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Estimating time-varying conditional correlations between stock and foreign exchange markets

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  • Tastan, Hüseyin

Abstract

This study explores the dynamic interaction between stock market returns and changes in nominal exchange rates. Many financial variables are known to exhibit fat tails and autoregressive variance structure. It is well-known that unconditional covariance and correlation coefficients also vary significantly over time and multivariate generalized autoregressive model (MGARCH) is able to capture the time-varying variance-covariance matrix for stock market returns and changes in exchange rates. The model is applied to daily Euro-Dollar exchange rates and two stock market indexes from the US economy: Dow-Jones Industrial Average Index and S&P500 Index. The news impact surfaces are also drawn based on the model estimates to see the effects of idiosyncratic shocks in respective markets.

Suggested Citation

  • Tastan, Hüseyin, 2006. "Estimating time-varying conditional correlations between stock and foreign exchange markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 360(2), pages 445-458.
    • Handle: RePEc:eee:phsmap:v:360:y:2006:i:2:p:445-458
    DOI: 10.1016/j.physa.2005.06.062
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    4. Daniel Kohler, 2007. "Carry Trades: Betting Against Safe Haven," University of St. Gallen Department of Economics working paper series 2007 2007-12, Department of Economics, University of St. Gallen.
    5. Kang, Sang Hoon & Cheong, Chongcheul & Yoon, Seong-Min, 2011. "Structural changes and volatility transmission in crude oil markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4317-4324.
    6. Selmi, Refk & Hammoudeh, Shawkat & Kasmaoui, Kamal & Sousa, Ricardo M. & Errami, Youssef, 2022. "The dual shocks of the COVID-19 and the oil price collapse: A spark or a setback for the circular economy?," Energy Economics, Elsevier, vol. 109(C).
    7. Nobi, Ashadun & Lee, Jae Woo, 2016. "State and group dynamics of world stock market by principal component analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 85-94.
    8. Fahim Afzal & Pan Haiying & Farman Afzal & Asif Mahmood & Amir Ikram, 2021. "Value-at-Risk Analysis for Measuring Stochastic Volatility of Stock Returns: Using GARCH-Based Dynamic Conditional Correlation Model," SAGE Open, , vol. 11(1), pages 21582440211, March.
    9. Abounoori, Esmaiel & Tour, Mansour, 2019. "Stock market interactions among Iran, USA, Turkey, and UAE," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 297-305.
    10. Wang, Yi-Chiuan & Wu, Jyh-Lin & Lai, Yi-Hao, 2013. "A revisit to the dependence structure between the stock and foreign exchange markets: A dependence-switching copula approach," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1706-1719.
    11. Kang, Sang Hoon & Cheong, Chongcheul & Yoon, Seong-Min, 2013. "Intraday volatility spillovers between spot and futures indices: Evidence from the Korean stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(8), pages 1795-1802.
    12. Sermpinis, Georgios & Stasinakis, Charalampos & Dunis, Christian, 2014. "Stochastic and genetic neural network combinations in trading and hybrid time-varying leverage effects," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 30(C), pages 21-54.
    13. Christos Kollias & Nikolaos Mylonidis & Suzanna-Maria Paleologou, 2012. "The nexus between exchange rates and stock markets: evidence from the euro-dollar rate and composite European stock indices using rolling analysis," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 36(1), pages 136-147, January.
    14. Michael C. Munnix & Rudi Schafer, 2011. "A Copula Approach on the Dynamics of Statistical Dependencies in the US Stock Market," Papers 1102.1099, arXiv.org, revised Mar 2011.
    15. Aviral Kumar Tiwari & Sangram Keshari Jena & Satish Kumar & Erik Hille, 2022. "Is oil price risk systemic to sectoral equity markets of an oil importing country? Evidence from a dependence-switching copula delta CoVaR approach," Annals of Operations Research, Springer, vol. 315(1), pages 429-461, August.
    16. Khalifa, Ahmed & Caporin, Massimiliano & Hammoudeh, Shawkat, 2015. "Spillovers between energy and FX markets: The importance of asymmetry, uncertainty and business cycle," Energy Policy, Elsevier, vol. 87(C), pages 72-82.
    17. Van Cauwenberge Annelies & Vancauteren Mark & Braekers Roel & Vandemaele Sigrid, 2022. "The degree of international trade and exchange rate exposure—Firm‐level evidence from two small open economies," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 3832-3850, October.
    18. Mensah, Jones Odei & Premaratne, Gamini, 2014. "Dependence patterns among Banking Sectors in Asia: A Copula Approach," MPRA Paper 60119, University Library of Munich, Germany.
    19. Münnix, Michael C. & Schäfer, Rudi, 2011. "A copula approach on the dynamics of statistical dependencies in the US stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4251-4259.
    20. Kumar, Satish & Tiwari, Aviral Kumar & Chauhan, Yogesh & Ji, Qiang, 2019. "Dependence structure between the BRICS foreign exchange and stock markets using the dependence-switching copula approach," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 273-284.
    21. Su, Jung-Bin, 2015. "Value-at-risk estimates of the stock indices in developed and emerging markets including the spillover effects of currency market," Economic Modelling, Elsevier, vol. 46(C), pages 204-224.

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