IDEAS home Printed from https://ideas.repec.org/a/eco/journ2/2024-04-34.html
   My bibliography  Save this article

Estimating the Impact of Oil Price Volatility on the Ecuadorian Economy: A MIDAS Approach

Author

Listed:
  • Freddy Ronalde Camacho-Villagomez

    (Catholic University of Santiago de Guayaquil, Faculty of Economics and Business, Ecuador)

  • Yanina Shegia Bajaña-Villagomez

    (Catholic University of Santiago de Guayaquil, Faculty of Economics and Business, Ecuador)

  • Andrea Johanna Rodríguez-Bustos

    (Catholic University of Santiago de Guayaquil, Faculty of Economics and Business, Ecuador)

Abstract

This paper provides empirical evidence about the impact of oil price volatility on government spending, tax revenues, and economic growth for Ecuador. The effects are estimated via a MIDAS regression using quarterly and monthly data for the period 2004 to 2019. The results show that oil price volatility has a positive impact on government spending and tax revenues which is an indicative of a non-prudence behavior in fiscal policy. The impact of GDP is non-significant, contrary to economic intuition and previous studies. These findings have several policy implications that are consistent with the literature of fiscal policy in oil-exporting countries.

Suggested Citation

  • Freddy Ronalde Camacho-Villagomez & Yanina Shegia Bajaña-Villagomez & Andrea Johanna Rodríguez-Bustos, 2024. "Estimating the Impact of Oil Price Volatility on the Ecuadorian Economy: A MIDAS Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 14(4), pages 371-376, July.
    • Handle: RePEc:eco:journ2:2024-04-34
    as

