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EXCHANGE RATE VOLATILITY TRANSITION WITH MULTIVARIATE GARCH MODEL

Year 2023, Volume: 25 Issue: 4, 1647 - 1662, 20.12.2023
https://doi.org/10.16953/deusosbil.1366905

Abstract

The variability in the price of a financial asset is called volatility and is often measured with a standard deviation. In case of high uncertainty, volatility increases. The effect of a shock in one market on returns and volatility in other markets is expressed as a volatility transmission. The volatility transmission in international financial markets gained importance with the effects of the 2008 global crisis. The aim of this study is to reveal the volatility transmission between the exchange rates of Belarus, Armenia, Kazakhstan, Kyrgyzstan, and Russia which constitute the Eurasian Economic Union as of January 1, 2015. The return series obtained over the daily closing prices of the exchange rate between 31.12.2018 and 30.06.2023 of the mentioned countries were analyzed with the MGARCH method, and the source countries of the news effect of the return volatility and the source countries of the volatility transmission were determined.

References

  • Ağır O. & Ağır, Ö. (2017). Avrupa birliği ve avrasya ekonomik birliği kuruluş süreçlerinin karşılaştırılması, Türkiye Sosyal Araştırmalar Dergisi, 21 (1): 103-128.
  • Baharçiçek, A. (1996). Yeni dünya düzeni: barış ve işbirliği mi, çatışma ve düzensizlik mi? Bilig Türk Dünyası Sosyal Bilimler Dergisi, 1: 101-105.
  • Bahsi-Koçer F. Ş. & Gökten, K. (2021). Avrasya Ekonomik Birliği: Oluşum, potansiyel ve sınırlılıklar, Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14 (4): 1468-1485.
  • Batmaz, T. (2021). Avrasya ekonomik birliği’nin üye ülke ekonomileri üzerine oluşturduğu etkiler-beklentiler (2010- 2019) , Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14 (4): 1529-1543.
  • Bauwens, L. & S. Laurent. (2004). “A new class of multivariate skew densities, with application to GARCH Models”, http://www.core.ucl.ac.be/econometrics/Bauwens/papers/2002-20-JBESfinal.pdf, (03.01.2011).
  • Bauwens, L., Laurent, S. & Rombouts, J.V. (2006). Multivariate GARCH models: a survey”, Journal of Applied Econometrics, 21 (1): 79-109.
  • Bekiros, S. & Georgoutsos, D. (2006). Estimating the correlation of international equity markets with multivariate extreme and garch models. CeNDEF Working Papers 06-17, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
  • Bera, A.K., Garcia, P. & Roh, J.S. (1997). Estimation of time-varying hedge ratios for corn and soybeans: BGARCH and random coefficient approaches, The Indian Journal of Statistics, 59 (3): 346-368.
  • Billio, M., Caporin, M., & Gobbo, M. (2006). Flexible dynamic conditional correlation multivariate garch models for asset allocation. Applied Financial Economics Letters, 2, 123–130.
  • Bollersev, T. (1986). Generalized autoregressive conditional heteroscedasticity, Journal of Econometrics, 32: 307-327.
  • Bollerslev, T. (1990). Modeling the coherence in short run nominal exchange rates: a multivariate generalized arch approach, Review of Economics and Statistics, 72, p.498-505.
  • Bollerslev, T., Engle, R.F. & Wooldridge, (1988). A Capital asset pricing model with time-varying covariances, The Journal of Political Economy, 96, p.116-131.
