نوع مقاله : مقاله پژوهشی

نویسندگان

1 کاندیدای دکتری اقتصاد، دانشگاه فردوسی، مشهد، ایراندانشگاه فردوسی مشهد

2 دانشیار گروه اقتصاد، دانشگاه فردوسی،مشهد، ایران

3 استاد گروه اقتصاد، دانشگاه فردوسی،مشهد، ایران

چکیده

شبکه بانکی نقش برجسته­ای در تامین مالی کسب و کارها ایفا می­نماید. در سال­های گذشته بواسطه افزایش مخارج جاری دولت و عدم افزایش متناسب درآمدهای دولت، کسری بودجه را ایجاد نموده است و بدلیل وابستگی زیاد بین دولت و شبکه بانکی در برخی موارد افزایش مخارج از طریق اخذ تسهیلات و استقراض از شبکه بانکی تامین شده است. از سوی دیگر همبستگی میان نوسانات مخارج جاری دولت، بدهی دولت به شبکه بانکی و نرخ ارز با توجه به دوره زمانی بروز نوسانات متفاوت باشد. همین منظور در پژوهش حاضر با استفاده از الگوی تبدیل موجک در بازه زمانی 1397-1388 بصورت ماهانه،  نوسانات نرخ ارز اسمی، بدهی دولت به شبکه بانکی و مخارج جاری دولت در سه سطح تجزیه شده است. نتایج نشان می­دهد هرچه دوره زمانی نوسانات افزایش یابد همبستگی نیز افزایش می­یابد. بر این اساس کمترین همبستگی مثبت در طی زمان مربوط به نرخ ارز و مخارج جاری دولت می­باشد که در بلندمدت به 28 درصد افزایش می­یابد. همبستگی میان نوسانات نرخ ارز و بدهی دولت به شبکه بانکی از 17 درصد در کوتاه­مدت به 53 درصد در بلندمدت می­رسد و بیشترین میزان همبستگی میان نوسانات مخارج جاری دولت و بدهی دولت به شبکه بانکی می­باشد که از 32.5 درصد در کوتاه­مدت به 76 درصد در بلندمدت افزایش می­یابد. درواقع براساس نتایج شبکه بانکی به عنوان ابزاری جهت پوشش مخارج جاری دولت بوده است و با توجه به بروز نوسانات ارزی در کشور و افزایش مخارج جاری دولت، می­تواند بدهی دولت به شبکه بانکی نیز افزایش یابد و توان اعتباری شبکه بانکی را کاهش دهد.

کلیدواژه‌ها

عنوان مقاله [English]

Foreign Exchange rate Volatilies, Government Debt to the Banks and Current Government Spending: Wavelet Transform approach

نویسندگان [English]

  • soheil roudari 1
  • masod homayounifar 2
  • mostafa salimifar 3

1 Ph.D. Candidate of Economics, Ferdowsi University,Mashad, Iran

2 Associate Professor in Economics, Ferdowsi University,Mashad, Iran

3 Professor in Economics, Ferdowsi University,Mashad, Iran

چکیده [English]