    Download full text from publisher

    File URL: https://www.econjournals.com/index.php/ijeep/article/download/16285/8048
    Download Restriction: no
    File URL: https://www.econjournals.com/index.php/ijeep/article/view/16285
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    2. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    3. Oriakhi D.E & Iyoha Daniel Osaze, 2013. "Oil Price Volatility and its Consequences on the Growth of the Nigerian Economy: An Examination (1970-2010)," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 3(5), pages 683-702.
    4. El Anshasy, Amany A. & Bradley, Michael D., 2012. "Oil prices and the fiscal policy response in oil-exporting countries," Journal of Policy Modeling, Elsevier, vol. 34(5), pages 605-620.
    5. Mohammadi, Hassan & Su, Lixian, 2010. "International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models," Energy Economics, Elsevier, vol. 32(5), pages 1001-1008, September.
    6. repec:eco:journ2:2017-04-21 is not listed on IDEAS
    7. Jouchi Nakajima, 2011. "Time-Varying Parameter VAR Model with Stochastic Volatility: An Overview of Methodology and Empirical Applications," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 29, pages 107-142, November.
    8. Pan, Zhiyuan & Wang, Yudong & Wu, Chongfeng & Yin, Libo, 2017. "Oil price volatility and macroeconomic fundamentals: A regime switching GARCH-MIDAS model," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 130-142.
    9. Tarek Bouazizi & Fatma Mrad & Arafet Hamida & Sawsen Nafti, 2022. "Effects of Conditional Oil Volatility on Exchange Rate and Stock Markets Returns," International Journal of Energy Economics and Policy, Econjournals, vol. 12(2), pages 53-71, March.
    10. Oriakhi D.E & Iyoha Daniel Osaze, 2013. "Oil Price Volatility and its Consequences on the Growth of the Nigerian Economy: An Examination (1970-2010)," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 3(5), pages 683-702, May.
    11. Kiseok Lee & Shawn Ni & Ronald A. Ratti, 1995. "Oil Shocks and the Macroeconomy: The Role of Price Variability," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 39-56.
    12. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    13. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    14. Olivier Blanchard & Roberto Perotti, 2002. "An Empirical Characterization of the Dynamic Effects of Changes in Government Spending and Taxes on Output," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 117(4), pages 1329-1368.
    15. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    16. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    17. Hou, Aijun & Suardi, Sandy, 2012. "A nonparametric GARCH model of crude oil price return volatility," Energy Economics, Elsevier, vol. 34(2), pages 618-626.
    18. Rotimi Ayoade Ogunjumo & Nuhu Musa & Ibrahim Adama & Silas Idoko Abu & Hitlar Inedu & Salami Hamzat & Stephen Adesina Ibitowa, 2024. "Nigeria’s Volatile Oil Revenue: Is There a Cause for Concern?," International Journal of Energy Economics and Policy, Econjournals, vol. 14(2), pages 299-303, March.
    19. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lu Wang & Feng Ma & Guoshan Liu & Qiaoqi Lang, 2023. "Do extreme shocks help forecast oil price volatility? The augmented GARCH‐MIDAS approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 2056-2073, April.
    2. Wang, Yudong & Liu, Li & Ma, Feng & Wu, Chongfeng, 2016. "What the investors need to know about forecasting oil futures return volatility," Energy Economics, Elsevier, vol. 57(C), pages 128-139.
    3. Charles, Amélie & Darné, Olivier, 2017. "Forecasting crude-oil market volatility: Further evidence with jumps," Energy Economics, Elsevier, vol. 67(C), pages 508-519.
    4. Raúl De Jesús Gutiérrez & Reyna Vergara González & Miguel A. Díaz Carreño, 2015. "Predicción de la volatilidad en el mercado del petróleo mexicano ante la presencia de efectos asimétricos," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, March.
    5. Fang, Tong & Lee, Tae-Hwy & Su, Zhi, 2020. "Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 36-49.
    6. Xiafei Li & Yu Wei & Xiaodan Chen & Feng Ma & Chao Liang & Wang Chen, 2022. "Which uncertainty is powerful to forecast crude oil market volatility? New evidence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4279-4297, October.
    7. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2003. "Some Like it Smooth, and Some Like it Rough: Untangling Continuous and Jump Components in Measuring, Modeling, and Forecasting Asset Return Volatility," PIER Working Paper Archive 03-025, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Sep 2003.
    8. Ekaterina Smetanina, 2017. "Real-Time GARCH," Journal of Financial Econometrics, Oxford University Press, vol. 15(4), pages 561-601.
    9. Nagaraj Naik & Biju R. Mohan, 2021. "Stock Price Volatility Estimation Using Regime Switching Technique-Empirical Study on the Indian Stock Market," Mathematics, MDPI, vol. 9(14), pages 1-18, July.
    10. Auer, Benjamin R., 2014. "Daily seasonality in crude oil returns and volatilities," Energy Economics, Elsevier, vol. 43(C), pages 82-88.
    11. Di Sanzo, Silvestro, 2018. "A Markov switching long memory model of crude oil price return volatility," Energy Economics, Elsevier, vol. 74(C), pages 351-359.
    12. Lin, Yu & Xiao, Yang & Li, Fuxing, 2020. "Forecasting crude oil price volatility via a HM-EGARCH model," Energy Economics, Elsevier, vol. 87(C).
    13. Wang, Yudong & Wu, Chongfeng, 2012. "Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?," Energy Economics, Elsevier, vol. 34(6), pages 2167-2181.
    14. Pablo Cansado-Bravo & Carlos Rodríguez-Monroy, 2018. "Persistence of Oil Prices in Gas Import Prices and the Resilience of the Oil-Indexation Mechanism. The Case of Spanish Gas Import Prices," Energies, MDPI, vol. 11(12), pages 1-17, December.
    15. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2005. "Volatility forecasting," CFS Working Paper Series 2005/08, Center for Financial Studies (CFS).
    16. Chen, Hongtao & Liu, Li & Li, Xiaolei, 2018. "The predictive content of CBOE crude oil volatility index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 837-850.
    17. Herrera, Ana María & Hu, Liang & Pastor, Daniel, 2018. "Forecasting crude oil price volatility," International Journal of Forecasting, Elsevier, vol. 34(4), pages 622-635.
    18. Lin, Boqiang & Wesseh, Presley K., 2013. "What causes price volatility and regime shifts in the natural gas market," Energy, Elsevier, vol. 55(C), pages 553-563.
    19. Kambouroudis, Dimos S. & McMillan, David G., 2015. "Is there an ideal in-sample length for forecasting volatility?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 37(C), pages 114-137.
    20. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.

    More about this item

    Keywords

    MIDAS; Ecuador; Volatility; Oil Price; Fiscal Policy;
    All these keywords.

    JEL classification:

    • H50 - Public Economics - - National Government Expenditures and Related Policies - - - General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E62 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Fiscal Policy; Modern Monetary Theory

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eco:journ2:2024-04-34. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ilhan Ozturk (email available below). General contact details of provider: http://www.econjournals.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.