Journal of Economic Modeling Research

Modeling systemic risk and dependence structure between cryptocurrency and stock markets using a variational mode decomposition-based copula meth (VMD- Copula)

Ali Rezazadeh

The fundamental aim of this study is to investigate the structural dependence between the cryptocurrency and the stock market index. In this study, the total index of Tehran Stock Exchange has been used as a representative of the developing stock market and the index (S&P500) has been used as a representative of the developed stock market. using daily data during the period from 8 August 2015 to 21 February 2023. The results show that there is no structural dependence between the return Bitcoin and Iran stock market , either in the short term or in the long term. In other words, the changes domain in return of Bitcoin during the low and high ranges on the return of the mentioned index are insignificant. The results indicates that the cryptocurrency market is separated from the main class of financial and economic assets and hence offers various benefits to investors. Also, in the long term, for the return of Bitcoin cryptocurrency and the S&P500 stock index, Clayton's copula function was chosen in the first place as the appropriate model to explain the correlation. There is no correlation between the returns of Bitcoin and the s&p500 stock index in the short term. The findings of this study indicate the important role of cryptocurrencies in investors' portfolios as they act as a diversified option for investors and confirm that cryptocurrencies are a new investment asset class. Furthermore, it analyzes the upside and downside risk spillovers between stock markets and the cryptocurrency market by quantifying market risk measures, namely the conditional VaR (CoVaR) and the delta CoVaR (ΔCoVaR). The results indicate that Bitcoin, Ethereum and Ripple cannot be considered a strong hedge during the time of crisis. The speculative nature of cryptocurrencies and risks embedded in Bitcoin, Ethereum, and Ripple increases the risk flow to stock markets during a crisis, thus rendering the hedging costlier. increases the risk flow to stock markets during a crisis, thus rendering the hedging costlier.

Investigating the Effects of Foreign Trade on Multidimensional Poverty in Iran (SVAR Approach)

Mohsen Zayanderoody

Agbarakwe, U. H., & Okpe, E. A. (2024). An Analytical Inquiry into the Impact of International Trade on Poverty Reduction in Nigeria. South Asian Research Journal of Humanities and Social Sciences, 6(1), 40-55.
Asongu, S. A., & Eita, J. H. (2023). The conditional influence of poverty, inequality, and severity of poverty on economic growth in sub-Saharan Africa. Journal of Applied Social Science, 17(3), 372-384.
Atrkar Roshan, S., & Hashemi, Z. (2016). The Impact of Trade Openness on Poverty in Iran: Simultaneous Equations System Method. Quarterly Journal of Applied Theories of Economics, 3(1), 183-204. (in Persian)
Balasubramanian, P., Burchi, F., & Malerba, D. (2023). Does economic growth reduce multidimensional poverty? Evidence from low-and middle-income countries. World Development, 161, 106119.
Cao, Y., Tabasam, A. H., Ahtsham Ali, S., Ashiq, A., Ramos-Meza, C. S., Jain, V., & Shahzad Shabbir, M. (2023). The dynamic role of sustainable development goals to eradicate the multidimensional poverty: evidence from emerging economy. Economic research-Ekonomska istraživanja, 36(3).
Chatziantoniou, I, Duffy. D, Filis, G. (2013). Stock Market Response to Monetary and Fiscal Policy Shocks: Multi-country evidence. Economic Modelling, 30, 454–769.
D’Attoma, I., & Matteucci, M. (2024). Multidimensional poverty: an analysis of definitions, measurement tools, applications and their evolution over time through a systematic review of the literature up to 2019. Quality & Quantity, 58(4), 3171-3213.
Deaton, A. (2005). Measuring poverty in a growing world (or measuring growth in a poor world). The Review of Economics and Statistics, MIT Press, 87(1), 1–19.
Hosseinidoust, S. E., Sepehrdoost, H., & Moradi, F. (2024). Investigating the Affecting Factors on Capability Poverty in the Selected Islamic Countries Emphasizing on Globalization and Economic Growth. Economic Growth and Development Research, 14(56), -. (in Persian)
Ilyas, A., Banaras, A., Javaid, Z., & Rahman, S. U. (2023). Effect of Foreign Direct Investment and Trade Openness on the Poverty Alleviation in Burundi–Sub African Country: ARDL (Co-integration) Approach. Pakistan Journal of Humanities and Social Sciences, 11(1), 555-565.
Imam, M. F., Islam, M. A., & Hossain, M. J. (2018). Factors affecting poverty in rural Bangladesh: an analysis using multilevel modelling. Journal of the Bangladesh Agricultural University, 16(1), 123-130.
Iranmanesh, S. (2023). Analyzing the Role of Poverty in Financial Development in Selected Countries. Bi-quarterly Journal of development economics and planning, 10(1), 177-196. (in Persian)
Liu, M., Feng, X., Zhao, Y., & Qiu, H. (2023). Impact of poverty alleviation through relocation: From the perspectives of income and multidimensional poverty. Journal of Rural Studies, 99, 35-44.
Lundberg, L., & Squire, L. (2000). Inequality and growth; Lessons for policy. Mimeo, World Bank, Washington, D. C.
Mekonnen, A. G. (2024). Estimating multidimensional poverty: A new methodological approach. Journal of Poverty, 1-19
Motaghi, S., Gholami, R., Talei, S., & Ebrahimi, S. (2023). An Analysis of Regional Poverty and Factors Affecting it in Developed and Developing Countries. Journal of economics and regional development, 30(25), 39-66. (in Persian)
Nguyen, T. T., Hoang, V. N., Wilson, C., & Managi, S. (2019). Energy transition, poverty and inequality in Vietnam. Energy Policy, 132, 536-548.
Nurlina, N., Ridha, A., Syahputra, R., & Muda, I. (2024). Impact of selected macroeconomic on poverty alleviation in Indonesia: Evidence from NARDL approach. Journal of Infrastructure, Policy and Development, 8(8), 5166.
Saddique, R., Zeng, W., Zhao, P., & Awan, A. (2023). Understanding multidimensional poverty in pakistan: implications for regional and demographic-specific policies. Environmental Science and Pollution Research, 1-16.
Shabanzadeh-Khoshrody, M., Javdan, E., & Shemshadi, K. (2024). Spatial Distribution of Poverty, Food Insecurity and Factors Affecting it in Urban Areas of Iran. Journal of Agricultural Economics and Development, (), -. (in Persian)
Tasan, M., Piraee, K., Nonejad, M., & Abdoshahi, A. (2021). The Effect of Economic Openness on Improving the Life of the Poor in Iran. Agricultural Economics Research, 13(1), 123-146. (in Persian)
Yan, J., Işık, C., & Gu, X. (2024). The nexus between natural resource development, trade policy uncertainty, financial technology and poverty in China: Contributing to the realization of SDG 1. Resources Policy, 95, 105154.

