IDEAS home Printed from https://ideas.repec.org/a/vrs/foeste/v23y2023i2p1-23n20.html
   My bibliography  Save this article

TVP-VAR Frequency Connectedness Between the Foreign Exchange Rates of Non-Euro Area Member Countries

Author

Listed:
  • Akbulut Nesrin

    (Alanya Alaaddin Keykubat University, Turkey Department of Economics)

  • Ari Yakup

    (Alanya Alaaddin Keykubat University, Turkey Department of Economics)

Abstract

Research background The main purpose of monetary integration between EU countries is to eliminate excessive fluctuations in exchange rates. High volatility in exchange rates can cause various negative economic and financial effects, especially during periods of economic shocks. In addition, estimating the volatility between currencies and their interactions is of great importance for effective portfolio management. Purpose The objective of this research is to scrutinize the transmission of volatility between the currencies of those European Union nations that do not participate in the EURO area, focusing on the exchange rate parity of the US Dollar with seven non-EURO zone currencies. Research methodology Daily volatility in exchange rates was calculated using the Garman-Klass-Yang-Zhang (GK-YZ) method. To investigate the connectedness between these volatilities, we used the Time-Varying Parameter Vector Autoregression (TVP-VAR) frequency connectedness approach. Results The Average Total Connectedness Index exhibits a significant degree of connectedness of approximately 71.84%. The Net Total Directional Connectedness Index indicates that the CZK, DKK and RON exchange rates are net beneficiaries in aggregate and in a longer term perspective, whereas the DKK, HUF and PLN exchange rates are net beneficiaries in a shorter term horizon. In the context of major global events such as the onset of the COVID-19 outbreak in March 2020 and the start of the Russia-Ukraine conflict in February 2022, it could be observed that the dynamic Total Connectedness Index exhibited a substantial increase, both overall and from a long-term perspective, corroborating theoretical expectations. According to the Net Pairwise Directional Connectedness index, the highest bilateral connectedness overall and in the short run was between DKK and RON, while in the long run between BGN and DKK. Novelty Examining the connectedness of currencies is of great importance for investors doing business with foreign currency, international cooperation and policies, risk management and portfolio management. Determining the connectedness in different frequency (short and long-term) ranges provides important information for hedging risk. In addition, the bilateral connectedness between currencies is a guide for effective portfolio diversification.

Suggested Citation

  • Akbulut Nesrin & Ari Yakup, 2023. "TVP-VAR Frequency Connectedness Between the Foreign Exchange Rates of Non-Euro Area Member Countries," Folia Oeconomica Stetinensia, Sciendo, vol. 23(2), pages 1-23, December.
  • Handle: RePEc:vrs:foeste:v:23:y:2023:i:2:p:1-23:n:20
    DOI: 10.2478/foli-2023-0016
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/foli-2023-0016
    Download Restriction: no

