نوع مقاله : مقاله پژوهشی

نویسندگان

گروه مدیریت مالی، دانشکده مدیریت، دانشگاه تهران، تهران، ایران.

10.22105/dmor.2022.312912.1518

چکیده

هدف: در این تحقیق، به بررسی تأثیر مقیاس-زمان نوسانات دارایی­ها (ارز، سهام و مسکن) بر کارایی شبکه بانکی در دوره زمانی 1399:4-1388:1 به‌صورت فصلی با استفاده از الگوی مارکوف سویچینگ پرداخته شده است.
روش‌شناسی پژوهش: در پژوهش حاضر ابتدا به محاسبه کارایی شبکه بانکی با استفاده از الگوی تحلیل پوششی داده با داده­های بوت‌استرپ می‌پردازیم. سپس نوسانات بازارهای دارایی (نرخ ارز، شاخص بازار سهام و شاخص قیمت مسکن) را با استفاده از الگوی تبدیل موجک استخراج کرده و به بررسی تأثیر نوسانات بازارهای دارایی بر میزان کارایی شبکه بانکی کشور در قالب الگوی چرخشی مارکوف و مشاهده تأثیرگذاری آن‌ها در سطوح کارایی بالا و پایین خواهیم پرداخت.
یافته ‎ها: میانگین کارایی شبکه بانکی کشور در دوره مورد بررسی حدود 1/56 درصد بوده است که نشان می­دهد کارایی مناسب نبوده است. نوسانات کوتاه­مدت نرخ ارز در حالتی که کارایی شبکه بانکی در سطح و رژیم بالا می­باشد تأثیر منفی و معنادار دارد اما چنانچه نوسانات ارز بلندمدت باشد فارغ از رژیم و سطح کارایی شبکه بانکی تأثیر منفی و معنادار دارد. نوسانات کوتاه­مدت شاخص بازار سهام در شرایطی که سطح کارایی شبکه بانکی پایین است تأثیر مثبت و معنادار داشته است. اما چنانچه نوسانات در بازار سهام ادامه­دار باشد فارغ از سطح و رژیم کارایی شبکه بانکی تأثیر منفی و معنادار دارد. نوسانات کوتاه­مدت در بازار مسکن در حالتی که کارایی شبکه بانکی در سطح بالا باشد تأثیر مثبت و معنادار داشته است. اما در نقطه مقابل نوسانات بلندمدت در این بازار و در شرایطی که کارایی شبکه بانکی در سطح بالا باشد می­تواند منجر به کاهش معنادار آن شود. بنابراین با ایجاد ثبات در اقتصاد (عدم تغییرات زیاد نرخ ارز، شاخص سهام و مسکن)  می­توان بهبود کارایی شبکه بانکی کشور را با توجه به سطح و رژیم آن انتظار داشت.
اصالت/ارزش افزوده علمی:  از جمله مسائلی که از منظر سیاست­گذاری می‌تواند حائز اهمیت باشد درنظرگرفتن  تأثیر نوسانات بازارهای دارایی در دوره های زمانی مختلف بر سطوح مختلف کارایی شبکه بانکی کشور می‌باشد. زیرا ممکن است در سطوح مختلف کارایی شبکه بانکی و همچنین دوره­های زمانی مختلف نوسانات بازارهای دارایی، تأثیر متفاوتی برجای گذارند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

The time-scale effect of volatility of asset market on the efficiency of the country's banking network with emphasis on regime change

نویسندگان [English]

  • Reza Raei
  • Saeed Bajalan
  • Zahra Saedi

Department of Finanacial Management, Faculty of Management, University of Tehran, Tehran, Iran.

چکیده [English]

Purpose: In this research, the effect of scale-time volatility of assets (currency, stocks and housing) on the efficiency of the banking network in the period 1399: 4-1388: 1 has been studied quarterly using the Markov switching model.
Methodology: In this study, we first calculate the efficiency of the bank network using the data envelopment analysis model with bootstrap data. Then, the volatility of asset market (exchange rate, stock market index and housing price index) extracted using the wavelet conversion pattern and examines the impact of volatility of asset market on the efficiency of the country's banking network in the form of the Markov switching model and observing their effect on different levels of efficiency.
Findings: The average efficiency of the country's banking network in the study period has been about 56.1%, which indicates that efficiency has not been appropriate. The short-term volatility of the exchange rate in the state that the  efficiency of the bank network and the high regime has a negative and significant effect, but if the long-term exchange  volatility, regardless of the regime and the level of banking network  efficiency, has a negative and significant effect. The short-term volatility of the stock market index have had a positive and significant effect on the level of low banking network efficiency. But if volatility are continued in the stock market, regardless of the level and regime, the efficiency of the banking network has a negative and significant effect. The short-term  volatility in the housing market have had a positive and significant effect on the level of bank network efficiency but in the opposite side of the long-term volatility in this market and in a high level of bank network  efficiency, it can lead to significant reductions. Therefore, by stabilizing the economy (lack of large exchange rate, stock index and housing), it can be expected to improve the efficiency of the country's banking network due to its level and regime.
Originality/Value: One of the issues that can be important in policy making perspective is to consider the impact of volatility of assets market in different time periods on different levels of banking network efficiency. Because they may have a different impact on different levels of bank network efficiency as well as different periods of volatility of assets market. 