The Nonlinear Effect of Inflationary Expectations and Budget Deficit on Inflation in Iran

Samaneh Omidpour

Introduction
Inflation, as one of the structural and chronic issues of Iran's economy, has always remained at high levels and has had widespread effects on macroeconomic variables and social welfare. The persistence of high inflation leads to instability in economic, social, and political spheres, to the extent that in some cases, inflationary instability can even result in the downfall of governments. Therefore, understanding the roots of inflation can help policymakers in designing appropriate policies to control and curb it.
Numerous studies have examined the factors influencing inflation and the role of inflation expectations.The literature review revealed that, so far, no comprehensive research has been conducted on the factors affecting inflation, with an emphasis on the nonlinear relationship between inflation expectations and budget deficits within the framework of the New Keynesian approach in Iran. Additionally, the study employs the Hodrick-Prescott filter and the Kalman filter to measure inflation expectations, which is considered an innovative approach. Furthermore, relying on the New Keynesian framework, this study examines the role of the output gap, budget deficit, exchange rate, and inflation expectations in the formation of inflation, specifically identifying the asymmetric impact of inflation expectations.
Method
The aim of this study is to examine the nonlinear effects of inflation expectations and budget deficits on inflation in Iran. For this purpose, the New Keynesian approach and the Nonlinear Autoregressive Distributed Lag (NARDL) method have been used to estimate the model over the period 1988 to 2022. Inflation expectations have been calculated using two methods: the Hodrick-Prescott filter and the Kalman filter.
Results and Discussion
The research findings indicate that the estimated models based on both filters yielded very similar results, demonstrating the robustness of the study's outcomes. Additionally, the results show that variables such as the output gap, inflation expectations, budget deficit, and exchange rate influence the inflation rate. Furthermore, inflation expectations have an asymmetric effect on inflation, where their increase leads to greater persistence and stability of inflation. Moreover, inappropriate fiscal policies exacerbate inflationary pressures by intensifying the budget deficit.
Keywords: Inflationary expectations, budget deficit, inflation, Iran, NARDL.
JEL: E62, E31,H62