  • Brooks, C., S. (2008). Introductory Econometrics for Finance. Cambridge University Press, 2nd Edition.
  • Brooks, C., S. Burke and G. Persand (2003). Multivariate GARCH Models: Software Choice and Estimatio Issues. ISMA Centre Discussion Papers in Finance, 07.
  • Çiçek, M. & F. Öztürk (2007). Yabancı hisse senedi yatırımcıları Türkiye’de döviz kuru volatilitesini şiddetlendiriyor mu? Ankara Üniversitesi Siyasal Bilgiler Fakültesi Dergisi, 62 (4): 83-106.
  • DEİK, (2022). Belarus Bilgi Notu. İstanbul
  • Doroodian, K. & Caporale, T. (2000). Currency risk and the safe-haven hypothesis, Atlantic Economic Journal, International Atlantic Economic Society, 28 (2): 186-195.
  • Engle, R. & Colacito, R. (2006). Testing and valuing dynamic correlations for asset allocation, Journal of Business & Economic Statistics, American Statistical Association, 24, 238-253.
  • Engle, R. F. (2001). “GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics”, Journal of Economic Perspectives, 15 (4): p.157-168.
  • Engle, R. F. and Sheppard, K. (2001). Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH, NBER Working Paper Series, No: 8554.
  • Engle, R. F. (1982). “Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation”, Econometrica, 55 (2): 987-1007.
  • Engle, R. F. & Kroner, (1995). “Multivariate simultaneous generalized ARCH”, Econometric Theory, 11 (1): 122-150.
  • Erdoğan, O. & Schmidbauer, H. (1997). Yatırımcıların iki finansal piyasa arasındaki tercihi: koşullu korelasyon yaklaşımı, İMKB Dergisi, 8 (30): p.1-19.
  • Hafner, C. & Herwartz, H. (1998). Volatility impulse response functions for multivariate GARCH models, CORE Discussion Papers Université catholique de Louvain, Center for Operations Research and Econometrics, 1998047.
  • Hamilton, J.D. (1994). Time Series analysis. princeton university press, princeton. https://doi.org/10.1515/9780691218632
  • Hamzaoğlu, H. (2021). Avrasya ekonomik birliği’nin tarihsel gelişimi, Sosyal, Beşeri ve İdari Bilimler Dergisi, 3 (6): 463–473.
  • Hooy C. W. & Goh K. L. (2010). “Exposure to the World and Trading-Bloc Risks: A Multivariate Capital Asset Pricing Model”, Research in International Business and Finance Elsevier, 24 (2): 206-222.
  • Karolyi, G. (1995). A Multivariate GARCH model of international transmissions of stock returns and volatility: the case of the United States and Canada, Journal of Business & Economic Statistics, 13 (1): p.11-25.
  • Kocaoğlu, A. M. (1996). Rusya'nın tarihe düşen emperyalist gölgesi, Bilig Türk Dünyası Sosyal Bilimler Dergisi, 3: 39-52.
  • Kolcu, F. & Yamak, R. (2022). Döviz kurunun mevduat dolarizasyonu üzerindeki asimetrik etkisi. İzmir İktisat Dergisi. 37 (2). 481-500. Doi: 10.24988/ije.1005229.
  • Kroner F. K. & Claessens, S. (1991). Optimal dynamic hedging portfolios and the currency composition of external debt, Journal of International Money and Finance, Elsevier, 10 (1): 131-148.
  • Laurent, S., Rombouts, J.V.K., Silvennoinen, A. & Violante, F. (2006). “Comparing and ranking covariance structures of M-GARCH volatility models”, www.cide.info/conf/papers/L2.pdf, (12.04.2010).
  • Lien, D. & Luo, X. (1994). Multiperiod hedging in the presence of conditional heteroskedasticity, Journal of Futures Markets, 13, 665-676.