کلیدواژه‌ها [English]

  • Exchange rate
  • Stock Market Index
  • Housing
  • Banking Network Efficiency
  • Markov switch
Ahmad, N. H., & Noor, M. A. N. M. (2011). The determinants efficiency and profitability of world Islamic banks. 2010 international conference on e-business, management and economics (Vol. 3, pp. 228-233). IACSIT Press. http://www.ipedr.com/vol3/47-M10013.pdf
Amiri, H. (2018). Evaluation the effectiveness of selected banks in Iran and its relationship with banking internal and macroeconomic variables. Journal of applied economics studies in Iran, 7(26), 89-114. (In Persian). https://aes.basu.ac.ir/article_2313_en.html?lang=fa
Andrieș, A. M., & Ursu, S. G. (2016). Financial crisis and bank efficiency: an empirical study of European banks. Economic research-Ekonomska istraživanja, 29(1), 485-497.
Babazadeh, M., Farokhnejad, F., & Aghababaei, M. E. (2011). Effects of changes in the exchange rates on the banksprofitability in short-term and long term: VECM approach. Journal of monetary and banking research, 4(9), 205-225. (In Persian). https://ideas.repec.org/a/mbr/jmbres/v4y2011i9p205-225.html
Banai, Á., & Vágó, N. (2018). The effect of house prices on bank risk: empirical evidence from Hungary. National Bank of Poland Education & Publishing Department.
Barros, C. P., & Wanke, P. (2014). Banking efficiency in Brazil. Journal of international financial markets, institutions and money, 28, 54-65.
Barros, C. P., Managi, S., & Matousek, R. (2012). The technical efficiency of the Japanese banks: non-radial directional performance measurement with undesirable output. Omega, 40(1), 1-8.
Bastanzad, H. (2015). New policy environment to achieve monetary policy goals: a case study of Iran. Twenty-fifth annual monetary policy conference. Monetary and Banking Research Institute, Gharchak, Iran. (In Persian). https://civilica.com/doc/842895/
Batir, T. E., Volkman, D. A., & Gungor, B. (2017). Determinants of bank efficiency in Turkey: participation banks versus conventional banks. Borsa Istanbul review, 17(2), 86-96.
Berger, A. N., & Humphrey, D. B. (1997). Efficiency of financial institutions: international survey and directions for future research. European journal of operational research, 98(2), 175-212.
Bikker, J., & Bos, J. W. (2008). Bank Performance: a theoretical and empirical framework for the analysis of profitability, competition and efficiency. Routledge.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.
Cooper, W. W., Seiford, L. M., & Zhu, J. (2011). Handbook on data envelopment analysis. Springer.
Fallah Mehrjerdi, M., Shahmoradi, N., & Dehestani, M. A. (2016). Evaluating the efficiency and determining the optimal structure of resources and performance indicators of public and private banks in Iran using a multi-period model of data envelopment analysis. International conference on management, accounting, educational sciences and resistance economics. Minoo University of Applied Sciences - Office of the International Confederation of World Inventors in Iran, Tehran. (In Persian). https://civilica.com/doc/535550/ 
Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the royal statistical society: series a (general), 120(3), 253-281.
Fukuyama, H., & Weber, W. L. (2008). Japanese banking inefficiency and shadow pricing. Mathematical and computer modelling, 48(11-12), 1854-1867.
Gholizadeh, A. A., & Shalyari, F. (2017). The investigation of macroeconomic variables effect on credit risk Iranian banking system. Journal of Islamic economics & banking, 6(20), 183-200. (In Persian). http://mieaoi.ir/article-1-529-fa.html
Gholizadeh, A., & Golzarian Pour, S. (2019). Investigating the effect of housing prices on non-performing loans in banking system of Iran. Quarterly journal of applied theories of economics, 6(3), 189-214. (In Persian). https://ideas.repec.org/a/ris/qjatoe/0163.html
Gilkeson, J. H., & Smith, S. D. (1992). The convexity trap: pitfalls in financing mortgage portfolios and related securities. Economic review-Federal Reserve Bank of Atlanta77(6), 14-27.
Golbazkhanian pour, G., Fazel Yazdi, A., & Tahari Mehrjardi, M. (2013). Identifying the relative efficiency of banks, using the data envelopment analysis and fuzzy multi-attribute decision-making approach (case study: the bank accepted in the Tehran Stock Exchange). Journal of investment knowledge, 2(7), 85-104. (In Persian). https://jik.srbiau.ac.ir/article_7511.html?lang=en
Goswami, R., Hussain, F., & Kumar, M. (2019). Banking efficiency determinants in India: a two-stage analysis. The journal of applied economic research, 13(4), 361-380.
Hadi, A., & Khajvand, M. (2015). Investigating the role of non-performing loans parameter in predicting branch efficiency by combining research methods in operations and data mining. Fifth national conference on electronic banking and payment systems. Monetary and Banking Research Institute, Tehran. (In Persian). https://civilica.com/doc/785602/
