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ANALYSIS OF US DOLLAR/TURKISH LIRA EXCHANGE RATE BY AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY MODELS: THE CASE OF TURKEY

Year 2018, Issue: 20, 151 - 168, 10.01.2018
https://doi.org/10.18092/ulikidince.338893

Abstract

Exchange rates, which have attracted much attention throughout the world, are an important financial
problem. In this study, we investigate the performance of generalized autoregressive conditional
heteroscedasticity models by employing GARCH, EGARCH and TARCH models using daily data over the period
04.01.2010 to 17.03.2017 in terms of modelling the daily changes in exchange rate. All results obtained from
the models show that volatility was persistent. In addition, the findings of AR(2)-EGARCH(2,2,2) model verify
that there is existence of statistically significant asymmetric effects. The results from all asymmetry models
emphasize that the hypothesis of leverage effect cannot be rejected because the effects of negative and
positive shocks have not same impact on volatility. As a result, the findings of this study provide relevant
information and benchmark for policy makers and investors in decision making to comprehend investment
strategies and enhance exchange rate stability in economy. 

References

  • Akgül, I., & Sayyan, H. (2008). Modelling and forecasting long memory in exchange rate volatility vs. stable and integrated GARCH models. Applied Financial Economics, 18(6), 463-483.
  • Balaban, E. (2004). Forecasting exchange rate volatility. Working Paper. URL: http://ssrn.com/abstract=494482.
  • Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics. 31(3): 307-327.
  • Bollerslev, T., Chou, R. Y., & Kroner, K. F. (1992). ARCH modeling in finance: A review of the theory and empirical evidence. Journal of econometrics, 52(1-2), 5-59.
  • Chuang, I. Y., Lu, J. R., & Lee, P. H. (2007). Forecasting volatility in the financial markets: a comparison of alternative distributional assumptions. Applied Financial Economics, 17(13), 1051-1060.
  • Cont, R. (2001). Empirical Properties of Asset Returns: Stylized Facts and Statistical Issues. Quantitative Finance. 1(2): 223-236.
  • Emeç, H., & Özdemir, M.O. (2104). Türkiye’de Döviz Kuru Oynaklığının Otoregresif Koşullu Değişen Varyans Modelleri ile İncelenmesi. Finans Politik & Ekonomik Yorumlar, 51(596), 85-100.
  • Engle, R.F. (1982). Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica. 50(4): 987-1007.
  • Engle, R. F., & Patton, A. J. (2001). What good is a volatility model. Quantitative finance, 1(2), 237-245.
  • Gülay, E., & Emeç, H. (2013). Farklı Hisse senedi Piyasalarında İşlem Gören Hisse senedi getirilerinin Oynaklığının Tahminlenesi ve Oynaklık Modellerinin Öngrümleme Performanslarının Karşılaştırılası. Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Doktora tezi.
  • Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The journal of finance, 48(5), 1779-1801.
  • Hansen, P. R., & Lunde, A. (2001). A comparison of volatility models: Does anything beat a GARCH (1, 1). Unpublished manuscript. Department of Economics, Brown University.
  • Hansen, P. R., & Lunde, A. (2005). A forecast comparison of volatility models: does anything beat a GARCH (1, 1)?. Journal of applied econometrics, 20(7), 873-889.
  • Karuthedath, S. K., & Shanmugasundaram, G. (2012). Foreign exchange rate volatility of Indian Rupee/US Dollar. XI Capital Markets Conference. Indian Institute of Capital Markets.
  • Liu, H. C., Lee, Y. H., & Lee, M. C. (2009). Forecasting China stock markets volatility via GARCH models under skewed-GED distribution. Journal of money, Investment and Banking, 7(1).
  • Longmore, R., & Robinson, W. (2004). Modelling and forecasting exchange rate dynamics: an application of asymmetric volatility models. Research Services Department, Bank of Jamaica, Working Paper no. WP2004/03.
  • Mandelbrot, B. (1963). The Variation of Certain Speculative Prices. The Journal of Business. 36(4): 394-419.
  • Nelson, D.B. (1991). Conditional Heterokedasticity in Asset Returns: A New Approach. Econometrica. 59(2): 347-370.
  • Olowe, R. A. (2009). Modelling naira/dollar exchange rate volatility: application of GARCH and assymetric models. International Review of Business Research Papers, 5(3), 377-398.
  • Poon, S. H., & Granger, C. W. (2003). Forecasting volatility in financial markets: A review. Journal of economic literature, 41(2), 478-539.
  • Sağlam, M., & Başar, M. (2016). Döviz kuru oynaklığının öngörülmesi: Türkiye örneği. Sosyal ve Ekonomik Araştırmalar Dergisi, 18(31), 23.
  • Soytaş, U., & Ünal, Ö. S. (2010). Türkiye Döviz Piyasalarında Oynaklığın Öngörülmesi ve Risk Yönetimi Kapsamında Değerlendirilmesi. Yönetim ve Ekonomi: Celal Bayar Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 17(1), 121-145.
  • Taylor, S. (1986). Modelling Financial Time Series. Singapore: World Scientific Publishing Co. Pte. Ltd.
  • Uysal, D., & Özşahin, Ş. (2012). Reel Efektif Döviz Kuru Endeksi Volatilitesinin ARCH ve GARCH Modelleri ile Tahmini. Anadolu Üniversitesi Sosyal Bililer Dergisi, 12(1), 13-20.
  • Wilhelmsson, A. (2006). GARCH forecasting performance under different distribution assumptions. Journal of Forecasting, 25(8), 561-578.
  • Zakoian, J. M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and control, 18(5), 931-955.
  • Zivot, E. (2009). Practical issues in the analysis of univariate GARCH models. In Handbook of financial time series (pp. 113-155). Springer Berlin Heidelberg.
  • http://www.aomori-u.ac.jp/staff/midori/ProbDistr/t-e.pdf
  • http://www.pearsonhighered.com/assets/hip/us/hip_us_pearsonhighered/samplechapter/0205739873.pdf
  • www.investing.com