    File URL: https://libkey.io/10.2478/foli-2023-0016?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Francis X. Diebold & Kamil Yilmaz, 2009. "Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets," Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.
    2. Garman, Mark B & Klass, Michael J, 1980. "On the Estimation of Security Price Volatilities from Historical Data," The Journal of Business, University of Chicago Press, vol. 53(1), pages 67-78, January.
    3. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    4. Diebold, Francis X. & Yilmaz, Kamil, 2015. "Financial and Macroeconomic Connectedness: A Network Approach to Measurement and Monitoring," OUP Catalogue, Oxford University Press, number 9780199338306.
    5. Koop, Gary & Korobilis, Dimitris, 2013. "Large time-varying parameter VARs," Journal of Econometrics, Elsevier, vol. 177(2), pages 185-198.
    6. Baillie, Richard T. & Bollerslev, Tim, 1990. "A multivariate generalized ARCH approach to modeling risk premia in forward foreign exchange rate markets," Journal of International Money and Finance, Elsevier, vol. 9(3), pages 309-324, September.
    7. Gabauer, David, 2021. "Dynamic measures of asymmetric & pairwise connectedness within an optimal currency area: Evidence from the ERM I system," Journal of Multinational Financial Management, Elsevier, vol. 60(C).
    8. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    9. Simon Sosvilla-Rivero & Fernando Fernandez-Rodriguez & Oscar Bajo-Rubio, 1999. "Exchange rate volatility in the EMS before and after the fall," Applied Economics Letters, Taylor & Francis Journals, vol. 6(11), pages 717-722.
    10. Bouri, Elie & Lucey, Brian & Saeed, Tareq & Vo, Xuan Vinh, 2020. "Extreme spillovers across Asian-Pacific currencies: A quantile-based analysis," International Review of Financial Analysis, Elsevier, vol. 72(C).
    11. Greenwood-Nimmo, Matthew & Nguyen, Viet Hoang & Rafferty, Barry, 2016. "Risk and return spillovers among the G10 currencies," Journal of Financial Markets, Elsevier, vol. 31(C), pages 43-62.
    12. Hong, Yongmiao, 2001. "A test for volatility spillover with application to exchange rates," Journal of Econometrics, Elsevier, vol. 103(1-2), pages 183-224, July.
    13. Wan, Yang & He, Shi, 2021. "Dynamic connectedness of currencies in G7 countries: A Bayesian time-varying approach," Finance Research Letters, Elsevier, vol. 41(C).
    14. John Cairns & Corrinne Ho & Robert McCauley, 2007. "Exchange rates and global volatility: implications for Asia-Pacific currencies," BIS Quarterly Review, Bank for International Settlements, March.
    15. Ioannis Chatziantoniou & David Gabauer & Rangan Gupta, 2021. "Integration and Risk Transmission in the Market for Crude Oil: A Time-Varying Parameter Frequency Connectedness Approach," Working Papers 202147, University of Pretoria, Department of Economics.
    16. Nikolaos Antonakakis & Ioannis Chatziantoniou & David Gabauer, 2021. "The impact of Euro through time: Exchange rate dynamics under different regimes," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1375-1408, January.
    17. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    18. Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," The Journal of Business, University of Chicago Press, vol. 53(1), pages 61-65, January.
    19. Wen, Tiange & Wang, Gang-Jin, 2020. "Volatility connectedness in global foreign exchange markets," Journal of Multinational Financial Management, Elsevier, vol. 54(C).
    20. repec:iae:iaewps:wp2016n4 is not listed on IDEAS
    21. Huang, Jionghao & Chen, Baifan & Xu, Yushi & Xia, Xiaohua, 2023. "Time-frequency volatility transmission among energy commodities and financial markets during the COVID-19 pandemic: A Novel TVP-VAR frequency connectedness approach," Finance Research Letters, Elsevier, vol. 53(C).
    22. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    23. Thomas J. Fisher & Colin M. Gallagher, 2012. "New Weighted Portmanteau Statistics for Time Series Goodness of Fit Testing," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 777-787, June.
    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. Abakah, Emmanuel Joel Aikins & Brahim, Mariem & Carlotti, Jean-Etienne & Tiwari, Aviral Kumar & Mensi, Walid, 2024. "Extreme downside risk connectedness and portfolio hedging among the G10 currencies," International Economics, Elsevier, vol. 178(C).
    2. Stenfors, Alexis & Chatziantoniou, Ioannis & Gabauer, David, 2022. "Independent policy, dependent outcomes: A game of cross-country dominoes across European yield curves," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    3. Cagli, Efe Caglar, 2023. "The volatility spillover between battery metals and future mobility stocks: Evidence from the time-varying frequency connectedness approach," Resources Policy, Elsevier, vol. 86(PA).
