ORIGINAL_ARTICLE
Achieving Regional Convergence through the Role of Foreign Direct Investment and Portfolio Investment: Evidence from ASEAN+3
T
he purpose of this study is to analyze the effect of foreign direct investment and portfolio investment on the convergence occurrence of economic growth of countries in the ASEAN + 3 region and to determine the time or speed required to achieve convergence. The type of data used in this research is secondary data panel which is combination between time series data and cross-section data with annual time period 2001-2015 and number of ASEAN member country that is Brunei Darussalam, Cambodia, Indonesia, Lao PDR, Malaysia, Myanmar, Philippines, Singapore, Thailand, Viet Nam and plus three countries namely China, Korea and Japan. So the total observation is 195. The analytical method used is the Arellano Bond dynamic panel. The results of the study show that the first lag variable of economic growth, Foreign Direct Investment (FDI) and portfolio investment have a significant influence on economic growth in ASEAN + 3, but not on ASEAN without China, Japan, and Republic Korea. Foreign Direct Investment (FDI) has a greater influence on changes in economic growth than portfolio investment. Half-life conditional convergence shows a value of 10.63 years which means the time required achieving steady-state conditions of the convergence process or the time required to achieve half of the convergence at convergence rates reaches 0.065% / year. Meanwhile, the influence of portfolio investment provides a greater speed of adjustment to the convergence process than foreign direct investment, but in a half-life of convergence, foreign direct investment is faster than the influence of portfolio investment.
https://ier.ut.ac.ir/article_74472_e9a5848446e2a0068f36600757733bbd.pdf
2020-01-01
1
18
10.22059/ier.2020.74472
Keywords: Foreign Direct Investment
Portfolio Investment
economic growth
convergence
Dynamic Panel. JEL Classification: F23
F21
O40
O47
C51
I Made
Suidarma
suidarma@undiknas.ac.id
1
Department of Management, University of Pendidikan Nasional Denpasar, Bali, Indonesia
LEAD_AUTHOR
Wayan
Sri Maitri
shrimaitri@undiknas.ac.id
2
Department of Management, University of Pendidikan Nasional Denpasar, Bali, Indonesia
AUTHOR
I Made
Darta
madedarta@undiknas.ac.id
3
Department of Management, University of Pendidikan Nasional Denpasar, Bali, Indonesia
AUTHOR
I Gusti Nengah Darma
Diatmika
igustinengahdd@gmail.com
4
Department of Economics, University of Tabanan, Bali, Indonesia
AUTHOR
Alvarado, R., Iniguez, M., & Ponce, P. (2017). Foreign Direct Investment and Economic Growth in Latin America. Economic Analysis andPolicy, 56, 176-187.
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29
ORIGINAL_ARTICLE
The Impact of Neighborhood on Iran’s Intra-Industry Trade (A Spatial Panel Econometric Approach)
he main purpose of this research is to answer the question that "How neighborhood of Iran's trading partners will have an effect on Iran intra-industry trade? For this purpose, the impact of spatial neighborhood effects of 23 major trade partners on Iran’s intra-industry trade for the period of 1995-2014, has been investigated through Spatial Panel Econometric and Maximum Likelihood Estimator (ML). By using the Spatial Autoregressive (SAR) Model and Geographic Distance Matrix, neighborhood effects have been investigated. The results gained from spatial neighborhood effects estimates showed that the neighborhood of Iran trade partner countries had a negative effect on Iran’s intra-industry trade. It was also found that differences in market size, exchange rate volatility, trade orientation, and geographical concentration had a negative effect and economic growth, Linder Effect, exchange rate, and volume per capita trade had a positive effect on intra-industry trade. Accordingly, it is recommended that Iran has more trade with Belgium, India, Korea, Netherlands, Iraq, Saudi Arabia, and Singapore.
https://ier.ut.ac.ir/article_74473_293b4e8359692469a9af40de6636a089.pdf
2020-01-01
19
39
10.22059/ier.2020.74473
Keywords: Intra-industry Trade
Geographic Neighborhood
Spatial Panel Econometric
Iran. JEL Classification: F12
F14
C21
C33
Amin
Mansouri
sa.mansouri@scu.ac.ir
1
Department of Economics, Shahid Chamran University of Ahvaz, Ahvaz, Iran
LEAD_AUTHOR
Abdolmajid
Ahangari
a.ahanari@scu.ac.ir
2
Department of Economics, Shahid Chamran University of Ahvaz, Ahvaz, Iran
AUTHOR
Andresen, M. A. (2003). Empirical Intra-industry Trade: What We Know and What We Need to Know. University of British Columbia, Retrieved from
1
https://www.researchgate.net/profile/Martin_Andresen2/publication/228841795_Empirical_intra-industry_trade_What_we_know_and_what_we_need_to_know/links/54f3ea430cf24eb8794cb5f2.pdf.