ABD DOLARI/TÜRK LİRASI DÖVİZ KURUNUN OTOREGRESİF KOŞULLU DEĞİŞEN VARYANS MODELLERİ İLE İNCELENMESİ: TÜRKİYE ÖRNEĞİ

Year 2018, Issue: 20, 151 - 168, 10.01.2018
https://doi.org/10.18092/ulikidince.338893

Abstract

Döviz kurları dünya çapında ilgi gören önemli bir finansal problemdir. Bu çalışmada, döviz kurunda meydana
gelen günlük değişimlerin modellenmesinde genelleştirilmiş otoregresif koşullu varyans modellerinin
performansı, GARCH, EGARCH ve TARCH yöntemleri kullanılarak günlük veriler üzerinden 04.01.2010 ve
17.03.2017 dönemi için incelenmiştir. Tüm modellerden elde edilen sonuçlar, oynaklığın kalıcı olduğunu
göstermektedir. Aynı zamanda, AR(2)-EGARCH(2,2,2) modelinden elde edilen sonuçlar istatistiksel olarak
anlamlı asimetrik etkilerin varlığını doğrulamaktadır. Asimetrik modellerden, EGARCH ve TARCH gibi, elde
edilen sonuçlar kaldıraç etkisi hipotezini reddedememektedir. Bu durum oynaklık üzerinde negatif ve pozitif
şokların etkisinin aynı olmadığını göstermektedir. Sonuç olarak, çalışmadan elde edilen sonuçların gerek
yatırımcılara gerekse karar alıcılara ülke ekonomisindeki döviz kuru istikrarının güçlendirilmesinde ve yatırım
stratejilerinin anlaşılmasında uygun kararların alınması aşamasında yararlı bir ön bilgi ve bir referans
sağlayacağı düşünülmektedir. 