    4. Juncal Cunado & David Gabauer & Rangan Gupta & Chien-Chiang Lee, 2022. "On the Propagation Mechanism of International Real Interest Rate Spillovers: Evidence from More than 200 Years of Data," Working Papers 202212, University of Pretoria, Department of Economics.
    5. Balcilar, Mehmet & Gabauer, David & Umar, Zaghum, 2021. "Crude Oil futures contracts and commodity markets: New evidence from a TVP-VAR extended joint connectedness approach," Resources Policy, Elsevier, vol. 73(C).
    6. Elie Bouri & David Gabauer & Rangan Gupta & Harald Kinateder, 2023. "Geopolitical Risk and Inflation Spillovers across European and North American Economies," Working Papers 202304, University of Pretoria, Department of Economics.
    7. Nikolaos Antonakakis & Ioannis Chatziantoniou & David Gabauer, 2020. "Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions," JRFM, MDPI, vol. 13(4), pages 1-23, April.
    8. Chatziantoniou, Ioannis & Gabauer, David & Stenfors, Alexis, 2020. "From CIP-deviations to a market for risk premia: A dynamic investigation of cross-currency basis swaps," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 69(C).
    9. Lu, Man & Chang, Bisharat Hussain & Salman, Asma & Razzaq, Muthanna G. Abdul & Uddin, Mohammed Ahmar, 2023. "Time varying connectedness between foreign exchange markets and crude oil futures prices," Resources Policy, Elsevier, vol. 86(PB).
    10. Balli, Faruk & Balli, Hatice Ozer & Dang, Tam Hoang Nhat & Gabauer, David, 2023. "Contemporaneous and lagged R2 decomposed connectedness approach: New evidence from the energy futures market," Finance Research Letters, Elsevier, vol. 57(C).
    11. Ioannis Chatziantoniou & David Gabauer & Hardik A. Marfatia, 2022. "Dynamic connectedness and spillovers across sectors: Evidence from the Indian stock market," Scottish Journal of Political Economy, Scottish Economic Society, vol. 69(3), pages 283-300, July.
    12. Chuliá, Helena & Fernández, Julián & Uribe, Jorge M., 2018. "Currency downside risk, liquidity, and financial stability," Journal of International Money and Finance, Elsevier, vol. 89(C), pages 83-102.
    13. Timo Bettendorf & Reinhold Heinlein, 2023. "Connectedness between G10 currencies: Searching for the causal structure," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 3938-3959, October.
    14. Chatziantoniou, Ioannis & Gabauer, David & Gupta, Rangan, 2023. "Integration and risk transmission in the market for crude oil: New evidence from a time-varying parameter frequency connectedness approach," Resources Policy, Elsevier, vol. 84(C).
    15. Bouri, Elie & Cepni, Oguzhan & Gabauer, David & Gupta, Rangan, 2021. "Return connectedness across asset classes around the COVID-19 outbreak," International Review of Financial Analysis, Elsevier, vol. 73(C).
    16. Tiwari, Aviral Kumar & Aikins Abakah, Emmanuel Joel & Gabauer, David & Dwumfour, Richard Adjei, 2022. "Dynamic spillover effects among green bond, renewable energy stocks and carbon markets during COVID-19 pandemic: Implications for hedging and investments strategies," Global Finance Journal, Elsevier, vol. 51(C).
    17. Chatziantoniou, Ioannis & Gabauer, David, 2021. "EMU risk-synchronisation and financial fragility through the prism of dynamic connectedness," The Quarterly Review of Economics and Finance, Elsevier, vol. 79(C), pages 1-14.
    18. Guangxi Cao & Fei Xie, 2024. "Extreme risk spillovers across energy and carbon markets: Evidence from the quantile extended joint connectedness approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2155-2175, April.
    19. Chatziantoniou, Ioannis & Elsayed, Ahmed H. & Gabauer, David & Gozgor, Giray, 2023. "Oil price shocks and exchange rate dynamics: Evidence from decomposed and partial connectedness measures for oil importing and exporting economies," Energy Economics, Elsevier, vol. 120(C).
    20. Cocca, Teodoro & Gabauer, David & Pomberger, Stefan, 2024. "Clean energy market connectedness and investment strategies: New evidence from DCC-GARCH R2 decomposed connectedness measures," Energy Economics, Elsevier, vol. 136(C).

    More about this item

    Keywords

    Garman-Klass-Yang-Zhang; FOREX markets; Non-Euro Area; Pairwise Connectedness Index; TVP-VAR Frequency Connectedness; Volatility transmission;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G00 - Financial Economics - - General - - - General

    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:vrs:foeste:v:23:y:2023:i:2:p:1-23:n:20. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.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.