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Journals / Süleyman Demirel Üniversitesi Vizyoner Dergisi / 2019 / Cilt: 10 - Sayı: 25

TESTING ASYMMETRIC SIGN AND SIZE BIAS IN EXCHANGE RATE VOLATILITY: AN INVESTIGATION ON EURO/TL EXCHANGE RATE VOLATILITY

DÖVİZ KURU OYNAKLIĞINDA ASİMETRİK İŞARET VE BOYUT YANLILIĞININ TEST EDİLMESİ: EURO/TL KUR OYNAKLIĞI ÜZERİNE BİR İNCELEME

Hakan DEMİRGİL
Sinan YILDIRIM
Zübeyde ÇİÇEK
Pages
485–494
Publication Date
2019-10-28
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to the results obtained from the asymmetric GARCH models (E-GARCH, TGARCH and APGARCH), positive shocks in the EURO/TL exchange rate have stronger effect than the negative shocks of the same magnitude.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The aim of the study is to construct the model of the volatility in exchange rates considering the asymmetric effects determined by Engel and Ng (1993) sign and size bias tests.
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Key Findings & Data:A model using monthly time series data covering the period between 1999M01-2019M05 is employed.
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Conclusion & Impact:According to the results obtained from the asymmetric GARCH models (E-GARCH, TGARCH and APGARCH), positive shocks in the EURO/TL exchange rate have stronger effect than the negative shocks of the same magnitude.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Kur Oynaklığı · Asimetri · İşaret ve Boyut Yanlılığı

Abstract

The aim of the study is to construct the model of the volatility in exchange rates considering the asymmetric effects determined by Engel and Ng (1993) sign and size bias tests. Accordingly, the volatility of the TL against EURO which is the currency of European countries having the biggest share of Turkey’s foreign trade is examined by asymmetric GARCH models. A model using monthly time series data covering the period between 1999M01-2019M05 is employed. The size and dimension of the residuals of the previous period have a significant effect on the explanation of exchange rate volatility. According to the results obtained from the asymmetric GARCH models (E-GARCH, TGARCH and APGARCH), positive shocks in the EURO/TL exchange rate have stronger effect than the negative shocks of the same magnitude. Finally, it is concluded based on forecasting error statistics that the best model for estimating the EURO/TL exchange rate volatility is TGARCH model.

Özet

Bu çalışmanın amacı Engel ve Ng (1993) nin işaret ve boyut yanlılığı testleri ile belirlenen asimetrik etkileri dikkate alarak döviz kurlarında yaşanan oynaklığın modellenmesidir. Bu doğrultuda, Türkiye’nin dış ticaretinde en büyük paya sahip Avrupa ülkeleri para birimi EURO’nun TL karşısındaki oynaklığı asimetrik GARCH modelleri ile incelenmiştir. EURO/TL döviz kuru değişkenine ait 1999M01-2019M05 dönemi aylık veriler kullanılmıştır. Ampirik sonuçlar, EURO/TL döviz kuru oynaklık serisinin asimetriye sahip olduğunu göstermektedir. Geçmiş dönem kalıntılarının boyutu ve büyüklüğü, döviz kuru oynaklığının açıklanmasında önemli bir etkiye sahiptir. Asimetrik GARCH modellerinden (E-GARCH, TGARCH ve APGARCH) elde edilen sonuçlar, EURO/TL döviz kurunda meydana gelen pozitif şokların aynı büyüklükte ki negatif şoklardan daha fazla etkiye sahip olduğuna işaret etmektedir. Son olarak, bu modeller kullanılarak elde edilen öngörü hata istatistiklerine göre EURO/TL döviz kuru oynaklığı tahmini için en iyi modelin TGARCH modeli olduğu sonucuna ulaşılmıştır.

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Cite This Article

DEMİRGİL, H., YILDIRIM, S., ÇİÇEK, Z. (2019). TESTING ASYMMETRIC SIGN AND SIZE BIAS IN EXCHANGE RATE VOLATILITY: AN INVESTIGATION ON EURO/TL EXCHANGE RATE VOLATILITY. Süleyman Demirel Üniversitesi Vizyoner Dergisi, Cilt: 10 - Sayı: 25, 485–494. https://ajindex.com/articles/163912

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References (15)

  1. Abdalla S.Z.S. (2012). “Modelling Exchange Rate Volatility Using GARCH Models: Empirical Evidence from Arab Countries”, International Journal of Economics and Finance, 4(3), 216-229.
  2. Balaban E. (2004). “Comparative Forecasting Performance of Symmetric and Asymmetric Conditional Volatility Models of An Exchange Rate”, Economics Letters, 83(1), 99-105.
  3. Barışık S. ve Demircioğlu E. (2006). “Türkiye’de Döviz Kuru Rejimi, Konvertibilete, İhracat-İthalat İlişkisi (1980-2001)”, ZKÜ Sosyal Bilimler Dergisi, 2(3), 2006, 71-84.
  4. Berument H. (2002). “Döviz Kuru Hareketleri ve Enflasyon Dinamiği: Türkiye Örneği”, Bilkent Üniversitesi Yayınları.
  5. Bollerslev, T. (1986). “Generalized Autoregressive Conditional Heteroskedasticity”. Journal of Econometrics, 31(3), 307-327.
  6. Bollerslev, T. (2007). “Glossary to ARCH (GARCH)”. Duke University and NBER. Bozkurt, H. (2009). “M-GARCH Modellerinin Karşılaştırmalı Analizi”. Kocaeli Üniversitesi Sosyal Bilimler Enstitüsü Dergisi (18)2, 126 – 145. Çağlayan E. ve Dayıoğlu T. (2009). “Döviz Kuru Getiri Volatilitesinin Koşullu Değişen Varyans Modelleri İle Öngörüsü”, Ekonometri ve İstatistik e-Dergisi, 9, 1-16.
  7. Ding, Z., Granger, C.W.J. ve Engle, R. F. (1993). “A Long Memory Property of Stock Market Returns and a New Model, Journal of Empirical Finance”, 1, 83-106.
  8. Dutta, A. (2014). “Modelling Volatility: Symemtric or Asymmetric GARCH Models?”, Jornal of Statistics: Advances in Theory and Applications, 12(2), 99-108.
  9. Dutta, A. (2018). “Forecasting Ethanol Market Volatility: New Evidence from the Corn Implied Volatility Index”, Biofuels, Bioprod. Bioref. 13, 48–54.
  10. Engle R.F. (1982). “Autoregressive Conditional Heteroscedasticity With Estimates of the Variance of United Kingdom Inflation”, Econometrica: Journal of the Econometric Society, 50(4), 987-1008.
  11. Engle, R.F. ve Kroner, K.F. (1995). “Multivariate Simultaneous Generalized ARCH”, Econometric Theory, 11(1), 122-150.
  12. Engle, R.F. ve Ng, V.K. (1993). “Measuring and Testing The Impact of News on Volatility. The Journal of Finance, 48(5), 1749-1778.
  13. Fidrmuc J. ve Horvath R. (2008). “Volatility of Exchange Rates in Selected New EU Members: Evidence from Daily Data”, Economic Systems 32(1), 103-118.
  14. Gonzalez-Rivera, G. (1998). “Smooth Transition GARCH Models”, Studies in Nonlinear Dynamics and Econometrics, 3, 61–78.
  15. Kim, S. K. (1995). “Modelling Changes in Daily $A Exchange Rates: An Application of GARCH, C.S. Forbes, P. Kofman and T.R.L. Fry (Ed.), in Proceedings of the Econometrics Confer