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Anselin, L. (2013). Spatial Econometrics: Methods and Models. Netherlands: Springer.
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43
ORIGINAL_ARTICLE
Social Collateral and Repayment Performance: Evidence from Islamic Micro Finance
I
n this study we designed to test the remarkable repayment performance of Akhuwat in Pakistan; the most successful Islamic Microfinance Institution (IMFI), which offers interest-free loans in order to improve the quality of life and alleviate poverty. The model of Akhuwat is based on Muakhaat (brotherhood) and Qard-e-Hasan (offering financial assistance to somebody in need without interest). The primary objective of this study was to investigate the determinants of microfinance repayment performance. The study examined the borrowers’ characteristics, loan attributes, lender/institutional characteristics and the social collateral characteristics related to the Akhuwat and the data of 387 borrowers is obtained from microfinance programs carried out on a continuous basis by Akhuwat. The findings depicted that among the socio-demographic factors like gender, marital status, number of dependents and numbers of previous loans are significantly and positively associated with loan repayment performance. However, previous loan default and religion are significantly and inversely associated with the loan repayment performance. The findings of the study supported the role of social ties in improving repayment performance and hold key insights and directions about microfinance policymaking in Pakistan.
https://ier.ut.ac.ir/article_74474_e7d04655aaa706afd5079b7a17319384.pdf
2020-01-01
41
74
10.22059/ier.2020.74474
Keywords: Islamic Microfinance, Repayments
Pakistan, Social Collateral. JEL Classification: G20, G21, G29
Abdul
Rafay
abdul.rafay@umt.edu.pk
1
Department of Finance, School of Business & Economics, University of Management & Technology, Lahore, Pakistan
LEAD_AUTHOR
Saqib
Farid
saqib.farid@umt.edu.pk
2
Department of Finance, School of Business & Economics, University of Management & Technology, Lahore, Pakistan
AUTHOR
Farah
Yasser
farah.yasser@umt.edu.pk
3
School of Commerce & Accountancy, University of Management & Technology, Lahore, Pakistan
AUTHOR
Shahid
Safdar
shahis.safdar@akhuwat.org.pk
4
HR Manager, Akhuwat, Pakistan
AUTHOR
Abdullah, S., & Quayes, S. (2016). Do Women Borrowers Augment Financial Performance of MFIs? Applied Economics, 48(57), 5593-5604.
1
AbdulSamad, M. (2014). Islamic Micro Finance: Tool for Economic Stability and Social Change. Humanomics, 30(3), 199-226.
2
Ahlin, C., & Townsend, R. M. (2007). Using Repayment Data to Test across Models of Joint Liability Lending. The Economic Journal, 117(517), F11-F51.
3
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4
Ahmed, H. (2002). Financing Microenterprises: An Analytical Study of Islamic Microfinance Institutions. Islamic Economic Studies, 9(2), 27-64.
5
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71
Wydick, B. (2001). Group Lending under Dynamic Incentives as a Borrower Discipline Device. Review of Development Economics, 5(3), 406-420.
72
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73
ORIGINAL_ARTICLE
The Optimal Allocation of Iran's Natural Gas
T
he optimal allocation of natural gas resources to various uses such as final and intermediate consumption, injection into oil fields, and exports can help policymakers to use this kind of resources efficiently. Empirical evidence support using hyperbolic discount rates instead of fixed discount rates in the economic literature. The purpose of this study is to maximize the social welfare function and analyze the optimal paths of different uses of natural gas over the next three decades based on a nonlinear dynamic programming model using a hyperbolic discount rate. The results show that in the current situation, gas exports do not maximize social welfare, but by expanding Iran's natural gas production, exports will lead to maximizing social welfare.
https://ier.ut.ac.ir/article_74475_6be9a1f79b304165e4314aacf5677456.pdf
2020-01-01
75
98
10.22059/ier.2020.74475
Keywords: Natural Gas
Optimal Allocation
Hyperbolic Discounting
Iran. JEL Classifications: Q34
Q48
C61
Seyed Ehsan
Alavi
alavi.ehsan@mail.um.ac.ir
1
Department of Economics, Ferdowsi University of Mashhad, Mashhad, Iran
AUTHOR
Mohammad
Lotfalipour
lotfalipour@um.ac.ir
2
Department of Economics, Ferdowsi University of Mashhad, Mashhad, Iran
LEAD_AUTHOR
Mohammad Ali
Falahi
falahi@um.ac.ir
3
Department of Economics, Ferdowsi University of Mashhad, Mashhad, Iran
AUTHOR
Sohrab
Effati
s-effati@um.ac.ir
4
Department of Applied Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran
AUTHOR
Azfar, O. (1999). Rationalizing Hyperbolic Discounting. Journal of Economic Behavior & Organization, 38, 245-252.
