Vol. 15 No. 2 (44) (2024)

DOI: https://doi.org/10.46991/BYSU.G/2024.15.2
Published: 2025-01-30

Full Issue

Management

Finance

Economic and mathematical modeling

  • Economic and mathematical modeling

    ASSESSMENT OF THE LONG-TERM RELATIONSHIP BETWEEN RA PERSONAL TRANSFERS-ECONOMIC GROWTH USING THE VEC MODEL

    Alvard Kharatyan, Anahit Buniatyan
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    Abstract

    The article evaluates the macroeconomic impact of personal transfers from abroad in Armenia. The analysis is based on the quarterly data of macroeconomic indicators for the period 1996–2024. The data sources are the databases of the Central Bank of Armenia and the Statistical Committee of Armenia. Hypotheses have been proposed, and to test them, regression and Vector Error Correction (VEC) models were constructed. The Granger causality test showed that there is a unidirectional causality—personal transfers from abroad are the cause of changes in economic growth, but the reverse is not true. The Johansen cointegration test revealed that there is a long-term relationship between personal transfers from abroad and economic growth in Armenia. According to the results of the VEC model, a 1% increase in personal transfers from abroad contributes to a 0.42% increase in Armenia's real GDP in the long run, while a 1% increase in the export of goods and services contributes to a 0.35% increase. An increase in the rate of economic growth in Armenia leads to a reduction in personal transfers from abroad. A 1% increase in real GDP results in a 1.08% decrease in external personal transfers after 4 quarters, 1.05% after 5 quarters, and 1.22% after 6 quarters. According to the results of the regression and VEC models, personal transfers from abroad do not have a significant short-term impact on Armenia’s economic growth. The results of the research can be useful in the development of Armenia's macroeconomic policy.

    References

    Abdelhadi, Samer & Ala’ Bashayreh. Remittances and Economic Growth Nexus: Evidence from Jordan. International Journal of Business and Social Scienc, Volume 8, Number 12, December 2017, 98-102.

    Ahmed, Haydory Akbar & Uddin, Md.Gazi Salah. Exports, Imports, Remittance and Growth in Bangladesh: An Emprical Analysis. Trade and Development Review, Vol. 2, Issue 2, 2009, 79-92.

    Bucevska, Vesna. Impact of remittances on economic growth: empirical evidence from South-East European countries. South East European Journal of Economics and Business. Volume 17 (1) 2022, 79-94.

    Cazachevici, Alina, Havranek Tomas & Horvath Roman. Remittances and economic growth: A meta-analysis. World Development, Volume 134, October 2020, Page 105021. https://www.sciencedirect.com/science/article/abs/pii/S0305750X20301479

    Comes, Calin-Adrian, Elena Bunduchi, Valentina Vasile & Daniel Stefan. The Impact of Foreign Direct Investments and Remittances on Economic Growth: A Case Study in Central and Eastern Europe. MDPI. Sustainability, 2018, 10, 238, 1-16.

    Engle, Robert & C.W. Granger. Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, Vol. 55, No. 2. Mar., 1987, 251-276.

    Johansen, Soren. Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control.Volume 12, Issues 2–3, June–September 1988, 231-254.

    Kumar, Ronald Ravinesh, Peter Josef Stauvermann, Arvind Patel & Selvin Prasad. The Effect of Remittances on Economic Growth in Kyrgyzstan and Macedonia: Accounting for Financial Development. International Migration Vol. 56 (1) 2018, pp. 95-126.

    Paul, J. Gertler, Sebastian W. Martinez & Marta Rubio-Codina. Investing Cash Transfers to Raise Long-Term Living Standards. American Economic Journal: Applied Economics 2012, 4(1), 164–192.

    Shera, Adela & Dietmar Meyer. Remittances and their impact on Economic Growth. Social and Management Sciences 21/1, 2013, 3-19.

    Olofsdotter, Karin & Ravshanbek Abdullaev. Impact of remittances on economic growth in selected Asian and Former Soviet Union countries. Lund University, School of Economics and Management, 2011, 16-31.

    Central Bank of Armenia, 2024 https://www.cba.am/am/SitePages/statexternalsector.aspx

    Statistical Committee of the RA, 2024 https://armstat.am/en/

  • Economic and mathematical modeling

    COMPARISON OF MARKOWITZ AND HELLINGER-NORMAL PORTFOLIOS

    Vardan Bardakhchyan, Mr. Mesrop Mesropyan
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    Abstract

    In this paper we compare the performances of Markowitz portfolio and the portfolio closest to normal in distribution. The latter is obtained by fixing the same desired level of expected returns and optimizing the Hellinger distance to Gaussian distribution with parameters obtained from Markowitz portfolio optimization for the same expected return. We confine ourselves to long-position portfolio only. We found that in contrast to the expectations, the Hellinger-Normal portfolio does not smooth enough the extreme loses, but do not worse in that regard than Markowitz portfolio. We also found that overall in non-long run passively managed portfolios, the Hellinger-Normal portfolio had better overall realized Sharpe and Kelly ratios.

