IDEAS home Printed from https://ideas.repec.org/a/bkr/journl/v82y2023i3p87-109.html
   My bibliography  Save this article

Copula-Based Modelling of Relationship Between Dollar/Rouble Exchange Rate and Oil Prices

Author

Listed:
  • Andrey Bedin

    (RANEPA; MIPT)

  • Alexander Kulikov

    (RANEPA; MIPT)

  • Andrey Polbin

    (Gaidar Institute for Economic Policy; RANEPA)

Abstract

Oil prices are an important factor defining the dynamics of the rouble exchange rate. In this paper, we use the copula approach to describe the impact of oil prices on the rouble exchange rate in 2016-2021 on weekly and monthly data. To model one-dimensional distributions of log oil price growth and log US dollar/rouble exchange rate growth, we compare approaches using empirical distributions and calibrated parametric distributions. We show that, even though the best copula for both weekly and monthly data is the Student copula, the response of the exchange rate to oil price changes is asymmetrical due to the higher skewness of the distribution of log exchange rate growth. The copulas considered in this paper are tested for goodness-of-fit. We also test the model on 2022 data.

Suggested Citation

  • Andrey Bedin & Alexander Kulikov & Andrey Polbin, 2023. "Copula-Based Modelling of Relationship Between Dollar/Rouble Exchange Rate and Oil Prices," Russian Journal of Money and Finance, Bank of Russia, vol. 82(3), pages 87-109, September.
    • Handle: RePEc:bkr:journl:v:82:y:2023:i:3:p:87-109
    as