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35
ORIGINAL_ARTICLE
Reaction of Stock Market Index to Oil Price Shocks
T his study examines how oil price shocks interact with the stock market index within a nonlinear autoregressive distributed lag model in Iran. Based on quarterly data for the period from 1991 to 2017, the findings revealed statistically significant evidence of short-run and long-run asymmetric behavior of stock market index in response to the positive and negative shocks occurring in oil price, industrial production and lending rate. In particular, Unanticipated short-run and long-run positive (negative) oil price shocks trigger an addition (reduction) in the stock market index. Moreover, both short-run and long-run results present that the stock market index is more affected by positive changes in oil prices than the negative ones. Furthermore, the cumulative dynamic multipliers point out a significant asymmetric reaction of the stock market index to oil price shocks and other macroeconomic determinants. The aforementioned multipliers also show that the speed of response and time required to reach a new equilibrium state are sensitive to the direction of changes in the macroeconomic fundamentals. Consequently, the results prescribe that financial participants, energy policymakers and the government should adjust their respective strategies to changes in oil prices and consider the asymmetry when forecasting and managing the negative impacts of unexpected events.
https://ier.ut.ac.ir/article_74476_701543f828434c8a777839915523c11a.pdf
2020-01-01
99
128
10.22059/ier.2020.74476
Keywords: Oil Price Shocks
Stock Market Index
Nonlinear Autoregressive Distributed Lag Model
Dynamic Multiplier. JEL Classification: E32
G17
Q43
Masoud
Shirazi
ma.shirazi@atu.ac.ir
1
Faculty of Economics, Allameh Tabataba’i University, Tehran, Iran
LEAD_AUTHOR
Ali
Emami Meibodi
emami@atu.ac.ir
2
Faculty of Economics, Allameh Tabataba’i University, Tehran, Iran
AUTHOR
Antoniou, A., Holmes, P., & Priestley, R. (1998). The Effects of Stock Index Futures Trading on Stock Index Volatility: An Analysis of the Asymmetric Response of Volatility to News. Journal of Futures Markets, 18(2), 151-166.
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62
ORIGINAL_ARTICLE
Dutch Disease, Rentier State, and Resource Curse: A Characteristic Triangle and Ultra Challenge in the Iranian Economy
O wning Natural resources has been beneficial for some Countries, but hurtful for some others. Optimum management of natural resources in the former group has enhanced economic growth, bad governance, however, has led to Dutch Disease in the latter group. Bad governance in Iran has created Dutch Disease, Rentier State, and Resource Curse, DRR as a characteristic triangle. As the Iranian annual budget is financed by oil revenue, it is involved in a rentier state case. By applying analytical and statistical tools, this article is highlighting the different dimensions of a characteristic triangle in the Iranian economy. Positing DRR, as the number one challenge in the Iranian economy, is another mission of this article. A first specific feature of DRR is that its 3angles connect closely to each other. Secondly, DRR has led to weaken the Iranian taxing system. Thirdly, DRR has been in charge of the current less developed private sector. Some policy implications of this article include standardizing the Iranian taxing system, improving the Iranian "National Development Fund" and supporting the private sector to become a well-developed one. Disregarding DRR in Iran would deepen economic hardships. Curing DRR is the most urgent in the Iranian economy. Settling the DRR in Iran is a prerequisite of sorting out other challenges.
https://ier.ut.ac.ir/article_74477_4cec6036f076717c3db8d6d077f14888.pdf
2020-01-01
129
157
10.22059/ier.2020.74477
Keywords: Dutch Diseases
Rentier State
Resource Curse
Iranian economy. JEL Classification: Q38
Q30
F20
Yadollah
Dadgar
y_dadgar@sbu.ac.ir
1
Department of Economics and Political Sciences, Shahid Beheshti University, Tehran, Iran
LEAD_AUTHOR
Zeinab
Orooji
zeinab.orooji@yahoo.com
2
Department of Economics and Political Sciences, Shahid Beheshti University, Tehran, Iran
AUTHOR
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84
ORIGINAL_ARTICLE
Tourism Impact on Air Pollution in Developed and Developing Countries
T
he main purpose of this paper is to investigate and compare the impact of tourism on air pollution in selected developed and developing countries during the period of 1995-2014. To this end, at first, the model was designed based on the Environmental Kuznets Curve (EKC) assumptions and with the presence of the major influencing factors on air pollution along with the international tourism variable. Then, the long-run relationship between these variables was estimated by the Continuously-updated and Fully-Modified (CUP-FM) method, considering the existence of a cross-sectional dependence between the model variables in both groups of studied countries. The results of this study indicate the positive impact of international tourism on air pollution in selected developing countries; while the expansion of international tourism reduces air pollution in studied developed countries.
