Asymetric Shocks and Long-Memory Volatility: An Egarch Approach to Global Oil and Local Import Dynamics
DOI:
https://doi.org/10.47743/saeb-2026-0023Keywords:
volatility modeling, GARCH, EGARCH, commodity import price index, structural breaks, leverage effects, small open economies.Abstract
This study investigates the volatility dynamics of commodity import prices in Republic of Moldova and global Brent crude oil prices, employing advanced econometric models to enhance understanding of risk in small open economies and energy markets. Utilizing monthly data from 1992 to 2025 for Republic of Moldova's Commodity Import Price Index and from January 1990 to March 2025 for Brent oil, the analysis confirms that both series are integrated of order one, with no significant structural breaks in the mean, supporting constant-parameter modelling. For Republic of Moldova's Commodity Import Price Index, an AR (1)-GARCH (1,1) specification effectively captures high volatility persistence and symmetric shock responses, indicating long-memory effects without asymmetry. In contrast, Brent oil exhibits leverage effects, where negative shocks amplify volatility more than positive ones, best demonstrated via AR(1)-EGARCH(1,1). Comprehensive diagnostics, including ADF tests, Bai-Perron structural break analysis, ARCH-LM, Ljung-Box, and Nyblom stability tests, validate model adequacy, confirming residual whiteness and structural stability. These findings underscore the need for symmetric risk management in Republic of Moldova's import sector and asymmetric hedging in oil markets, with implications for macroeconomic stability and policy formulation. Research gaps highlight opportunities for multivariate spillover analysis and regime-switching models to address cross-market dependencies.
References
Abdulkareem, A., & Abdulkareem, K. A. (2016). Analysing Oil Price-Macroeconomic Volatility in Nigeria. CBN Journal of Applied Statistics, 7(1), 1–22.
Al-Sharoot, M., & Al-Rashide, H. A. (2024). Forecasting the volatility of OPEC oil prices using EGARCH and ARMA–GARCH models. 54(1), 400–417. http://dx.doi.org/10.55562/jrucs.v54i1.609
Aloui, C., & Jammazi, R. (2009). The effects of crude oil shocks on stock market shifts behaviour: A regime switching approach. Energy Economics, 31(5), 789–799. http://dx.doi.org/10.1016/j.eneco.2009.03.009
Amiri, O., & Remita, M. (2022). Measuring the impact of COVID-19 pandemic on oil price volatility through GARCH modelling. 11(6), 297–306. http://dx.doi.org/10.37418/amsj.11.6.2
Awartani, B., & Maghyereh, A. I. (2013). Dynamic spillovers between oil and stock markets in the Gulf Cooperation Council countries. Energy Economics, 36, 28–42. http://dx.doi.org/10.1016/j.eneco.2012.11.024
Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78. http://dx.doi.org/10.2307/2998540
Bai, J., & Perron, P. (2003). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18(1), 1–22. http://dx.doi.org/10.1002/jae.659
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. http://dx.doi.org/10.1016/0304-4076(86)90063-1
Brooks, C., Burke, S. P., & Persand, G. (2001). Benchmarks and the accuracy of GARCH model estimation. International Journal of Forecasting, 17(1), 45–56. http://dx.doi.org/10.1016/S0169-2070(00)00070-4
Büyükşahin, B., & Robe, M. A. (2014). Speculators, commodities and cross-market linkages. Journal of International Money and Finance, 42, 38–70. http://dx.doi.org/10.1016/j.jimonfin.2013.08.004
Canova, F., & Hansen, B. E. (1995). Are seasonal patterns constant over time? A test for seasonal stability. Journal of Business & Economic Statistics, 13(3), 237–252. http://dx.doi.org/10.2307/1392184
Cashin, P., Liang, H., & McDermott, C. J. (2000). How persistent are shocks to world commodity prices? IMF Staff Papers, 47(2), 177–217. http://dx.doi.org/10.2307/3867658
Chang, C. L., McAleer, M., & Tansuchat, R. (2013). Conditional correlations and volatility spillovers between crude oil and stock index returns. The North American Journal of Economics and Finance, 25, 116–138. http://dx.doi.org/10.1016/j.najef.2012.06.002
Chevallier, J., & Ielpo, F. (2013). Volatility spillovers in commodity markets. Applied Economics Letters, 20(13), 1211–1227. http://dx.doi.org/10.1080/13504851.2013.799748