References

  • Akgül, I., & Sayyan, H. (2008). Modelling and forecasting long memory in exchange rate volatility vs. stable and integrated GARCH models. Applied Financial Economics, 18(6), 463-483.
  • Balaban, E. (2004). Forecasting exchange rate volatility. Working Paper. URL: http://ssrn.com/abstract=494482.
  • Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics. 31(3): 307-327.
  • Bollerslev, T., Chou, R. Y., & Kroner, K. F. (1992). ARCH modeling in finance: A review of the theory and empirical evidence. Journal of econometrics, 52(1-2), 5-59.
  • Chuang, I. Y., Lu, J. R., & Lee, P. H. (2007). Forecasting volatility in the financial markets: a comparison of alternative distributional assumptions. Applied Financial Economics, 17(13), 1051-1060.
  • Cont, R. (2001). Empirical Properties of Asset Returns: Stylized Facts and Statistical Issues. Quantitative Finance. 1(2): 223-236.
  • Emeç, H., & Özdemir, M.O. (2104). Türkiye’de Döviz Kuru Oynaklığının Otoregresif Koşullu Değişen Varyans Modelleri ile İncelenmesi. Finans Politik & Ekonomik Yorumlar, 51(596), 85-100.
  • Engle, R.F. (1982). Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica. 50(4): 987-1007.
  • Engle, R. F., & Patton, A. J. (2001). What good is a volatility model. Quantitative finance, 1(2), 237-245.
  • Gülay, E., & Emeç, H. (2013). Farklı Hisse senedi Piyasalarında İşlem Gören Hisse senedi getirilerinin Oynaklığının Tahminlenesi ve Oynaklık Modellerinin Öngrümleme Performanslarının Karşılaştırılası. Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Doktora tezi.
  • Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The journal of finance, 48(5), 1779-1801.
  • Hansen, P. R., & Lunde, A. (2001). A comparison of volatility models: Does anything beat a GARCH (1, 1). Unpublished manuscript. Department of Economics, Brown University.
  • Hansen, P. R., & Lunde, A. (2005). A forecast comparison of volatility models: does anything beat a GARCH (1, 1)?. Journal of applied econometrics, 20(7), 873-889.
  • Karuthedath, S. K., & Shanmugasundaram, G. (2012). Foreign exchange rate volatility of Indian Rupee/US Dollar. XI Capital Markets Conference. Indian Institute of Capital Markets.
  • Liu, H. C., Lee, Y. H., & Lee, M. C. (2009). Forecasting China stock markets volatility via GARCH models under skewed-GED distribution. Journal of money, Investment and Banking, 7(1).
  • Longmore, R., & Robinson, W. (2004). Modelling and forecasting exchange rate dynamics: an application of asymmetric volatility models. Research Services Department, Bank of Jamaica, Working Paper no. WP2004/03.
  • Mandelbrot, B. (1963). The Variation of Certain Speculative Prices. The Journal of Business. 36(4): 394-419.
  • Nelson, D.B. (1991). Conditional Heterokedasticity in Asset Returns: A New Approach. Econometrica. 59(2): 347-370.
  • Olowe, R. A. (2009). Modelling naira/dollar exchange rate volatility: application of GARCH and assymetric models. International Review of Business Research Papers, 5(3), 377-398.
  • Poon, S. H., & Granger, C. W. (2003). Forecasting volatility in financial markets: A review. Journal of economic literature, 41(2), 478-539.
  • Sağlam, M., & Başar, M. (2016). Döviz kuru oynaklığının öngörülmesi: Türkiye örneği. Sosyal ve Ekonomik Araştırmalar Dergisi, 18(31), 23.
  • Soytaş, U., & Ünal, Ö. S. (2010). Türkiye Döviz Piyasalarında Oynaklığın Öngörülmesi ve Risk Yönetimi Kapsamında Değerlendirilmesi. Yönetim ve Ekonomi: Celal Bayar Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 17(1), 121-145.
  • Taylor, S. (1986). Modelling Financial Time Series. Singapore: World Scientific Publishing Co. Pte. Ltd.
  • Uysal, D., & Özşahin, Ş. (2012). Reel Efektif Döviz Kuru Endeksi Volatilitesinin ARCH ve GARCH Modelleri ile Tahmini. Anadolu Üniversitesi Sosyal Bililer Dergisi, 12(1), 13-20.
  • Wilhelmsson, A. (2006). GARCH forecasting performance under different distribution assumptions. Journal of Forecasting, 25(8), 561-578.
  • Zakoian, J. M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and control, 18(5), 931-955.
  • Zivot, E. (2009). Practical issues in the analysis of univariate GARCH models. In Handbook of financial time series (pp. 113-155). Springer Berlin Heidelberg.
  • http://www.aomori-u.ac.jp/staff/midori/ProbDistr/t-e.pdf
  • http://www.pearsonhighered.com/assets/hip/us/hip_us_pearsonhighered/samplechapter/0205739873.pdf
  • www.investing.com
There are 30 citations in total.

Details

Journal Section Articles
Authors

Funda İşçioğlu

Emrah Gülay

Publication Date January 10, 2018
Published in Issue Year 2018 Issue: 20

Cite

APA İşçioğlu, F., & Gülay, E. (2018). ABD DOLARI/TÜRK LİRASI DÖVİZ KURUNUN OTOREGRESİF KOŞULLU DEĞİŞEN VARYANS MODELLERİ İLE İNCELENMESİ: TÜRKİYE ÖRNEĞİ. Uluslararası İktisadi Ve İdari İncelemeler Dergisi(20), 151-168. https://doi.org/10.18092/ulikidince.338893

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