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 59(2), 357-384.
Hassanzadeh, A. (2007). Efficiency and its determinants in the Iranian banking system. Journal of economic essays, 4(7), 75-98. (In Persian). https://www.sid.ir/en/Journal/ViewPaper.aspx?ID=110511
Hemmti, H., & Abbasifar, A. (2016). Effect of stock market volatility on banks performance accepted in Tehran Stock Exchange. Journal of economics and business research, 6(10), 13-26. (In Persian). http://jebr.azad.ac.ir/article_526596_112715.html?lang=en
Hollingsworth, B., & Smith, P. (2003). Use of ratios in data envelopment analysis. Applied economics letters, 10(11), 733-735.
Iacoviello, M. (2005). House prices, borrowing constraints, and monetary policy in the business cycle. American economic review, 95(3), 739-764.
Karimkhani, A., & Forati, M. (2012). Investigating the effect of macroeconomic variables on banks' resources and expenditures. Sepah Bank Risk Research and Control Office. (In Persian). https://civilica.com/doc/1236653/
Kuan, C. M. (2002). Lecture on the Markov switching model. Institute of economics academia sinica, 8(15), 1-30.
Lagat, C. C., & Nyandema, D. M. (2016). The influence of foreign exchange rate fluctuations on the financial performance of commercial banks listed at the Nairobi Securities Exchange. British journal of marketing studies, 4(3), 1-11.
Mishkin, F. S. (1992). The economics of money, banking, and financial markets. NewYork: Harper Collins Inc.
Nilchi, M., E., Fadaeinejad, M. E., Razavi Hajiagha, S. H., & Badri, A. (2017). Providing new multi-component data envelopment analysis to evaluate efficiency of bank branches. Journal of industrial management studies, 15(46), 73-96. (In Persian). https://www.sid.ir/en/Journal/ViewPaper.aspx?ID=577455
Osundina, C. K., Osundina, J. A., Jayeoba, O. O., & Olayinka, I. M. (2016). Exchange rate volatility and banks performance: Evidence from Nigeria. International journal of economics and business management, 2(4), 1-11.
Paradi, J. C., & Zhu, H. (2013). A survey on bank branch efficiency and performance research with data envelopment analysis. Omega, 41(1), 61-79.
Partovi, E., & Matousek, R. (2019). Bank efficiency and non-performing loans: evidence from Turkey. Research in international business and finance, 48, 287-309. https://doi.org/10.1016/j.ribaf.2018.12.011
Pazoki, N., Hamidian, A., Mohammadi, S., & Mahmoudi, V. (2013). Correlation analysis of stock exchange index, oil price, exchange rate and gold price: a wavelet decomposition method‎. Journal of investment knowledge, 2(7), 131-148.
Roudari, S., Homayounifar, M., & Salimifar, M. (2020). The effect of exchange rate and stock index fluctuations on the efficiency of agricultural facilities. Journal Of agricultural economics and development, 34(1), 81-96. (In Persian). DOI: 10.22067/JEAD2.VI0.84763
Saunders, A., & Yourougou, P. (1990). Are banks special? the separation of banking from commerce and interest rate risk. Journal of economics and business, 42(2), 171-182.
Scheel, H. (2001). Undesirable outputs in efficiency valuations. European journal of operational research, 132(2), 400-410.
Seyednourani, S., & Ebadi, M. (2020). Evaluation of performance of Iranian commercial banks method: Bootstrap algorithm. Macroeconomics research letter, 14(28), 169-198. (In Persian). DOI: 10.22080/IEJM.2020.17001.1703
Shahraki, J., Shahiki Tash, M. N., & Khajeh Hassani, M. (2016). Evaluation of Iranian banking system using Bootstrap data envelopment analysis approach and SW algorithm. Journal of monetary and banking researches, 9(28), 299-326. (In Persian). https://ideas.repec.org/a/mbr/jmbres/v9y2016i28p299-326.html
Sherman, H. D., & Gold, F. (1985). Bank branch operating efficiency: evaluation with data envelopment analysis. Journal of banking & finance, 9(2), 297-315.
Simar, L., & Wilson, P. W. (1998). Sensitivity analysis of efficiency scores: how to bootstrap in nonparametric frontier models. Management science, 44(1), 49-61.
Tuo, M. (2016). An empirical analysis of Chinese commercial banks’ efficiency and influencing factors—under the constraint of non-performing loans. American journal of industrial and business management, 6(4), 455-466.
Zamani, Z., Jannati, A., & Ghorbani, M. (2017). The impact of currency fluctuations on Iran's banking system performance. Journal of Islamic finance and banking studies, 4(8), 81-104. (In Persian). https://jifb.ibi.ac.ir/article_65139.html 
Zhou, L., & Zhu, S. (2017). Research on the efficiency of Chinese commercial banks based on undesirable output and super-SBM DEA model. Journal of mathematical finance, 7(01), 102-121. DOI: 10.4236/jmf.2017.71006