https://ier.ut.ac.ir/article_74478_a27f23dd69ed21d72937430d37fb5a13.pdf
2020-01-01
159
180
10.22059/ier.2020.74478
Keywords: Air Pollution
Tourism Development
Cross-Sectional Dependent
Continuously-updated and Fully-Modified (CUP-FM). JEL Classification: C23
Q56
J51
F12
Mohammad
Alizadeh
alizadeh.m@lu.ac.ir
1
Faculty of Economics and Administrative Sciences, Lorestan University, Khoram Abad, Iran
LEAD_AUTHOR
Aghaei, M., Ghanbari, A., Agheli, L., & Sadeghi, H. (2012). Investigating the Relationship between Energy Consumption and Economic Growth in Iran's Provinces Using Cointegration and Multivariate Panel Error Correction Model. Journal of Economics and Modeling, 9, 148-185.
1
Asghari, M., Yousefi Tudashaki, Sh., & Arbabian, Sh. (2012). The Impact of Tourism on Environmental Quality. Journal of Economic Development Research, 7, 1-28.
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6
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7
Cole, M. A., Rayner, A. J., & Bates, J. M. (1997). The Environmental Kuznets Curve: An Empirical Analysis. Environmental and Development Economics, 2, 401-416.
8
Dean, M. (2002). Does Trade Liberalization Harm the Environment? A New Test. the Canadian Journal of Economics, 35(4), 819-842.
9
Dogan, E., Seker, F., & Bulbul, S. (2017). Investigating the Impacts of Energy Consumption, Real GDP, Tourism and Trade on CO2 Emissions by Accounting for Cross-Sectional Dependence: A Panel Study of OECD Countries. Current Issues in Tourism, 20(16), 1701-1719.
10
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11
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35
ORIGINAL_ARTICLE
Investigating the Effects of Contractual Factors and Arrangements on the Optimum Level of Production in Oil and Gas Projects: Evidence from the South Pars Phases 17 & 18
D evelopment of oil and gas fields is facing many risks, which are mainly due to uncertainties about the existence of commercial reserves, natural and economic environment, political conditions of host countries, legal and infrastructure issues and a market for petroleum products. In such an environment, investors are often engaged as contractors to develop and operate petroleum projects, constantly seeking to recover their capital and operating expenditures quickly, through increasing production levels. But some geological and geophysical factors, hydrocarbon structure, technological and scientific constraints, as well as some of the contractual arrangements, such as cost recovery ceiling, virtually prevent the investor from gaining access to high economic benefits and quick recovery. The purpose of this study is to investigate the effects of contractual factors and arrangements on the optimal production levels of petroleum projects. To this end, information on the South Pars phases 17 and 18 projects were collected as a case and the optimal level of rich-gas production in this project was simulated in form of a nonlinear dynamic optimization model under the Iranian Petroleum Contract and Engineering, Procurement, Construction arrangements. The production levels and scenario analysis indicated that contractual factors and arrangements could affect the optimum level of petroleum production, significantly. Given that the production paths obtained for this project are different from that drawn by the Management and Consolidated Planning department in National Iranian Oil Company, the current production profile for the Phases 17 and 18 is not optimal, in which the executive suggestions are presented. These findings would be applicable to the formulation of Master Development Plans.