  • Minović, J.Z. (2009). Modeling multivariate volatility processes: theory and evidence, Theoretical and Applied Economics, 5 (5): 21-44.
  • Mun, K. C. (2007). Volatility and correlation in international stock markets and the role of exchange rate fluctuations, Journal of International Financial Markets, Institutions & Money, 17, 25-41.
  • Ng, L. (1991). Tests of the CAPM with time-varying covariances: A Multivariate GARCH approach, Journal of Finance, 46, 1507–1521.
  • Özdemir, M.O. & Emeç, H. (2020). Tek Değişkenli GARCH Modelleri İle Türkiye’nin CDS Primi Oynaklığının Analizi. İzmir İktisat Dergisi. 35 (1). 113-122. Doi: 10.24988/ije.202035109.
  • Öztürk, Y. (2013). Avrasya birliği projesi ve Türk dış politikasına yansımaları, Çankırı Karatekin Üniversitesi Uluslararası Avrasya Strateji Dergisi, 2 (2): 223-244.
  • Park, T. H. & Switzer, L. N. (1995). Bivariate GARCH estimation of the optimal hedge ratios for stock index futures: a note, Journal of Futures Markets, 15, 61-67.
  • Pirimbayev, İ C. & Ganiyev, C. (2010). Avrasya ekonomik topluluğu: bir iktisadi işbirliği alternatifi, International Conference on Eurasian Economies 2010: 82-85.
  • Polasek, W. & L. Ren (2000). A Multivariate GARCH-M model for exchange rates in the US, Germany and Japan, Computing in Economics and Finance, http://fmwww.bc.edu/cef00/papers/paper223.pdf, (08.05.2010).
  • Rombouts, J. & Verbeek, M. (2004). “Evaluating Portfolio Value-at-Risk Using Semi-Parametric GARCH Models”, Cahiers de recherche HEC Montréal, Institut d'économie appliqué, 14 (4):1-32.
  • Schröder, M. & Schüler, M. (2003). “The systemic risk potential in european banking - evidence from bivariate GARCH Models”, ZEW Discussion Paper, No:03-11, 1-42.
  • Silvennoinen A. & Teräsvirta T. (2007), “Multivariate GARCH Models”, SSE/EFI Working Paper Series in Economics and Finance, No: 669.
  • Tai, C.-S. (2001). A multivariate GARCH in mean approach to testing uncovered interest parity: Evidence from Asia-Pacific foreign exchange markets. Quartely Review of Economics and Finance, 41: 441-460.
  • Tay, N. & Zhu, Z. (2000). “Correlations in returns and volatilities in Pacific-Rim stock markets. Open Economies Review, Springer, 11 (1): 27-47.
  • Tse, Y. & Tsui, A. (2000). A Multivariate GARCH model with time-varying correlations”, http://papers.ssrn.com/sol3/papers.cfm?abstract_id=250228, (12.05.2010).
  • Turna Y., Eşmen S. & Turna B. (2022). Türkiye' de döviz kurunun enflasyon etkisi ve fiyat yapışkanlıkları: nardl yaklaşımı. İzmir İktisat Dergisi. 37 (2). 522-535. Doi: 10.24988/ije.932967.
  • Türkiye İhracatçılar Meclisi (2019). https://tim.org.tr/tr/default (Erişim Tarihi: 10/07/2023).
  • Ural, M., Demireli, E. & Aydın, Ü. (2022). Finansal Yatırımlarda Riske Maruz Değer Analizi. Seçkin Yayıncılık, Ankara
  • Wei, C.C. (2008). Multivariate GARCH modeling analysis of unexpected usd, yen and euro-dollar to reminibi volatility spillover to stock markets”, Economics Bulletin, 3 (64): 1-15
  • Worthington, A. & Higgs, H. (2004). “Transmission of equity returns and volatility in asian developed and emerging markets: A Multivariate GARCH analysis, International Journal of Finance and Economics, 9 (1): p.71-80.
  • Yang, W., & Allen, D. E. (2004). “Multivariate GARCH hedge ratios and hedging effectiveness in australian futures markets”, Accounting and Finance, 45 (2): 301-321.