    Download full text from publisher

    File URL: https://rjmf.econs.online/upload/iblock/8e4/Copula-Based-Modelling-of-Relationship-Between-Dollar-Rouble-Exchange-Rate-and-Oil-Prices.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Alexander Knyazev & Oleg Lepekhin & Arkady Shemyakin, 2016. "Joint distribution of stock indices: Methodological aspects of construction and selection of copula models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 42, pages 30-53.
    2. Лакшина Валерия Владимировна, 2016. "Динамическое Хеджирование С Учетом Степени Неприятия Риска," Higher School of Economics Economic Journal Экономический журнал Высшей школы экономики, CyberLeninka;Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский университет «Высшая школа экономики», vol. 20(1), pages 156-174.
    3. Abbritti, Mirko & Fahr, Stephan, 2013. "Downward wage rigidity and business cycle asymmetries," Journal of Monetary Economics, Elsevier, vol. 60(7), pages 871-886.
    4. Junker, Markus & Szimayer, Alex & Wagner, Niklas, 2006. "Nonlinear term structure dependence: Copula functions, empirics, and risk implications," Journal of Banking & Finance, Elsevier, vol. 30(4), pages 1171-1199, April.
    5. Boris Brodsky & Henry Penikas & Irina Safaryan, 2009. "Detection of Structural Breaks in Copula Models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 16(4), pages 3-15.
    6. Henry Penikas, 2010. "Copula-Models in Foreign Exchange Risk-Management of a Bank," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 17(1), pages 62-87.
    7. A. Polbin., 2017. "Econometric estimation of the impact of oil prices shock on the Russian economy in VECM model," VOPROSY ECONOMIKI, N.P. Redaktsiya zhurnala "Voprosy Economiki", vol. 10.
    8. Henry Penikas, 2011. "Copula-Based Price Risk Hedging Models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 22(2), pages 3-21.
    9. Chang, Kuang-Liang, 2012. "The time-varying and asymmetric dependence between crude oil spot and futures markets: Evidence from the Mixture copula-based ARJI–GARCH model," Economic Modelling, Elsevier, vol. 29(6), pages 2298-2309.
    10. Skrobotov, Anton (Скроботов, Антон) & Fokin, Nikita (Фокин, Никита), 2018. "Testing the Asymmetric Convergence of the Real Exchange Rate to Equilibrium During the Managed Ruble Exchange Rate Regime [Тестирование Ассиметричной Сходимости Реального Обменного Курса К Равновес," Working Papers 021808, Russian Presidential Academy of National Economy and Public Administration.
    11. Henry Penikas, 2022. "Pass-Through of the Bank of Russia Key Rate into Deposit Rates Between 2020 and 2022," Russian Journal of Money and Finance, Bank of Russia, vol. 81(2), pages 20-48, June.
    12. Efim Bronshtein & Elena Prokudina & Anna Gerasimova & Ksenya Dubinskaya, 2011. "Estimation of the interdependence of time series of stocks prices based on copula," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 22(2), pages 22-31.
    13. A. Polbin, 2017. "Econometric estimation of the impact of oil prices shock on the Russian economy in VECM model," Voprosy Ekonomiki, NP Voprosy Ekonomiki, issue 10.
    14. Stephanie Schmitt-Grohé & Martín Uribe, 2016. "Downward Nominal Wage Rigidity, Currency Pegs, and Involuntary Unemployment," Journal of Political Economy, University of Chicago Press, vol. 124(5), pages 1466-1514.
    15. Manner Hans, 2010. "Testing for Asymmetric Dependence," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(2), pages 1-32, March.
    16. Andrew J. Patton, 2006. "Modelling Asymmetric Exchange Rate Dependence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 47(2), pages 527-556, May.
    17. Andrey Polbin & Andrei Shumilov & Andrei Bedin & Alexander Kulikov, 2019. "Modeling real exchange rate of the Russian ruble using Markov regime switching approach," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 55, pages 32-50.
    18. Hansen, Bruce E, 1994. "Autoregressive Conditional Density Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(3), pages 705-730, August.
    19. Travkin, A., 2015. "Estimating Pair-Copula Constructions Using Empirical Tail Dependence Functions: an Application to Russian Stock Market," Journal of the New Economic Association, New Economic Association, vol. 25(1), pages 39-55.
    20. Henry Penikas & Varvara Simakova, 2009. "Interest Rate Risk Management Based on Copula-GARCH Models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 13(1), pages 3-36.
    21. Skrobotov, Anton A. (Скроботов, Антон) & Fokin, Nikita D. (Фокин, Никита), 2018. "Testing Asymmetric Convergence of the Real Exchange Rate to Equilibrium During Ruble Exchange Rate Targeting [Тестирование Асимметричной Сходимости Реального Обменного Курса К Равновесному Во Время," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 3, pages 132-147, June.
    22. Sun Wei & Rachev Svetlozar & Stoyanov Stoyan V. & Fabozzi Frank J., 2008. "Multivariate Skewed Student's t Copula in the Analysis of Nonlinear and Asymmetric Dependence in the German Equity Market," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(2), pages 1-37, May.
    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. Kim, Jong-Min & Jung, Hojin, 2017. "Can asymmetric conditional volatility imply asymmetric tail dependence?," Economic Modelling, Elsevier, vol. 64(C), pages 409-418.
    2. Beatriz de la Flor & Javier Ojea-Ferreiro & Eva Ferreira, 2022. "The Hedging Cost of Forgetting the Exchange Rate," Documentos de Trabajo del ICAE 2022-01, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    3. Liu, Xiang-dong & Pan, Fei & Cai, Wen-li & Peng, Rui, 2020. "Correlation and risk measurement modeling: A Markov-switching mixed Clayton copula approach," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    4. Arthur Charpentier, 2015. "Prévision avec des copules en finance," Working Papers hal-01151233, HAL.
    5. Manner, H., 2007. "Estimation and model selection of copulas with an application to exchange rates," Research Memorandum 056, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    6. Nikita Fokin & Andrey Polbin, 2019. "Forecasting Russia's Key Macroeconomic Indicators with the VAR-LASSO Model," Russian Journal of Money and Finance, Bank of Russia, vol. 78(2), pages 67-93, June.
    7. Rehman, Mobeen Ur & Katsiampa, Paraskevi & Zeitun, Rami & Vo, Xuan Vinh, 2023. "Conditional dependence structure and risk spillovers between Bitcoin and fiat currencies," Emerging Markets Review, Elsevier, vol. 55(C).
    8. Nikita D. Fokin & Ekaterina V. Malikova & Andrey V. Polbin, 2024. "Time-varying parameters error correction model for real ruble exchange rate and oil prices: What has changed due to capital control and sanctions?," Russian Journal of Economics, ARPHA Platform, vol. 10(1), pages 20-33, March.
    9. Chu, Ba, 2011. "Recovering copulas from limited information and an application to asset allocation," Journal of Banking & Finance, Elsevier, vol. 35(7), pages 1824-1842, July.
    10. Bouteska, Ahmed & Sharif, Taimur & Abedin, Mohammad Zoynul, 2023. "COVID-19 and stock returns: Evidence from the Markov switching dependence approach," Research in International Business and Finance, Elsevier, vol. 64(C).
    11. Dupraz, Stéphane & Nakamura, Emi & Steinsson, Jón, 2025. "A plucking model of business cycles," Journal of Monetary Economics, Elsevier, vol. 152(C).
    12. Javier Ojea Ferreiro, 2018. "Contagion spillovers between sovereign and financial European sector from a Delta CoVaR approach," Documentos de Trabajo del ICAE 2018-12, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    13. Xun Lu & Kin Lai & Liang Liang, 2014. "Portfolio value-at-risk estimation in energy futures markets with time-varying copula-GARCH model," Annals of Operations Research, Springer, vol. 219(1), pages 333-357, August.
    14. Rehman, Mobeen Ur, 2020. "Do bitcoin and precious metals do any good together? An extreme dependence and risk spillover analysis," Resources Policy, Elsevier, vol. 68(C).
    15. Juan C. Reboredo & Andrea Ugolini & Yifei Chen, 2019. "Interdependence Between Renewable-Energy and Low-Carbon Stock Prices," Energies, MDPI, vol. 12(23), pages 1-14, November.
    16. Bernardi, Mauro & Catania, Leopoldo, 2018. "Portfolio optimisation under flexible dynamic dependence modelling," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 1-18.
    17. Lorán Chollete & Andréas Heinen & Alfonso Valdesogo, 2009. "Modeling International Financial Returns with a Multivariate Regime-switching Copula," Journal of Financial Econometrics, Oxford University Press, vol. 7(4), pages 437-480, Fall.
    18. Wen, Xiaoqian & Bouri, Elie & Roubaud, David, 2017. "Can energy commodity futures add to the value of carbon assets?," Economic Modelling, Elsevier, vol. 62(C), pages 194-206.
    19. Henry Penikas & Varvara Simakova, 2009. "Interest Rate Risk Management Based on Copula-GARCH Models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 13(1), pages 3-36.
    20. Andrey Polbin & Sergey Sinelnikov-Murylev, 2024. "Developing and impulse response matching estimation of the DSGE model for the Russian economy," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 73, pages 5-34.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

    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:bkr:journl:v:82:y:2023:i:3:p:87-109. 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: Olga Kuvshinova (email available below). General contact details of provider: https://edirc.repec.org/data/cbrgvru.html .

    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.