https://ier.ut.ac.ir/article_74479_d6026adb745e5847cd1ee9948c280281.pdf
2020-01-01
181
223
10.22059/ier.2020.74479
Keywords: Optimal Production
Petroleum Projects
Petroleum Contracts
IPC
EPC
South Pars Gas Field. JEL Classification: C61
Q35
K32
Ali
Emami Meibodi
emami@atu.ac.ir
1
Faculty of Economics, Allameh Tabataba’i University, Tehran, Iran
LEAD_AUTHOR
Atefeh
Taklif
a.taklif@atu.ac.ir
2
Faculty of Economics, Allameh Tabataba’i University, Tehran, Iran
AUTHOR
Hamidreza
Arbab
arbab@atu.ac.ir
3
Faculty of Economics, Allameh Tabataba’i University, Tehran, Iran
AUTHOR
Hassan
Bovairi Monji
4
Faculty of Economics, Allameh Tabataba’i University, Tehran, Iran
AUTHOR
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47
ORIGINAL_ARTICLE
Investigating Causal Relationship between Financial Development Indicators and Economic Growth: Toda and Yamamoto Approach
T he Causal relationship between financial development and economic growth has received divergent views in the literature under the traditional Granger approach to causality using data from various countries. The more recent Toda and Yamamoto and Dolado and Lütkepohl (TYDL) approach to causality were used to investigate the causal relationship between financial development and economic growth in Nigeria for the period 1985 to 2015. TYDL is based on an augmented VAR modeling and it is adjudged more robust to order of integration of the variables when compared with Granger framework. The maximum order of integration was two while the optimal lag length of three was selected by FPE, AIC and HQ criteria. Bi-directional causality was found between financial markets indicators and economic growth while unilateral causality running from stock market indicators to GDP was established. The findings support existing studies that agree with the fact that a well-structured financial sector breeds economic growth and this by implication suggests, it is imperative for the government of Nigeria and other developing countries to create an atmosphere for a thriving financial sector and engage in reforms that will stimulate the economy.
https://ier.ut.ac.ir/article_74480_26cc1fbb389ed085d368c54ba00747b4.pdf
2020-01-01
225
246
10.22059/ier.2020.74480
Keywords: Causality
Financial Development
Granger
Economy
Toda
Yamamoto. JEL Classification: C01
C18
D3
E44
Oluyemi
Okunlola
ookunlola@unimed.edu.ng
1
University of Medical Sciences, Ondo City, Nigeria
LEAD_AUTHOR
Emilomo
Masade
emilomo_masade@yahoo.com
2
Maslomo Auto and Company Limited, Ondo State, Nigeria
AUTHOR
Adewale
Folaranmi Lukman
adewale.folaranmi@lmu.edu.ng
3
Landmark University, Omu Aran, Nigeria
AUTHOR
Samuel
Ajayi Abiodun
ajayi.abiodun@lmu.edu.ng
4
Landmark University, Omu Aran, Nigeria
AUTHOR
Arestis, P., Demetriades, P. O., & Luintel, K. B. (2001). Financial Development and Economic Growth: The Role of the Stock Market. Journal of Money, Credit and Banking, 33(1), 16-41.
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Toda, H. Y., & Yamamoto, T. (1995). Statistical Inference in Vector Autoregressions with Possibly Integrated Process. Journal of Econometrics, 66(1-2), 225-250.
27
Umar, B. N. (2010). Stock Markets, Banks and Economic Growth: Time Series Evidence from South Africa. The African Finance Journal, 12(2), 72-92.
28
Xu, Z. (2000). Financial Development, Investment, and Economic Growth. Economic Inquiry, 38(2), 331-344.
29
Zang, H., & Chul Kim, Y. (2007). Does Financial Development Precede Growth? Robinson and Lucas Might be Right. Applied Economic Letters, 14, 15-19.
30
ORIGINAL_ARTICLE
Identification of Cyclical Banks in Iranian Banking System (Focus on Leverage Ratio)
T
he cyclical banks have different behavior than other banks. The structure of the balance sheet in cyclical banks is different from anti-cyclical banks. The cyclical banks have a relationship between leverage growth and asset growth while the other banks have no relationship between asset growth and leverage growth in the banking system. This relationship depends on the structure of the balance sheet and explains how banks behave in the business environment. Banks decide based on economic conditions and their leverage introduces the structure of balance sheet and banking models. This research surveys the behavior of cyclical banks in the Iranian banking system during the period of 2005-2015. According to the results, the Positive relationship between asset growth and leverage growth approves cyclical behavior of leverage. The structure of the balance sheet and a variety of banks is more important to leverage behavior. This paper uses the type of banks and size as the main variable in the Iranian banking system.
The bank should be adjusting its balance sheets based on economic conditions and business. Banks that influence cyclical leverage behavior have a higher share of credits in balance sheets. A higher share of credits explains tending banks for short-term credits.
https://ier.ut.ac.ir/article_74481_c4cd39d2145b5f240d096084d8b661c2.pdf
2020-01-01
247
266
10.22059/ier.2020.74481
Keywords: Banking System
Cyclicality
Banking Regulation
Leverage. JEL Classification: G21
G32
G28
C23
Mahshid
Shahchera
m.shahchera@mbri.ac.ir
1
Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran
LEAD_AUTHOR
Acharya, V. & Ryan, S. (2016). Banks' Financial Reporting and Financial System Stability. Journal of Accounting Research, 54(2), 277-340.