ÇOK DEĞİŞKENLİ GARCH MODELİYLE DÖVİZ KURLARINDA OYNAKLIK GEÇİŞİ

Year 2023, Volume: 25 Issue: 4, 1647 - 1662, 20.12.2023
https://doi.org/10.16953/deusosbil.1366905

Abstract

Bir finansal varlığın fiyatındaki değişkenliğe oynaklık denilmekte ve çoğunlukla standart sapma ile ölçülmektedir. Yüksek belirsizlik durumunda oynaklık artmaktadır. Bir piyasada yaşanan şokun diğer piyasalarda getiri ve oynaklık üzerindeki etkisi ise oynaklık geçişi olarak ifade edilmektedir. Uluslararası finansal piyasalarda oynaklık geçişi, 2008 yılı küresel krizinin etkileriyle birlikte önem kazanmıştır. Bu çalışmanın amacı, 1 Ocak 2015 tarihi itibarıyla Avrasya Ekonomik Birliğini oluşturan Belarus, Ermenistan, Kazakistan, Kırgızistan ve Rusya döviz kurları arasında oynaklık geçişini ortaya koymaktır. Söz konusu ülkelerin 31.12.2018 – 30.06.2023 tarihleri arasındaki döviz kuru günlük kapanış fiyatları üzerinden elde edilen getiri serileri MGARCH yöntemiyle analiz edilmiş, getiri oynaklıklarının haber etkisinin kaynak ülkeleri ile oynaklık geçişinin kaynak ülkeleri belirlenmiştir.