Introduction: The banking network plays a prominent role in the financing of businesses. In recent years, due to increased government spending and disproportionate increases in government revenues, a budget deficit has been created, and due to the high dependence between the government and the banking network, in some cases increased current spending has been provided through borrowing from the banking network.
Theoretical Framework: One of the most important factors that effect on the formation of the financial crisis is the instability in other financial markets, especially the exchange rate, which affects the GDP of the country and the current expenditures of the government, and affecting the performance of the banking sector subsequently. By affecting the government budget, the exchange rate can affect the motivation of the government and government-affiliated companies to obtain loans and facilities from the banking network. Also, the increase in the exchange rate by increasing the cost of goods and services leads to a decrease in disposable income and subsequently a decrease in people's consumption. According to dependence of industrial sector to imports of intermediary goods, changes in exchange rate causes a change in the supply sector (Boschi & D' Addona, 2019). On the other hand, exchange rate volatility due to the uncertainty and increases in the cost of production has been effective on government debt to the banking system and current expenditures (Adrian & Shin,2010).
Methodology: In this study, using the wavelet transform model during the period of 1388-1397 monthly, the nominal exchange rate volatilities, government debt to the banking network, and current government expenditures are divided into three levels by using wavelet transform. In fact, wavelet transform explains the deviation from the main trend. To examine the relationship between the variables, the use of patterns such as Granjer causality is used, which provides a momentary criterion of causality test, therefore, it is unable to analyze the dynamics and reliability of variables relationship. In addition, in such methods, because the lag of variables can be used, it is possible to eliminate the immediate effects. Spectral analysis is used to solve this problem (Aguiar, et al.,2008).
Results and Discussion: In the short term, there is no significant correlation between nominal exchange rate fluctuations and current government spending fluctuations. Interestingly, there is a significant correlation between government debt to banking network fluctuations and exchange rate fluctuations. This indicates that about 17% of the fluctuations in the foreign exchange market and government debt to the banking network are consistent. Significantly, there is a relatively high correlation between government debt to banking network fluctuations  and current government spending fluctuations in the short term, and about 32.5 percent of changes and fluctuations in each have led to a change in the other one, and in fact It can show the lack of independence of the country's banking network and the dependence and attitude of the government to provide current expenses from this source. There is a positive and significant correlation between nominal exchange rate fluctuations and current government spending fluctuations in the medium term. Of course, only about 19% of the fluctuations in each are positively followed by other fluctuations. In the medium term, the movement between exchange rate fluctuations and government debt to banking network fluctuations increases compared to the short-term (0.26), and this can also indicate the delayed effects of the exchange rate. Interestingly, there is a high correlation between government debt to banking network fluctuations and current government spending fluctuations, and over a longer period the fluctuations between the two are more intense in terms of intensity and direction. The time factor plays a very important role in the correlation between government debt fluctuations and exchange rate fluctuations. The correlation between these two cases started from about 0.17 in the short term and reached 0.53 in the long run. In terms of time factor, it has shown more biger about fluctuations in current government expenditures and fluctuations in government debt to banks than the other cases. The correlation between the two fluctuations has risen from 32.5 percent in the short term to 76 percent in the long term.
Conclusions and Suggestions: government and the banking network have a close relationship with each other, and this relationship is due to the fact that many of the country's banks are state-owned be greater in the long run. In fact, this is one of the main reasons for the non-performing loans in the country's banking network, and the government has used its bargaining power to cover its current expenditures, which have been very volatile in recent years and take loans and did not pay on time. In fact, based on the results, banking network has been a tool to cover current government expenditures, and due to exchange rate fluctuations in the country and increasing government current expenditures, government debt to the banking network can increase and reduce the credit ability of the banking network and can lead to inefficient allocation of resources.

کلیدواژه‌ها [English]