    References

    References

    Bardakհchyan, Vardan, and Mesrop Mesropyan. 2023. "Hellinger’s distance to normal distribution as market invariant." Bulletin of Yerevan University G: Economics 14, no. 1 (40): 64-71. https://doi.org/10.46991/BYSU:G/2023.14.1.064

    Beaulieu, Norman C., and Qiong Xie. 2004. "An optimal lognormal approximation to lognormal sum distributions." IEEE Transactions on vehicular technology 53.2: 479-489. DOI: 10.1109/TVT.2004.823494

    Becker, Franziska, and Marc Gürtler. 2010. "Quantitative forecast model for the application of the Black-Litterman approach." Paris December 2009 Finance International Meeting AFFI-EUROFIDAI. Available at SSRN: https://ssrn.com/abstract=1483571 or http://dx.doi.org/10.2139/ssrn.1483571

    Cevizci, Aylin. 2016. "A comparison of optimal portfolio performances of three optimization methods." International Journal of Commerce and Finance 2.1: 137-146.

    Corlu, Canan G., Melike Meterelliyoz, and Murat Tinic. 2016. "Empirical distributions of daily equity index returns: A comparison." Expert systems with applications 54: 170-192; https://doi.org/10.1016/j.eswa.2015.12.048

    De Athayde, Gustavo M., and Renato G. Flôres. 2003. "Incorporating skewness and kurtosis in portfolio optimization: A multidimensional efficient set." In Advances in portfolio construction and implementation, pp. 243-257. Butterworth-Heinemann. https://doi.org/10.1016/B978-075065448-7.50011-2

    Durrett, Rick. 2019. Probability: theory and examples. Vol. 49. Cambridge university press.

    Egan, William J., 2007. "The distribution of S&P 500 index returns." Available at SSRN 955639. Available at SSRN https://ssrn.com/abstract=955639 or http://dx.doi.org/10.2139/ssrn.955639

    Ibragimov, I. 1975."Independent and stationary sequences of random variables." Wolters, Noordhoff Pub.

    Kane, Alex. 1982. "Skewness preference and portfolio choice." Journal of Financial and Quantitative Analysis 17, no. 1: 15-25. https://doi.org/10.2307/2330926

    Kim, Hongseon, Soonbong Lee, Seung Bum Soh, and Seongmoon Kim. 2022. "Improving portfolio investment performance with distance‐based portfolio‐combining algorithms." Journal of Financial Research 45, no. 4: 941-959. https://doi.org/10.1111/jfir.12303

    Kraus, Alan, and Robert H. Litzenberger. 1976. "Skewness preference and the valuation of risk assets." The Journal of finance 31, no. 4: 1085-1100. https://doi.org/10.2307/2326275

    Lai, Kin Keung, Lean Yu, and Shouyang Wang. 2006. "Mean-variance-skewness-kurtosis-based portfolio optimization." In First International Multi-Symposiums on Computer and Computational Sciences (IMSCCS'06), vol. 2, pp. 292-297. IEEE. DOI: 10.1109/IMSCCS.2006.239

    Mercurio, Peter Joseph, Yuehua Wu, and Hong Xie. 2020. "An entropy-based approach to portfolio optimization." Entropy 22, no. 3: 332. https://doi.org/10.3390/e22030332

    Mesropyan, M. T., and V. G. Bardakhchyan. 2023. "Hellinger’s Distance and Correlation for a Subclass of Stable Distributions." Journal of Contemporary Mathematical Analysis (Armenian Academy of Sciences) 58, no. 3: 191-195. https://doi.org/10.3103/S106836232303007X

    M. Mesropyan and V. Mkrtchyan. 2021. "Assessing normality of group of assets based on portfolio construction", Alternative scientific journal, 3, pp. 14-21.

    Michaud, Richard O., and Tongshu Ma. 2015. "Efficient asset management: a practical guide to stock portfolio optimization and asset allocation.": 901-904. https://doi.org/10.1093/rfs/14.3.901

    Post, Thierry, Pim Van Vliet, and Haim Levy. 2008. "Risk aversion and skewness preference." Journal of Banking & Finance 32, no. 7: 1178-1187.

    Qian, Edward, and Stephen Gorman. 2001. "Conditional distribution in portfolio theory." Financial Analysts Journal 57.2: 44-51. https://doi.org/10.2469/faj.v57.n2.2432

    Rachev, Svetlozar T., Lev B. Klebanov, Stoyan V. Stoyanov, Frank Fabozzi. 2013. "Main Directions in the Theory of Probability Metrics." The Methods of Distances in the Theory of Probability and Statistics: 1-7. https://doi.org/10.1007/978-1-4614-4869-3_1

    Rachev, Svetlozar, Sergio Ortobelli, Stoyan Stoyanov, Frank J. Fabozzi, and Almira Biglova. 2008. "Desirable properties of an ideal risk measure in portfolio theory." International Journal of Theoretical and Applied Finance 11, no. 01: 19-54. https://doi.org/10.1142/S0219024908004713

    Roncalli, Thierry. 2013. Introduction to risk parity and budgeting. CRC Press.

