تأثیر نوسانات نرخ ارز و سهام بر کارایی تسهیلات اعطایی به بخش کشاورزی

نوع مقاله : مقالات پژوهشی

نویسندگان

1 دانشگاه فردوسی مشهد

2 دانشگاه فردوسی

3 دانشکده اقتصاد دانشگاه فردوسی مشهد

چکیده

مسئله نوسانات نرخ ارز و سهام از مسائل اساسی در کشورهای در حال توسعه است. نوسانات زیاد بازارهای دارایی مختلف، موجب نااطمینانی و تغییر در بهای تمام شده محصولات بخش­های مختلف می­شود. مطالعه حاضر، با بهره‌گیری از داده‌های فصلی 1384:1- 1396:4 به بررسی تأثیر نوسانات نرخ ارز و سهام در کنار چرخه­های تجاری و میزان واردات در بخش کشاورزی بر کارایی تسهیلات اعطایی به بخش کشاورزی با استفاده از الگوی چرخشی مارکوف پرداخته است. نتایج پژوهش نشان می­دهد نوسانات کوتاه­مدت ارز تأثیر معناداری بر کارایی تسهیلات ندارد اما نوسانات بلندمدت آن فارغ از رژیم کارایی، تأثیر منفی و معنادار دارد و این اثرگذاری در رژیم بالای کارایی بزرگتر می­باشد. همچنین نوسانات شاخص سهام تنها در بلندمدت و در شرایط رژیم بالای کارایی تأثیر مثبت و معنادار دارد و در رژیم و سطح پایین کارایی، افزایش شاخص سهام بدلیل سهم اندک آن در تأمین مالی، توانایی بهبود کارایی تسهیلات را ندارد. چرخه­های تجاری نیز فارغ از رژیم کارایی تاثیر منفی و معنادار دارد. میزان واردات محصولات کشاورزی نیز در رژیم بالای کارایی تأثیر منفی و معنادار دارد. در شرایطی که تسهیلات اعطایی بتوانند درآمد و اشتغال بالایی را برای بخش کشاورزی ایجاد نمایند، واردات می­تواند از طریق محدود ساختن تقاضای محصولات کشاورزی داخلی، زمینه را برای کاهش درآمد و متعاقبا کاهش اشتغال این بخش فراهم آورد. بنابراین، بایستی مدیریت ارز و بازار سهام با توجه به دوره زمانی نوسانات و همچنین سطح و رژیم کارایی تسهیلات بخش کشاورزی صورت پذیرد و مدیریت واردات محصولات کشاورزی در کشور نیز بایستی با توجه به رژیم کارایی تسهیلات ارائه شده صورت پذیرد تا شبکه بانکی نیز آسیب کمتری ببیند.

کلیدواژه‌ها


عنوان مقاله [English]

The Effect of Exchange Rate and Stock Index Fluctuations on the Efficiency of Agricultural Facilities

نویسندگان [English]

  • masoud homayounifar 2
  • M. Salimifar 3
3 Economic Faculty of Ferdowsi University of Mashhad
چکیده [English]

Introduction: The banking sector has a financial intermediary role and can directly and indirectly support the growth of the real sector of the economy. In countries such as Iran whose economy is bank-based, economic growth is largely dependent on bank loans and according to macroeconomic changes, banking flexibility has a great importance in economic development. On the other hand, according to the policies of the agricultural sector in the development perspective document, which insist on food security of the country by relying on production from domestic sources and emphasize on the self-sufficiency of production of basic products, the development of the agricultural sector has always been a matter of concern for the policymakers. One of the most important limitations in the agricultural sector is the limitation of financial resources for using modern technologies and the creation of higher added value products. Among the factors influencing the formation of an efficient financing system is the instability in other financial markets, especially the exchange rate, which affects the country's GDP and creates cyclical effects, finally affecting the performance of the banking sector. In fact, the impact of GDP on banking ability to provide facilities during periods of recession and booms is explained through business cycles. The purpose of this study is to investigate the effect of fluctuations in some asset markets such as exchange rate and stock index along with the variables of business cycles and agricultural products import on the efficiency of agricultural facilities.
Materials and Methods: In the present study, several econometric models have been used to investigate the effect of exchange rate and stock index fluctuations and business cycles on the efficiency of agricultural facilities. Initially, wavelet transform model was used to extract exchange rate and stock index fluctuations. The Daubechies discrete wavelet was used for this purpose. The advantage of this approach over the family of Arch models is the ability to distinguish fluctuations across time periods. In addition, Hodrick Prescott filter was used to extract business cycles, and Bootstrap data envelopment analysis approach was applied to evaluate the efficiency of agricultural facilities. The advantage of this approach over the data envelopment analysis approach is its bias correction and greater stability. The Markov switching model was also used to estimate the final research pattern. The used period in the study is 1384:1-1396:4.
Results and Discussion: According to Hamilton's study, the intercept that has the lowest coefficient indicates a low regime (low efficiency of facilities granted to the agricultural sector) and the intercept with a highest coefficient indicates the high regime (high efficiency of facilities granted to agriculture). Therefore, the zero regime in the present study indicates the high efficiency regime while regime indicates the low efficiency regime of the granted facilities. Based on the results of model estimation, the occurrence of business cycles in all regimes would lead to decline in the efficiency of the banking network facilities provided to the agricultural sector. The impact of exchange rate fluctuations depends on the time period. Short-term fluctuations have no significant effect on facility efficiency but medium- and long-term fluctuations have a negative significant impact. If the currency market volatility persists, it would reduce the efficiency, regardless of the regime and the level of efficiency of the facility. Of course long-term exchange rate fluctuations will have a stronger negative impact when the regime of agricultural facilities efficiency is high. Stock index fluctuations in the medium and long term also have a positive and significant impact when the efficiency of agricultural facilities is high. In case of high efficiency level of agricultural facilities, increasing imports of agricultural products will lead to decrease in efficiency.
Conclusion: In many studies on the effect of exchange rate, stock fluctuations, and business cycles on the performance of the banking network, several important factors have been ignored such as regime changes, and time-scale in the efficiency of facilities granted to the agricultural sector. Due to different impacts of exchange rate and stock index fluctuations across different time periods as well as on the different efficiency regimes of agricultural sector facilities, the policymakers of currency and capital market should manage currency and stocks based on the volatility period and the level of efficiency of facility. In fact, in such situation, the nonperforming loans will be less and the banking network will not have problem to finance the agricultural sector.

