks in Iranian financial market. <br />This measure, introduced by Engle and Lang in 2001, measures the excess volume of buy and sell that leads to a specific price movement. The results show that the market depth for different stocks is time varying and the variation is significantly correlated with volatility. This is consistent with the prediction of asymmetric information models, in which higher volatility corresponds to higher probability of the presence of informed traders. <br /><span>JEL: D82, G1 </span> <br />How to cite this paper: Rahimian, S. (2016). Liquidity in Iranian Stock Market, Predicting Market Depth Using Intraday Data. Quarterly Journal of Risk Modeling and Financial Engineering, 1(1), 67–113. (In Persian)https://jferm.khatam.ac.ir/article_39358_2105fb55b816d4efc15b9566daa3db86.pdfJournal of Risk modeling and Financial Engineering2538-53721120160922A Comparison between Performance of Linear and Nonlinear Capital Asset Pricing Models in TSEA Comparison between Performance of Linear and Nonlinear Capital Asset Pricing Models in TSE11412839096FAMehdiAsimaPhD. Candidate, Banking Finance, Faculty of Management, University of Tehran, IranAmir AliAbbaszadeh AslMSc, Financial Engineering, Faculty of Management, University of Tehran, IranJournal Article20160525Capital asset pricing model (CAPM) has been among the common models to estimate expected rate of returns. Since the linearity assumption is considered in the standard version of the capital asset pricing model, estimating beta in nonlinear setting will be inconsistent and bias-oriented. Therefore, in this study, predictive power of the Linear and Nonlinear Capital Asset Pricing Models in Tehran Stock Exchange in the period from 2006 to 2015 has been tested. Semiparametric method and local kernel regression are utilized in order to estimate nonlinear model. For this purpose, expected returns has estimated with regard to two above mentioned models during period of the study and the results have compared with realized returns. Mean absolute percentage error and especially Diebold-Mariano test are used to measure predictive power of the models. The results indicate that considering nonlinearity relation between stock returns and market returns increases predictive power of realized returns.
<span>JEL: C12, G14</span>
How to cite this paper: Asima, M, & Abbaszadeh Asl, A. (2016). A Comparison between Performance of Linear and Nonlinear Capital Asset Pricing Models in TSE. Quarterly Journal of Risk Modeling and Financial Engineering, 1(1), 114–128. (In Persian)Capital asset pricing model (CAPM) has been among the common models to estimate expected rate of returns. Since the linearity assumption is considered in the standard version of the capital asset pricing model, estimating beta in nonlinear setting will be inconsistent and bias-oriented. Therefore, in this study, predictive power of the Linear and Nonlinear Capital Asset Pricing Models in Tehran Stock Exchange in the period from 2006 to 2015 has been tested. Semiparametric method and local kernel regression are utilized in order to estimate nonlinear model. For this purpose, expected returns has estimated with regard to two above mentioned models during period of the study and the results have compared with realized returns. Mean absolute percentage error and especially Diebold-Mariano test are used to measure predictive power of the models. The results indicate that considering nonlinearity relation between stock returns and market returns increases predictive power of realized returns.
<span>JEL: C12, G14</span>
How to cite this paper: Asima, M, & Abbaszadeh Asl, A. (2016). A Comparison between Performance of Linear and Nonlinear Capital Asset Pricing Models in TSE. Quarterly Journal of Risk Modeling and Financial Engineering, 1(1), 114–128. (In Persian)https://jferm.khatam.ac.ir/article_39096_96be41dfa85e077e905739214c320474.pdf