1
Adrian, T. (2014). Financial Stability Policies for Shadow Banking. Federal Reserve Bank of New York Staff Reports, Retrieved from
2
https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2395367_code387943.pdf?abstractid=2395367&mirid=1.
3
Adrian, T., & Ashcraft, A. B. (2012). Shadow Bank Regulation. Annual Review of Financial Economics, 4(1), 99-140.
4
Adrian, T., Ashcraft, A. B. & Cetorell, N. (2013). Shadow Bank Monitoring. Federal Reserve Bank of New York Staff Reports, Retrieved from
5
https://www.econstor.eu/bitstream/10419/93634/1/771630980.pdf.
6
Adrian, T., Covitz, D., & Liang, J. N. (2013). Financial Stability Monitoring. Federal Reserve Bank of New York Staff Report, Retrieved from
7
https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr601.pdf.
8
Adrian, T., & Boyarchenko, N. (2012). Intermediary Leverage Cycles and Financial Stability. Federal Reserve Bank of New York Staff Report, Retrieved from
9
https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr567.pdf.
10
Adrian, T., Crump, R. K., & Moench, E. (2012). Pricing the Term Structure with Linear Regressions. Federal Reserve Bank of New York Staff Report, Retrieved from
11
https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr340.pdf.
12
Adrian, T., & Moench, E., & Song Shin, H. (2010). Financial Intermediation, Asset Prices, Macroeconomic Dynamics. Federal Reserve Bank of New York Staff Report, Retrieved from
13
https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1532319_code387943.pdf?abstractid=1532319&mirid=1.
14
Adrian, T., & Song Shin, H. (2014). Pro-cyclical Leverage and Value-at-Risk. Review of Financial Studies, 27(2), 373-403.
15
---------- (2011a). Financial Intermediaries and Monetary Economics. Handbook of Monetary Economics, Retrieved from
16
https://www.econstor.eu/bitstream/10419/60885/1/622780220.pdf.
17
---------- (2011b). Financial Intermediary Balance Sheet Management. Annual Review of Financial Economics, Retrieved from
18
https://www.econstor.eu/bitstream/10419/60910/1/683159941.pdf.
19
---------- (2010). Liquidity and Leverage. Journal of Financial Intermediation,19(3), 418-437.
20
---------- (2009a). Money, Liquidity and Monetary Policy. American Economic Review Papers & Proceedings, 99(2), 600-609.
21
---------- (2009b). Prices and Quantities in the Monetary Policy Transmission Mechanism. International Journal of Central Banking, 5(4), 131-142.
22
---------- (2008). Financial Intermediaries, Financial Stability, and Monetary Policy. Federal Reserve Bank of Kansas City Jackson Hole Economic Symposium Proceedings, Retrieved from
23
https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1266714_code387943.pdf?abstractid=1266714&mirid=1.
24
---------- (2006). Money, Liquidity, and Financial Cycles. Retrieved from
25
https://www.researchgate.net/profile/Tobias_Adrian/publication/229053567_Money_liquidity_and_financial_cycles/links/0deec52cafc5555da6000000.pdf.
26
Adrian, T., Etula, E., & Muir, T. (2013). Financial Intermediaries and the Cross Section of Asset Returns. Journal of Finance,Retrieved from https://onlinelibrary.wiley.com/doi/epdf/10.1111/jofi.12189.
27
Arnold, B., Borio, C., Ellis, L., & Moshirian, F. (2012). Systemic Risk, Macroprudential Policy Frameworks, Monitoring Financial Systems and the Evolution of Capital Adequacy. Journal of Banking and Finance, 36, 3125-3132. Aymanns, C., & Farmer, J. D. (2015). The Dynamics of the Leverage Cycle. Journal of Economic Dynamics and Control, 50, 155-179.
28
Bank for International Settlements. (2009). The Role of Valuation and Leverage in Pro-cyclicality. Committee on the Global Financial System, Retrieved from https://www.bis.org/publ/cgfs34.pdf.
29
Beccalli, E., Boitani, A., & Di Giuliantonio, S. (2015). Leverage Pro-Cyclicality and Securitization in US Banking. Journal of Financial Intermediation, 24(2), 200-230.
30
Beltratti, A., & Paladino, G. (2015). Bank Leverage and Profitability: Evidence from a Sample of International Banks. Review of Financial Economics, 27, 46-57.