References

  • Ağır O. & Ağır, Ö. (2017). Avrupa birliği ve avrasya ekonomik birliği kuruluş süreçlerinin karşılaştırılması, Türkiye Sosyal Araştırmalar Dergisi, 21 (1): 103-128.
  • Baharçiçek, A. (1996). Yeni dünya düzeni: barış ve işbirliği mi, çatışma ve düzensizlik mi? Bilig Türk Dünyası Sosyal Bilimler Dergisi, 1: 101-105.
  • Bahsi-Koçer F. Ş. & Gökten, K. (2021). Avrasya Ekonomik Birliği: Oluşum, potansiyel ve sınırlılıklar, Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14 (4): 1468-1485.
  • Batmaz, T. (2021). Avrasya ekonomik birliği’nin üye ülke ekonomileri üzerine oluşturduğu etkiler-beklentiler (2010- 2019) , Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14 (4): 1529-1543.
  • Bauwens, L. & S. Laurent. (2004). “A new class of multivariate skew densities, with application to GARCH Models”, http://www.core.ucl.ac.be/econometrics/Bauwens/papers/2002-20-JBESfinal.pdf, (03.01.2011).
  • Bauwens, L., Laurent, S. & Rombouts, J.V. (2006). Multivariate GARCH models: a survey”, Journal of Applied Econometrics, 21 (1): 79-109.
  • Bekiros, S. & Georgoutsos, D. (2006). Estimating the correlation of international equity markets with multivariate extreme and garch models. CeNDEF Working Papers 06-17, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
  • Bera, A.K., Garcia, P. & Roh, J.S. (1997). Estimation of time-varying hedge ratios for corn and soybeans: BGARCH and random coefficient approaches, The Indian Journal of Statistics, 59 (3): 346-368.
  • Billio, M., Caporin, M., & Gobbo, M. (2006). Flexible dynamic conditional correlation multivariate garch models for asset allocation. Applied Financial Economics Letters, 2, 123–130.
  • Bollersev, T. (1986). Generalized autoregressive conditional heteroscedasticity, Journal of Econometrics, 32: 307-327.
  • Bollerslev, T. (1990). Modeling the coherence in short run nominal exchange rates: a multivariate generalized arch approach, Review of Economics and Statistics, 72, p.498-505.
  • Bollerslev, T., Engle, R.F. & Wooldridge, (1988). A Capital asset pricing model with time-varying covariances, The Journal of Political Economy, 96, p.116-131.
  • Brooks, C., S. (2008). Introductory Econometrics for Finance. Cambridge University Press, 2nd Edition.
  • Brooks, C., S. Burke and G. Persand (2003). Multivariate GARCH Models: Software Choice and Estimatio Issues. ISMA Centre Discussion Papers in Finance, 07.
  • Çiçek, M. & F. Öztürk (2007). Yabancı hisse senedi yatırımcıları Türkiye’de döviz kuru volatilitesini şiddetlendiriyor mu? Ankara Üniversitesi Siyasal Bilgiler Fakültesi Dergisi, 62 (4): 83-106.
  • DEİK, (2022). Belarus Bilgi Notu. İstanbul
  • Doroodian, K. & Caporale, T. (2000). Currency risk and the safe-haven hypothesis, Atlantic Economic Journal, International Atlantic Economic Society, 28 (2): 186-195.
  • Engle, R. & Colacito, R. (2006). Testing and valuing dynamic correlations for asset allocation, Journal of Business & Economic Statistics, American Statistical Association, 24, 238-253.
  • Engle, R. F. (2001). “GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics”, Journal of Economic Perspectives, 15 (4): p.157-168.
  • Engle, R. F. and Sheppard, K. (2001). Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH, NBER Working Paper Series, No: 8554.
  • Engle, R. F. (1982). “Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation”, Econometrica, 55 (2): 987-1007.
  • Engle, R. F. & Kroner, (1995). “Multivariate simultaneous generalized ARCH”, Econometric Theory, 11 (1): 122-150.
  • Erdoğan, O. & Schmidbauer, H. (1997). Yatırımcıların iki finansal piyasa arasındaki tercihi: koşullu korelasyon yaklaşımı, İMKB Dergisi, 8 (30): p.1-19.
  • Hafner, C. & Herwartz, H. (1998). Volatility impulse response functions for multivariate GARCH models, CORE Discussion Papers Université catholique de Louvain, Center for Operations Research and Econometrics, 1998047.
  • Hamilton, J.D. (1994). Time Series analysis. princeton university press, princeton. https://doi.org/10.1515/9780691218632
  • Hamzaoğlu, H. (2021). Avrasya ekonomik birliği’nin tarihsel gelişimi, Sosyal, Beşeri ve İdari Bilimler Dergisi, 3 (6): 463–473.
  • Hooy C. W. & Goh K. L. (2010). “Exposure to the World and Trading-Bloc Risks: A Multivariate Capital Asset Pricing Model”, Research in International Business and Finance Elsevier, 24 (2): 206-222.
  • Karolyi, G. (1995). A Multivariate GARCH model of international transmissions of stock returns and volatility: the case of the United States and Canada, Journal of Business & Economic Statistics, 13 (1): p.11-25.