    Shiryaev, Albert, Zuoquan Xu, and Xun Yu Zhou. 2008. "Thou shalt buy and hold." Quantitative finance 8.8: 765-776. https://doi.org/10.1080/14697680802563732

    Yao, Haixiang, Zhongfei Li, and Yongzeng Lai. 2013. "Mean–CVaR portfolio selection: A nonparametric estimation framework." Computers & Operations Research 40, no. 4: 1014-1022. https://doi.org/10.1016/j.cor.2012.11.007

  • Economic and mathematical modeling

    THE SIGNIFICANCE OF EXPENDITURE POLICY IN FOSTERING ECONOMIC GROWTH (CASE OF ARMENIA)

    Taguhi Chapanyan
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    Abstract

    Budget expenditures can have a significant positive or negative impact on the economy of the country. The article refers to the dynamics, structure and interrelationship of economic growth and budget expenditures in Armenia. The impact of Armenia's budget expenditures, as well as individual items of the functional classification of budget expenditures, on economic growth was estimated using the least squares method. For the research have been used quarterly data during the 2000-2003 period.

    According to the results of the regression analysis, overall budget expenditures of Armenia have a positive effect on economic growth: an increase of 1 percent of budget expenditure would tend to increase economic growth by 0.14 percent after two quarters,  all other things being equal.

    From the items of the functional classification, spending on health and general public services has a positive effect on economic growth: an increase of 1 percent of health expenditure, economic growth will increase by 0.07% after 3 quarters, and an  increase of 1 percent of expenditure on general public services will increase the economic growth by 0.05% after two quarters, all other things being equal.

    Spending on defense and public order and safety activities will reduce economic growth: a 1% increase in public order and safety spending will reduce economic growth by 0.06% after two quarters, ceteris paribus.

    References

    1. Bose N., Haque M E., Osborn D.R., “Public expenditure and economic growth: A disaggregated analysis for developing countries.” The Manchester School, 75 (5), 2007, p 549.

    2. Chapanyan T., Theoretical Foundations of the Relationship Between the Structure of Public Spending and Economic Growth, Scientific Journal of Public Administration 1/2021, pp. 77-88, https://paara.am/wp-content/uploads/2023/07/taguhi-chapanyan.pdf

    3. Grigoryan K., Petrosyan G., Vardanyan K., Avagyan G. (2021). Assessment of the effects of public investment on GDP growth: case of Armenia. Sciences of Europe, No. 78, Vol. 2, pages 46-60. DOI: 10.24412/3162-2364-2021-78-2-46-60, https://www.europe-science.com/wp-content/uploads/2021/09/Sciences-of-Europe-No-78-2021-Vol.-2.pdf

    4. IMF database, https://data.imf.org

    5. Landau, D. (1983). Government Expenditure and Economic Growth: A Cross-Country Study. Southern Economic Journal, 49(3), 783–792. https://doi.org/10.2307/1058716

    6. Leke PULA, Alban ELSHANI, “Role of public expenditure in economic growth: Economic evidence from Kosovo 2002-2015”, Baltic journal of real estate economics and construction management, 2018, p 77.

    7. Lucas, R. E. (1988). On the Mechanics of Economic Development. Journal of Monetary Economics, 22, 3–42.

    8. Ministry of Finance of the Republic of Armenia, Report of the state budget of the Republic of Armenia for 2023, https://minfin.am/hy/page/petakan_byujei_hashvetvutyun_2023t_tarekan_

    9. Romer, P. M. (1986). Increasing Returns and Long-Run Growth. The Journal of Political Economy, 94(5), 1002–1037. https://doi.org/10.1086/261420

    10. Sáez, M. P., Álvarez-García, S., & Rodríguez, D. C.(2017). Government expenditure and economic growth in the European Union countries: New evidence. Bulletin of Geography. Socio-Economic Series, 36(36), 127–133. DOI http://dx.doi.org/10.1515/bog-2017-0020%20

    11. Solow, R. M. (1956). A Contribution to the Theory of Economic Growth. The Quartely Journal of Economics, 70(1), 65–94.

    12. Statistical Committee of the Republic of Armenia, Yearbooks of 2000-2023, www.armstat.am

    13. Swan, T. W. (1956). Economic Growth and Capital Accumulation. Economic Record, 32(2), 334– 361. https://doi.org/10.1111/j.1475-4932.1956.tb00434.x

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