31
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32
Brunnermeier , M. K., & Pedersen, L. H. (2009). Market Liquidity and Funding Liquidity. Review of Financial Studies, 22(6), 2201-2223.
33
Damar, E., Meh, C., & Terajima, Y. (2013). Leverage, Balance-sheet and Wholesale Funding. Journal of Financial Intermediation, 22, 639-662.
34
Fostel, A., & Geanakoplos, J. (2008). Leverage Cycles and the Anxious Economy. American Economic Review, 98(4), 1211-1244.
35
Geanakoplos, J. (2003). Liquidity, Default, and Crashes: Endogenous Contracts in General Equilibrium. Cambridge: Cambridge University Press.
36
Giordana, G., & Schumacher, I. (2013). What are the Bank-specific and Macroeconomic Drivers of Banks’ Leverage? Evidence from Luxembourg. Empircal Economics, 45(2), 905-928.
37
Hanson, S. G., Kashyap, A. K., & Stein, J. C. (2011). A Macroprudential Approach to Financial Regulation. Journal of Economic Perspectives, 25(1), 3-28.
38
Kashyap, A. K., & Stein, J. C. (2000). What Do a Million Observations on Banks Say about the Transmission of Monetary Policy? American Economic Review, 93(3), 407-428.
39
Kiyotaki, N., & Moore, J. (1997). Credit Chains. Journal of Political Economy, 105(21), 211- 248.
40
Laux, C., & Rauter, T. (2016). Procyclicality of US Bank Leverage. Retrieved from
41
https://ssrn.com/abstract=2409954 or http://dx.doi.org/10.2139/ssrn.2409954.
42
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43
Pagratis, S., Karakatsani E., & Louri, H. (2014). Bank Leverage and Return on Equity Targeting: Intrinsic Procyclicality of Short-term Choices. Bank of Greece, Working Paper, Retrieved from
44
https://www.bankofgreece.gr/Publications/Paper2014189.pdf.
45
Pedrono, J. (2017). Banking Leverage Procyclicality: A Theoretical Model Introducing Currency Diversification. CEPII Working Paper, Retrieved from http://www.cepii.fr/PDF_PUB/wp/2017/wp2017-06.pdf.
46
Schularik, M., & Taylor, A. M. (2012). Credit Booms Gone Bust: Monetary Policy, Leverage Cycles, and Financial Crisis (1870-2008). American Economic Review, 102(2), 1029-1061.
47
Valencia, O. C., & Bolanos, A. O. (2018). Bank Capital Buffers around the World: Cyclical Patterns and the Effect of Market Power. Journal of Financial Stability, Retrieved from https://mpra.ub.uni-muenchen.de/84617/1/MPRA_paper_84617.pdf.
48
ORIGINAL_ARTICLE
Agricultural Economic Dynamics in a Bayesian DSGE Model for Iran
I
ran’s economy is suffering from sharp and persistent economic shocks and agriculture plays an undeniable role in its economic growth and development. The aim of this paper is to study the relative contributions of various macroeconomic shocks to generating fluctuations in Iran’s agriculture sector. To do so, a Dynamic Stochastic General Equilibrium (DSGE) model, emphasizing on the agricultural sector, is developed. The model is estimated with Bayesian techniques using 9 macroeconomic variables. The findings indicate that agricultural productivity shock is the main driver of the economic fluctuations in the sector. Monetary shock and, to a lesser extent, government spending, preference and labor supply shocks, however, play an important role in agricultural dynamics. The two other shocks considered (oil revenue and money demand) are of less importance relatively. The historical decomposition shows after 2009, when imposed economic sanctions against Iran increase, the monetary shock becomes one of the main sources in explaining agricultural fluctuations. The results further confirm the symptoms of Dutch Disease (DD) in Iran’s agriculture.
https://ier.ut.ac.ir/article_74482_a2df61a0f8c36432f39f6a40fe42aa7e.pdf
2020-01-01
267
297
10.22059/ier.2020.74482
Keywords: Agricultural Sector
Macroeconomic Shocks
DSGE Model
Bayesian Techniques
Iran. JEL Classification: C69
N5
Mahdi
Khosravi
mahdikhosravi@uk.ac.ir
1
Department of Agricultural Economics, Shahid Bahonar University of Kerman, Kerman, Iran
LEAD_AUTHOR
Hossein
Mehrabi Boshrabadi
hmehrabi@uk.ac.ir
2
Department of Agricultural Economics, Shahid Bahonar University of Kerman, Kerman, Iran
AUTHOR
Allegret, J. P., & Benkhodja, M. T. (2015). External Shocks and Monetary Policy in an Oil Exporting Economy. Journal of Policy Modeling, 37, 652–667.