Creti, A., Joëts, M., & Mignon, V. (2013). On the links between stock and commodity markets’ volatility. Energy Economics, 37, 16–28. http://dx.doi.org/10.1016/j.eneco.2013.01.005
Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66. http://dx.doi.org/10.1016/j.ijforecast.2011.02.006
Dinku, T., & Worku, G. (2022). Asymmetric GARCH models on price volatility of agricultural commodities. SN Business & Economics, 2, 181. http://dx.doi.org/10.1007/s43546-022-00355-7
Ederington, L. H., & Salas, J. M. (2008). Minimum variance hedging when spot price changes are partially predictable. Journal of Banking & Finance, 32(5), 654–663. http://dx.doi.org/10.1016/j.jbankfin.2007.05.003
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. http://dx.doi.org/10.2307/1912773
Frankel, J. A. (2012). The natural resource curse: A survey of diagnoses and some prescriptions (CID Working Paper No. 233). Retrieved from Cambridge, MA: https://growthlab.hks.harvard.edu/wp-content/uploads/2015/02/cid_working_paper_233.pdf
Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48(5), 1779–1801. http://dx.doi.org/10.1111/j.1540-6261.1993.tb05128.x
Hammoudeh, S., & Li, H. (2008). Sudden changes in volatility in emerging markets: The case of Gulf Arab stock markets. International Review of Financial Analysis, 17(1), 47–63. http://dx.doi.org/10.1016/j.irfa.2005.01.002
Hansen, B. E. (1990). Lagrange Multiplier Tests for Parameter Instability in Non-Linear Models. Unpublished manuscript. Rochester, NY.
Hansen, P. R., & Lunde, A. (2005). A forecast comparison of volatility models: Does anything beat a GARCH(1,1)? Journal of Applied Econometrics, 20(7), 873–889. http://dx.doi.org/10.1002/jae.800
Hung, N. T., Thach, N. N., & Anh, L. H. (2018). GARCH Models in Forecasting the Volatility of the World’s Oil Prices. In L. H. Anh, L. S. Dong, V. Kreinovich, & N. N. Thach (Eds.), Econometrics for Financial Applications (Vol. 760, pp. 673–683). Cham: Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-73150-6_53
Hylleberg, S., Engle, R. F., Granger, C. W. J., & Yoo, B. S. (1990). Seasonal integration and cointegration. Journal of Econometrics, 44(1–2), 215–238. http://dx.doi.org/10.1016/0304-4076(90)90080-D
IMF. (2025). Primary commodity price system.
Jacks, D. S., O’Rourke, K. H., & Williamson, J. G. (2011). Commodity price volatility and world market integration since 1700. The Review of Economics and Statistics, 93(3), 800–813. http://dx.doi.org/10.1162/REST_a_00091
Kang, S. H., Kang, S. M., & Yoon, S. M. (2009). Forecasting volatility of crude oil markets. Energy Economics, 31(1), 119–125. http://dx.doi.org/10.1016/j.eneco.2008.09.006
Karali, B., & Power, G. J. (2013). Short- and long-run determinants of commodity price volatility. American Journal of Agricultural Economics, 95(3), 724–738. http://dx.doi.org/10.1093/ajae/aas122
Kristjanpoller, W., & Minutolo, M. C. (2015). Gold price volatility: A forecasting approach using the Artificial Neural Network–GARCH model. Expert Systems with Applications, 42(20), 7245–7251. http://dx.doi.org/10.1016/j.eswa.2015.04.058
Laurent, S., Rombouts, J. V. K., & Violante, F. (2012). On the forecasting accuracy of multivariate GARCH models. Journal of Applied Econometrics, 27(6), 934–955. http://dx.doi.org/10.1002/jae.1248
Lin, Y., Xiao, Y., & Li, F. (2020). Forecasting crude oil price volatility via a HM-EGARCH model. Energy Economics, 87. http://dx.doi.org/10.1016/j.eneco.2020.104693
Liu, L., & Wan, J. (2012). A study of Shanghai fuel oil futures price volatility based on high frequency data: Long-range dependence, modeling and forecasting. Economic Modelling, 29(6), 2245–2253. http://dx.doi.org/10.1016/j.econmod.2012.06.029
Malik, F., & Hammoudeh, S. (2007). Shock and volatility transmission in the oil, US and Gulf equity markets. International Review of Economics & Finance, 16(3), 357–368. http://dx.doi.org/10.1016/j.iref.2005.05.005
Merabet, F., Zeghdoudi, H., Yahia, R., & Saba, I. (2021). Modelling oil price volatility using ARIMA–GARCH models. 10(5), 2361–2380. http://dx.doi.org/10.37418/amsj.10.5.6
Mușetescu, R., Grigore, G., & Nicolae, S. (2022). Using GARCH autoregressive models in estimating and forecasting crude oil volatility. 14(1), 13–38. http://dx.doi.org/10.24818/ejis.2022.02
Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. http://dx.doi.org/10.2307/2938260
Nomikos, N. K., & Pouliasis, P. K. (2011). Forecasting petroleum futures markets volatility: The role of regimes and market conditions. Energy Economics, 33(2), 321–337. http://dx.doi.org/10.1016/j.eneco.2010.11.013
Nyblom, J. (1989). Testing for the constancy of parameters over time. Journal of the American Statistical Association, 84(405), 223–230. http://dx.doi.org/10.1080/01621459.1989.10478759
Olanrewaju, R. O., & Oseni, E. (2021). GARCH and its variants model: An application of crude oil distributions in Nigeria. International Journal of Accounting. International Journal of Accounting, Finance and Risk Management, 6(1), 25–35. http://dx.doi.org/10.11648/j.ijafrm.20210601.14
Patton, A. J. (2011). Volatility forecast comparison using imperfect volatility proxies. Journal of Econometrics, 160(1), 246–256. http://dx.doi.org/10.1016/j.jeconom.2010.03.034
Pindyck, R. S., & Rotemberg, J. J. (1990). The excess co-movement of commodity prices. Economic Journal (London), 100(403), 1173–1189. http://dx.doi.org/10.2307/2233966
Raddatz, C. (2007). Are external shocks responsible for the instability of output in low-income countries? Journal of Development Economics, 84(1), 155–187. http://dx.doi.org/10.1016/j.jdeveco.2006.11.001
Ramos-Pérez, E., Alonso-González, P. J., & Núñez-Velázquez, J. J. (2019). Forecasting volatility with a stacked model based on a hybridized artificial neural network. Expert Systems with Applications, 129, 1–9. http://dx.doi.org/10.1016/j.eswa.2019.03.046
Sadorsky, P. (2006). Modeling and forecasting petroleum futures volatility. Energy Economics, 28(4), 467–488. http://dx.doi.org/10.1016/j.eneco.2006.04.005
Serra, T. (2011). Volatility spillovers between food and energy markets: A semiparametric approach. Energy Economics, 33(6), 1155–1164. http://dx.doi.org/10.1016/j.eneco.2011.04.003
Silvennoinen, A., & Thorp, S. (2013). Financialization, crisis and commodity correlation dynamics. Journal of International Financial Markets, Institutions and Money, 24, 42–65. http://dx.doi.org/10.1016/j.intfin.2012.11.007
Tang, K., & Xiong, W. (2012). Index investment and the financialization of commodities. Financial Analysts Journal, 68(6), 54–74. http://dx.doi.org/10.2469/faj.v68.n6.5
van der Ploeg, F., & Poelhekke, S. (2009). Volatility and the natural resource curse. Oxford Economic Papers, 61(4), 727–760. http://dx.doi.org/10.1093/oep/gpp027
Wang, L., Ma, F., Liu, G., & Lang, Q. (2023). Do extreme shocks help forecast oil price volatility? The augmented GARCH–MIDAS approach. International Journal of Finance & Economics, 28(2), 2056–2073. http://dx.doi.org/10.1002/ijfe.2525
Wei, Y., Wang, Y., & Huang, D. (2010). Forecasting crude oil market volatility: Further evidence using GARCH-class models. Energy Economics, 32(6), 1477–1484. http://dx.doi.org/10.1016/j.eneco.2010.07.009
Yang, J., Bessler, D. A., & Leatham, D. J. (2001). Asset storability and price discovery in commodity futures markets: A new look. Journal of Futures Markets, 21(3), 279–300. http://dx.doi.org/10.1002/1096-9934(200103)21:3<279::AID-FUT5>3.0.CO;2-L
Zakoïan, J. M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics & Control, 18(5), 931–955. http://dx.doi.org/10.1016/0165-1889(94)90039-6
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Mircea Diavor, Ion Pârțachi

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
All accepted papers are published on an Open Access basis.
The Open Access License is based on the Creative Commons license.
The non-commercial use of the article will be governed by the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License as currently displayed on https://creativecommons.org/licenses/by-nc-nd/4.0
Under the Creative Commons Attribution-NonCommercial-NoDerivatives license, the author(s) and users are free to share (copy, distribute and transmit the contribution) under the following conditions:
1. they must attribute the contribution in the manner specified by the author or licensor,
2. they may not use this contribution for commercial purposes,
3. they may not alter, transform, or build upon this work.