  • Kocaoğlu, A. M. (1996). Rusya'nın tarihe düşen emperyalist gölgesi, Bilig Türk Dünyası Sosyal Bilimler Dergisi, 3: 39-52.
  • Kolcu, F. & Yamak, R. (2022). Döviz kurunun mevduat dolarizasyonu üzerindeki asimetrik etkisi. İzmir İktisat Dergisi. 37 (2). 481-500. Doi: 10.24988/ije.1005229.
  • Kroner F. K. & Claessens, S. (1991). Optimal dynamic hedging portfolios and the currency composition of external debt, Journal of International Money and Finance, Elsevier, 10 (1): 131-148.
  • Laurent, S., Rombouts, J.V.K., Silvennoinen, A. & Violante, F. (2006). “Comparing and ranking covariance structures of M-GARCH volatility models”, www.cide.info/conf/papers/L2.pdf, (12.04.2010).
  • Lien, D. & Luo, X. (1994). Multiperiod hedging in the presence of conditional heteroskedasticity, Journal of Futures Markets, 13, 665-676.
  • Minović, J.Z. (2009). Modeling multivariate volatility processes: theory and evidence, Theoretical and Applied Economics, 5 (5): 21-44.
  • Mun, K. C. (2007). Volatility and correlation in international stock markets and the role of exchange rate fluctuations, Journal of International Financial Markets, Institutions & Money, 17, 25-41.
  • Ng, L. (1991). Tests of the CAPM with time-varying covariances: A Multivariate GARCH approach, Journal of Finance, 46, 1507–1521.
  • Özdemir, M.O. & Emeç, H. (2020). Tek Değişkenli GARCH Modelleri İle Türkiye’nin CDS Primi Oynaklığının Analizi. İzmir İktisat Dergisi. 35 (1). 113-122. Doi: 10.24988/ije.202035109.
  • Öztürk, Y. (2013). Avrasya birliği projesi ve Türk dış politikasına yansımaları, Çankırı Karatekin Üniversitesi Uluslararası Avrasya Strateji Dergisi, 2 (2): 223-244.
  • Park, T. H. & Switzer, L. N. (1995). Bivariate GARCH estimation of the optimal hedge ratios for stock index futures: a note, Journal of Futures Markets, 15, 61-67.
  • Pirimbayev, İ C. & Ganiyev, C. (2010). Avrasya ekonomik topluluğu: bir iktisadi işbirliği alternatifi, International Conference on Eurasian Economies 2010: 82-85.
  • Polasek, W. & L. Ren (2000). A Multivariate GARCH-M model for exchange rates in the US, Germany and Japan, Computing in Economics and Finance, http://fmwww.bc.edu/cef00/papers/paper223.pdf, (08.05.2010).
  • Rombouts, J. & Verbeek, M. (2004). “Evaluating Portfolio Value-at-Risk Using Semi-Parametric GARCH Models”, Cahiers de recherche HEC Montréal, Institut d'économie appliqué, 14 (4):1-32.
  • Schröder, M. & Schüler, M. (2003). “The systemic risk potential in european banking - evidence from bivariate GARCH Models”, ZEW Discussion Paper, No:03-11, 1-42.
  • Silvennoinen A. & Teräsvirta T. (2007), “Multivariate GARCH Models”, SSE/EFI Working Paper Series in Economics and Finance, No: 669.
  • Tai, C.-S. (2001). A multivariate GARCH in mean approach to testing uncovered interest parity: Evidence from Asia-Pacific foreign exchange markets. Quartely Review of Economics and Finance, 41: 441-460.
  • Tay, N. & Zhu, Z. (2000). “Correlations in returns and volatilities in Pacific-Rim stock markets. Open Economies Review, Springer, 11 (1): 27-47.
  • Tse, Y. & Tsui, A. (2000). A Multivariate GARCH model with time-varying correlations”, http://papers.ssrn.com/sol3/papers.cfm?abstract_id=250228, (12.05.2010).
  • Turna Y., Eşmen S. & Turna B. (2022). Türkiye' de döviz kurunun enflasyon etkisi ve fiyat yapışkanlıkları: nardl yaklaşımı. İzmir İktisat Dergisi. 37 (2). 522-535. Doi: 10.24988/ije.932967.
  • Türkiye İhracatçılar Meclisi (2019). https://tim.org.tr/tr/default (Erişim Tarihi: 10/07/2023).
  • Ural, M., Demireli, E. & Aydın, Ü. (2022). Finansal Yatırımlarda Riske Maruz Değer Analizi. Seçkin Yayıncılık, Ankara
  • Wei, C.C. (2008). Multivariate GARCH modeling analysis of unexpected usd, yen and euro-dollar to reminibi volatility spillover to stock markets”, Economics Bulletin, 3 (64): 1-15
  • Worthington, A. & Higgs, H. (2004). “Transmission of equity returns and volatility in asian developed and emerging markets: A Multivariate GARCH analysis, International Journal of Finance and Economics, 9 (1): p.71-80.
  • Yang, W., & Allen, D. E. (2004). “Multivariate GARCH hedge ratios and hedging effectiveness in australian futures markets”, Accounting and Finance, 45 (2): 301-321.
There are 53 citations in total.

Details

Primary Language Turkish
Subjects Microeconomics (Other)
Journal Section Articles
Authors

Üzeyir Aydın 0000-0003-2777-6450

Publication Date December 20, 2023
Submission Date September 26, 2023
Published in Issue Year 2023 Volume: 25 Issue: 4

Cite

APA Aydın, Ü. (2023). ÇOK DEĞİŞKENLİ GARCH MODELİYLE DÖVİZ KURLARINDA OYNAKLIK GEÇİŞİ. Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 25(4), 1647-1662. https://doi.org/10.16953/deusosbil.1366905