1
Apere, T. O., & Karimo, T. M. (2015). Monetary Policy Shocks and Agricultural Output Growth in Nigeria. IOSR Journal of Economics and Finance (IOSR-JEF), 6(1), 45-50.
2
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3
Bakhtiari, S., & Haghi, Z. (2001). The Survey of Effects of Oil Revenues Increase on Agriculture Sector: Dutch Disease in Iran. Agricultural Economy and Development, 35, 109-139.
4
Bautista, R. M. (1986). Effects of Increasing Agricultural Productivity in a Multispectral Model for the Philippines. Agricultural Economics, 1(1), 67-85.
5
Benkhodja, M. T. (2011). Monetary Policy and the Dutch Disease in a Small Open Oil Exporting Economy. Working Paper, Retrieved from
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7
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8
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9
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11
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12
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13
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14
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15
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16
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17
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19
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23
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25
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26
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27
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28
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29
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30
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33
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35
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38
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39
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40
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41
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45
ORIGINAL_ARTICLE
Modeling Gold Volatility: Realized GARCH Approach
F
orecasting the volatility of a financial asset has wide implications in finance. Conditional variance extracted from the GARCH framework could be a suitable proxy of financial asset volatility. Option pricing, portfolio optimization, and risk management are examples of implications of conditional variance forecasting. One of the most recent methods of volatility forecasting is Realized GARCH (RGARCH) that considers a simultaneous model for both realized volatility and conditional variance at the same time. In this article, we estimate conditional variance with GARCH, EGARCH, GIR-GARCH, and RGARCH with two realized volatility estimators using gold intraday data. We compared models, for in-sample fitting; by the log-likelihood value and used MSE and QLIKE lose functions to evaluate predicting accuracy. The results show that the RGARCH method for GOLD outperforms the other methods in both ways. So, using the RGARCH model in practical situations, like pricing and risk management would tend to better results.
https://ier.ut.ac.ir/article_74483_247318cd2f3075c7221280d2965b2e78.pdf
2020-01-01
299
311
10.22059/ier.2020.74483
Keywords: Realized GARCH
Gold
GARCH Models
Volatility. JEL Classification: G10
G15
G17
Esmaiel
Abounoori
esmaiel.abounoori@semnan.ac.ir
1
Department of Economics, Semnan University, Semnan, Iran
LEAD_AUTHOR
Mohammad
Zabol
m.zabol@semnan.ac.ir
2
Department of Economics, Semnan University, Semnan, Iran
AUTHOR
Andersen, T. G., Bollerslev, T., Diebold, F. X., & Labys, P. (2003). Modeling and Forecasting Realized Volatility. Econometrica, 7(2), 579-625.
1
Badescu, A., Elliott, R. J., & Ortega, J. P. (2015). Non-Gaussian GARCH Option Pricing Models and Their Diffusion Limits. European Journal of Operational Research, 247(3), 820-830.
2
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3
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4
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5
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6
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7
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8
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9
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10
Huang, Z., Wang, T., & Hansen, P. R. (2017). Option Pricing with the Realized GARCH Model: An Analytical Approximation Approach. Journal of Futures Markets, 37(4), 328-358.
11
---------- (2017). Option Pricing with the Realized GARCH Model: An Analytical Approximation Approach. Journal of Futures Markets, 37(4), 328-358.
12
Jiang, W., Ruan, Q., Li, J., & Li, Y. (2018). Modeling Returns Volatility: Realized GARCH Incorporating Realized Risk Measure. Physica A: Statistical Mechanics and its Applications, 500, 249-258.
13
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14
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15
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16
Sahamkhadam, M., Stephan, A., & Östermark, R. (2018). Portfolio Optimization Based on GARCH-EVT-Copula Forecasting Models. International Journal of Forecasting, 34(3), 497-506.
17
Sharma, P. (2016). Forecasting Stock Market Volatility Using Realized GARCH Model: International Evidence. The Quarterly Review of Economics and Finance, 59, 222-230.
18
Shephard, N., & Sheppard, K. (2010). Realizing the Future: Forecasting with High-Frequency-Based Volatility (Heavy) Models. Journal of Applied Econometrics, 25(2), 197-231.
19
Wei, Z. Y., Luo, Y. F., Yu, D. Y., & Wang, A. F. (2017). The Measure of Risk for SSE 50 Index Based on Realized NGARCH Model. Journal of Chongqing University of Technology (Natural Science), 5, Retrieved from
20
http://en.cnki.com.cn/Article_en/CJFDTotal-CGGL201705030.htm.
21