کلیدواژه‌ها [English]

  • Exchange Rate
  • stock index
  • efficiency of agricultural facilities
  • Markov Switching
1- Adrian T., and Shin H.S. 2010. Liquidity and leverage. Journal of Financial Intermediation 19(3): 418-437.
2- Azariadis C. 2018. Credit Cycles and Business Cycles.
3- Castro V. 2013. Macroeconomic determinants of the credit risk in the banking system: The case of the GIPSI. Economic Modelling 31: 672-683.
4- Claessens S., Kose M.A., and Terrones M.E. 2012. How do business and financial cycles interact?. Journal of International Economics 87(1): 178-190.
5- Emrouznejad A., and Cabanda E. 2014. Managing service productivity using data envelopment analysis. In Managing Service Productivity, Springer, Berlin, Heidelberg, 1-17.
6- Enders W. 2004. Applied time series econometrics. Hoboken: John Wiley and Sons.
7- Esmaili B. 2018. The Role of Business Cycles in the Nonperforming loans of National Bank of Iran by Using Intermediate Filters, Journal of Financial Economics 12(44): 161-188. (In Persian)
8- Gilkeson J.H., and 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.
9- Hakimi Pour N. 2018. Assessing the Factors Affecting Nonperforming loans of Iran Banks (GMM Dynamic Panel Model Approach), Journal of Financial Economics 12(42): 99-119. (In Persian)
10- Hamilton J.D. 2016. Regime switching models. The new palgrave dictionary of economics, 1-
11- Heidari H., Zavarian Z., and Nourbakhsh I. 2011. Investigating the Effect of Macroeconomic Factors on Nonperforming loans, Journal of Economic Research 11(1): 43-65. (In Persian)
12- Hollingsworth B., and Smith P. 2003. Use of ratios in data envelopment analysis. Applied Economics Letters 10(11): 733-735.
13- Iraqi A., Mousavi Baigi M., and Hasheminia S.M. 2015. Applying Discrete Wavelet Transform for Trend Analysis and Identification of Oscillating Temperature Patterns (Case Study: Mashhad Synoptic Station), Journal of Water and Soil 1: 239-249. (In Persian)
14- Iyengar A.N. 2009. Wavelet Based Volatility Clustering Estimation of Foreign Exchange Rates. ArXiv preprint arXiv: 0910.0087.
15- Kazerouni A., Asgharpour H., Mohammadpour S., and Bahari S. 2012. The Asymmetric Effects of Real Exchange Rate Fluctuations on Economic Growth in Iran: The Markov Switching Approach, Economic Journal - Two Months Review of Economic Issues and Policies 7: 5-26. (In Persian)
16- Khuchiani R. 2018. Investigation Interactions of Time-Scale between Stock Price Index and Exchange Rate Fluctuations in Tehran Stock Exchange, Journal of Financial Management Strategy 6(21): 159-182. (In Persian)
17- Kontbay Busun S., and Kasman A. 2015. A Note on Bank Capital Buffer, Portfolio Risk and Business Cycle. Ege Academic Review 15(1).
18- Kordbache H., and Nooshabadi L. 2011. Explaining the Factors Affecting Nonperforming loans in the Iran Banking Industry, Journal of Economic Research of Iran 16(49). (In Persian)
19- Marcucci J., and Quagliariello M. 2009. Asymmetric effects of the business cycle on bank credit risk. Journal of Banking and Finance 33(9): 1624-1635.
20- Merz N. 2017. The impact of foreign currency debt on credit risk (Doctoral dissertation).
21- Mohammadi T., Shakeri A., Eskandari F., and Karimi D. 2016. Investigating the Impact of Exchange Rate Fluctuations on Nonperforming loans in the Banking System of Iran, Journal of Planning and Budgeting 2: 3-24. (In Persian)
22- Novignon J., and Nonvignon J. 2017. Improving primary health care facility performance in Ghana: efficiency analysis and fiscal space implications. BMC Health Services Research 17(1): 399.
23- Raoufi A., and Mohammadi T. 2018. Predicting Returns of Tehran Stock Market Using Wavelet Decomposition and Adaptive Fuzzy Neural Network, Journal of Economic Research of Iran 23(76): 136-107. (In Persian)
24- 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.
25- Simar L., and Wilson P.W. 1998. Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science 44(1) 49-61.
26- Vali Pour Pashah M., and Arbab Afzali M. 2016. The effects of currency market instability on the performance of banking network of Iran, Central Bank of the Islamic Republic of Iran policy paper. (In Persian)
27- Vithessonthi C., and Tongurai J. 2016. Financial markets development, business cycles, and bank risk in South America. Research in International Business and Finance 36: 472-484.
28- Zara Nejad M., Khodapanah M., and Khadivi N. 2018. Investigating the Impact of Financial Development and Business Cycles on Banking Credit Risk. Journal of Applied Economic Studies of Iran 7(26): 71-87. (In Persian)
CAPTCHA Image