Investigating the impact of monetary, financial and exchange market conditions on fuel smuggling in the Iranian economy using the SVAR model

Zahra Hashemi

The article examines the impact of monetary, financial and exchange market pressure indexes on fuel and petroleum products smuggling in Iran's economy by applying SVAR structural vector autoregression model based on seasonal data of 1390-1400. the model results show that the impulse response from the domestic and FOB Persian Gulf price difference of petroleum products, fuel smuggling constantly aggravates and its effect is increasing. In addition, the impulse response of the financial condition index and monetary conditional index is not very important, and the impulse from the exchange market pressure index has a negative effect at first, and after 5 periods of shock, it becomes zero and its effect becomes positive.
The results of the variance decomposition analysis show that in different periods, the fluctuations of the fuel smuggling variable are explained by the difference between domestic and FOB Persian Gulf price differenence of petroleum products. In fact, fixing the domestic oil products price below the export parity price is a very inefficient way to subsidize domestic oil consumption. In addition to the waste caused by low price, it has provided rents for smugglers and worsens the country's fiscal imbalance.

The impact of China's economic growth shocks on Iran's macroeconomic variables: application of the GVAR model

Ghodratollah Emamverdi

Due to the wide trade relationship between the countries of the world and the economic dependence of the countries on the global economy, the boom or record in the great economic powers of the world will quickly affect the economy of other countries.
In recent years, China has become one of the largest economic powers in the world and has been one of Iran's main trading partners for many years and is one of the countries that can have the greatest impact on Iran's economy.
On the other hand, due to the tightening of international sanctions on Iran in recent years, many measures have been taken to expand trade relations with other countries and attract foreign capital, among which the role of China as the main trading partner of Iran is prominent and it is necessary to reduce the shocks caused by To know the changes in China's economic growth and their effect on the macroeconomic indicators of the country.
Accordingly, the present study examines the effect of China's economic growth shocks on Iran's real GDP, inflation rate, and non-oil exports. In this regard, (GVAR) model and seasonal data from 1992 to 2022 for 34 major trading partner countries of Iran have been used.
The results of the study showed that the effect of a positive shock in China's real GDP on Iran's real GDP is positive in the short term, but in the long term, the said shock is negative and in the direction of its reduction. In relation to inflation, the effect of a positive shock to China's real production on Iran's inflation rate has always been positive and negative on Iran's non-oil exports.

Predicting Bitcoin Price Volatility Using Heterogeneous Autoregressive (HAR) models

fakhrodin fakhrehosseini

Predicting financial asset volatility is highly important because this information can help investors make more informed decisions regarding buying and selling. Accurate predictions can also reduce financial risks and identify profitable opportunities. Ultimately, the ability to forecast market changes improves portfolio management strategies and minimizes unexpected losses for investors. This study examines and predicts Bitcoin price volatility by using innovative data analysis models. The Heterogeneous Autoregressive (HAR) model and its variants were selected as the primary tools for modeling volatility because of their high capability to analyze volatility data across different time scales. Given the unique characteristics of cryptocurrency markets and rapid, unpredictable price fluctuations, the use of models that can simultaneously capture both short- and long-term volatility is of significant importance. In this study, high-frequency historical Bitcoin price data from 2018 to 2022, covering 60-minute, daily, weekly, and monthly intervals, were analyzed using the HAR, HARJ, HARQ, and HARQJ models. The results indicate that heterogeneous models have strong predictive power for Bitcoin price volatility, and incorporating jump factors into these models further improves their forecasting accuracy.
Predicting financial asset volatility is highly important because this information can help investors make more informed decisions regarding buying and selling. Accurate predictions can also reduce financial risks and identify profitable opportunities. Ultimately, the ability to forecast market changes improves portfolio management strategies and minimizes unexpected losses for investors. This study examines and predicts Bitcoin price volatility by using innovative data analysis models. The Heterogeneous Autoregressive (HAR) model and its variants were selected as the primary tools for modeling volatility because of their high capability to analyze volatility data across different time scales. Given the unique characteristics of cryptocurrency markets and rapid, unpredictable price fluctuations, the use of models that can simultaneously capture both short- and long-term volatility is of significant importance. In this study, high-frequency historical Bitcoin price data from 2018 to 2022, covering 60-minute, daily, weekly, and monthly intervals, were analyzed using the HAR, HARJ, HARQ, and HARQJ models. The results indicate that heterogeneous models have strong predictive power for Bitcoin price volatility, and incorporating jump factors into these models further improves their forecasting accuracy.