  • Volatilities
  • Nominal Exchange Rate
  • Current Government Expenditures
  • Government Debt to the Banks
  • Wavelet transform
[1]     Adrian, T., & Shin, H. S. (2010). Liquidity and leverage. Journal of financial intermediation, 19(3), 418-437.
[2]     Aguiar-Conraria, L., Azevedo, N., & Soares, M. J. (2008). Using wavelets to decompose the time–frequency effects of monetary policy. Physica A: Statistical mechanics and its Applications387(12), 2863-2878.
[3]     Auerbach, A. J., & Gorodnichenko, Y. (2016). Effects of fiscal shocks in a globalized world. IMF Economic Review64(1), 177-215.
[4]     Boschi, M., & d'Addona, S. (2019). The stability of tax elasticities over the business cycle in European countries. Fiscal Studies, 40(2), 175-210.
[5]     Castro, V. (2013). Macroeconomic determinants of the credit risk in the banking system: The case of the GIPSI. Economic Modelling, 31, 672-683.
[6]     Dimitrios, A., Helen, L., & Mike, T. (2016). Determinants of non-performing loans: Evidence from Euro-area countries. Finance research letters18, 116-119.
[7]     Durringer, F. (2009). The Trilemma: An Empirical Assessment over 35 years since the 1970s (No. gd09-069). Institute of Economic Research, Hitotsubashi University.
[8]     Eisavi, M., Ghelich,V.(2014). The ability of Islamic bonds to offset the government  budget deficit as a policy tool. Journal of Economic research, 15(56), 105-134 (In Persian).
[9]     Ghaffari, F., & Farhadi, A. (2016). An Analysis of Improving the Capability of Explaining ARCH model and State Space Using Haar Wavelet Transform and Monte Carlo simulation (The case study of the prediction of the TAIPX index). Journal of Financial Engineering and Management of Securities, 26, 143-159 (In Persian).
[10] Ghosh, A. (2015). Banking-industry specific and regional economic determinants of non-performing loans: Evidence from US states. Journal of Financial Stability20, 93-104.
[11] Gilkeson, J. H., & Smith, S. D. (1992). The convexity trap: pitfalls in financing mortgage portfolios and related securities. Economic Review-Federal Reserve Bank of Atlanta, 77(6), 14.
[12] Hakimipur, N. (2018). Evaluation of the Effective Banking Factors on Nonperforming loans of Iranian Banking (GMM -Dynamic Panel Model Approach), Journal of Financial Economics, 12(42), 99-119 (In Persian).
[13] Heidari, H., Zvarian, Z., & Nourbakhsh, I. (2011). A survey on the effect of macroeconomic indicators on Nonperforming loans, Journal of Economics Research, 11(1), 43-65 (In Persian).
[14] Hsing, Y. (2012). Impacts of the trilemma policies on inflation, growth and volatility in Greece. International Journal of Economics and Financial Issues2(3), 373-378.
[15] Hsing, Y. (2016). Impacts of Government Debt, the Exchange Rate and Other Macroeconomic Variables on Aggregate Output in Croatia. Managing Global Transitions: International Research Journal14(3).
[16] Ismaili, B. (2018). The role of the occurrence of business cycles in Nonperforming loans  by using intermediate filters, Journal of Financial Economics, 12(44), 161-188 (In Persian).
[17] Jakubík, P., & Reininger, T. (2014). What are the key determinants of nonperforming loans in CESEE? (No. 26/2014). IES Working Paper.
[18] Jensen, H., Ravn, S. H., & Santoro, E. (2018). Changing credit limits, changing business cycles. European Economic Review102, 211-239.
[19] Khan, R. E. A., Akhtar, A. A., & Rana, A. S. (2002). Relationship between Exchange Rate and Budgetary Deficit: Empirical Evidence from Pakistan. Journal of Applied Sciences2(8), 839-842.
[20] Kordbacheh, H., & Noushabadi, L. (2011). The Explanation of Factors Affecting Nonperforming loans in the Banking Industry of Iran, Journal of Economic Research of Iran, 16(49) (In Persian).
[21] Kuzucu, N., & Kuzucu, S. (2019). What Drives Non-Performing Loans? Evidence from Emerging and Advanced Economies during Pre-and Post-Global Financial Crisis. Emerging Markets Finance and Trade55(8), 1694-1708.
[22] Makri, V., Tsagkanos, A., & Bellas, A. (2014). Determinants of non-performing loans: The case of Eurozone. Panoeconomicus61(2), 193-206.
[23] Merz, N. (2017). The impact of foreign currency debt on credit risk (Doctoral dissertation).
[24] Mohammadi, Timur, Shakeri, Abbas, Eskandari, Farzad and Karimi, Davood (2017). Investigating the Impact of Exchange Rate volatilities on Non-performing loans in Iran's Banking System, Journal of Planning and Budget, 21(2), 24-24(In Persian).
[25] Pandit, R. (2005). The Impact of Fiscal Deficit on Long-term Nominal Interest Rate in Nepal. Economic Review, Occasional Paper17.
[26] Pazoki, N., Hamidian, A., Mohammadi, S., & Mahmoudi, V. (2013). Use wavelet transform to Survey the correlation between different exchange rates, Oil price, Gold Price and Tehran Stock Exchange Index at different time scales. Journal of Investment knowledge, 2(7), 131-148 (In Persian).
[27] Puckelwald, J. (2012). The influence of the macroeconomic trilemma on monetary policy-A functional coefficient approach for the Taylor rule. In 16th Conference of the Research Network Macroeconomics and Macroeconomic Policies (FMM).
[28] Rehman,O. (2017). Determinants of Non-Performing Loan in South Asia: The Role of Financial Crisis. Eurasian Journal of Business and Economics, 10(20),105-124.
[29] Roueff, F., & Von Sachs, R. (2011). Locally stationary long memory estimation. Stochastic Processes and their Applications121(4), 813-844.
[30] Sayedi, S. N. (2014). Credit risk, market power and exchange rate as determinants of banks performance in Nigeria. Journal of Business and Management, 16(1), 35-46.
[31] Saysombath, P., & Kyophilavong, P. (2013). Budget deficit and Real exchange rate: Further Evidence from cointegration and causality test for in the Lao PDR. Handbook on the Economic, Finance and Management Outlooks1, 1-5.
[32] Škarica, B. (2014). Determinants of non-performing loans in Central and Eastern European countries. Financial theory and practice38(1), 37-59.
[33] Tahmasebi, B., Jafarisamimi, A., & Amiri, H. (2012). The Effect of Budget deficit on real exchange rate in Iran. Journal of Audit knowledge, 12(49) (In Persian).
[34] Valipour pashah, M., & Arbab Afzali, M. (2016). The effects of the foreign exchange rate volatility on the returns of the Iranian banking network, the policy paper of the Central Bank of the Islamic Republic of Iran (In Persian).
[35] Wen, Y. (2005). Understanding the inventory cycle. Journal of Monetary Economics52(8), 1533-1555.