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Döviz Piyasalarının Etkinliği Üzerinde Uzun Hafızanın Rolü: Türk Döviz Piyasasında Ampirik Bir Araştırma

Year 2018, Volume: 12 Issue: 1, 87 - 107, 01.06.2018

Abstract

Yönü ve sebebi ne olursa olsun piyasalara ulaşan tüm bilgilerin hızlı şekilde fiyatlara yansıması geçmiş verilerden hareketle geleceği öngörmeyi ve diğer yatırımcılar karşısında anormal getiri elde etmeyi engellemektedir. Bu çalışmanın amacı ikili uzun hafıza modellerini kullanarak Türk Döviz piyasalarının zayıf formda etkin olup olmadığını ortaya koymaktır. İkili uzun hafızayı test etmek için kurulan ARFIMA-FIGARCH model sonuçları, getiri volatilitesinin uzun hafıza özelliğine sahip olduğunu göstermektedir. Araştırma sonuçlarına göre ilgili analiz dönemi için Türk Döviz piyasasının zayıf formda etkin piyasa olmadığı tespit edilmiştir. Her ne kadar tarihi verilerden faydalanarak geleceğin getiri volatilitesinin öngörülebilir olduğu, ayrıca Merkez Bankası’nın kurlara yaptığı müdahalelerin ortaya çıkardığı oynaklığın uzun dönemde sönümleneceği tespit edilmiş olsa da satın alma gücü paritesi teorisini destekler şekilde Türk Döviz piyasasının uzun dönemde dengeye gelme karakterine sahip istikrarlı bir piyasa olduğu volatilitedeki uzun hafızayı temsil eden d parametresinin katsayısından açıkça anlaşılabilmektedir

References

  • Aidoo, E. N., Saeed, B.I.I., Ababio, K.A., Nuamah, N.N., Louis, M. (2012). Analysis of Long Memory Dynamics in Exchange Rate, The Emprical Econo- mics Letters, 11(7), 745-754.
  • Alptekin, N.(2007). Long Memory Analysis of USD/TRL Exchange Rate. World Academy of Science, Engineering and Technology, 3, 298-300.
  • Baillie, R. T., Bollerslev, T., & Mikkelsen, H. O. (1996). Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity. Journal Of Eco- nometrics, 74(1), 3-30.
  • Bai, J. & Perron, P. (1998). Estimating and Testing Linear Models with Multip- le Structural Changes. Econometrica, 66, 47–78.
  • Barkoulas, J. T., Baum, C. F. (1997). Long Memory And Forecasting In Euro- yen Deposit Rates. Financial Engineering And The Japanese Markets, 4(3), 189-201.
  • Bhar, R. (2000). Testing for Long-Term Memory in Yen/Dollar Exchange Rate, Working Paper, Department of Economics and Finance, University of Wes- tern Sydney.
  • Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasti- city. Journal Of Econometrics, 31(3), 307-327.
  • Cheng, T. C. K. (2001). Long Memory Features In The Exchange Rates Of Asia-Pacific Countries, Working Paper, Department of Economics, National University of Singapore.
  • Cheung, Y. W., (1993). Long Memory in Foreign Exchange Rates, Journal of Business and Economics Statistics, 11, 93-101.
  • Cheung,Yin-Wong, Kon. S. Lai (2001).Long Memory and Nonlinear Mean Reversion in Japanese Yen-Based Real Exchange Rates. Journal of Internatio- nal Money and Finance 20, 115-132.
  • Chiang, S.M., Lee, Y.H., Su, Y.H. and Tzou, Y.P.(2011). Efficiency Test of Foreign Exchange Market for four Asian Countries. Research in International Business and Finance, 24(3), 284-294.
  • Chortareas, G., Jiang, Y. and Nankervis, J.C. (2011).The Random-Walk Be- haviour of the Euro Exchange Rate. Finance Research Letters, 8(3), 158-162.
  • Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity With Esti- mates Of The Variance Of United Kingdom Inflation. Econometrica: Journal of the Econometric Society, 987-1007.
  • Erlat, H. (2003). The Nature of Persistence in Turkish Real Exchange Rates, Emerging Markets Finance And Trade,39 (2), 70-97.
  • Geweke J. and Porter-Hudak S., (1983). The Estimation And Application Of Long Memory Time Series Models. Journal of Time Series Analysis, 4, 221– 238.
  • Granger, C. W., & Joyeux, R. (1980). An Introduction To Long‐Memory Time Series Models And Fractional Differencing. Journal of Time Series Analy- sis, 1(1), 15-29.
  • Floros, C. (2008). Long Memory in Exchange Rates: International Evidence, The International Journal of Business and Finance Research, 2(1), 31-39.
  • Han, Y.W. (2005). Long Memory Volatility Dependence, Temporal aggrega- tion and the Korean Currency Crisis: The Role of a High Frequency Korean Won (KRW)–US Dollar ($) Exchange Rate, Japan and World Economy,17(1): 97-109.
  • Hosking, J. R. (1981). Fractional Differencing. Biometrika, 68(1), 165-176.
  • Inclan, C., and Tiao, G.C. (1994). Use of Cumulative Sums of Squares for Retrospective Detection of Changes of Variance. Journal of the American Statistical Association, 89, 913–923.
  • Hurst H. (1951). Long Term Storage Capacity Of Reservoirs. Transactions of the American Society of Civil Engineers, 116, 770–799.
  • Kaya, H., Çelik, İ. (2018). Türkiye’de Satın Alma Gücü Paritesi Hipotezinin Ge- çerliliği: Uzun Hafıza Testlerinden Kanıtlar. Mehmet Akif Ersoy Üniversitesi, İİBF Dergisi. 5(2), 351-365.
  • Kumar, D. (2014). Long Memory in the Volatility of Indian Financial Market: An Empirical Analysis Based on Indian Data. diplom. de.
  • Kutlu, S., Yurttagüler, İ.M. (2014). Türkiye’de Reel Döviz Kurlarının Uzun Ha- fıza Özellikleri: Kesirli Bütünleşme Analizi. Marmara Üniversitesi İ.İ.B.F Dergi- si, XXXVI(I), 373-389.
  • Laurini, M. P. and M. S. Portugal (2003). Long Memory in The R$/US$ Exc- hange Rate: A Robust Analysis. Fınancelab Working Paper, Ibmec Sao Paulo, FLWP-03-2003.
  • Lo, A. W. (1991). Long Term Memory In Stock Market Prices. Econometrica, 59, 1279–1313.
  • Mandelbrot, B.B. (1972) Statistical Methodology For Nonperiodic Cycles: From The Covariance To R/S Analysis. Annals of Economic and Social Measu- rements, 1, 259–290.
  • Nakamura, T. and Small, M., 2007. Tests of the Random Walk Hypothe- sis for Financial Data. Physica A: Statistical Mechanics and Its Applications, 377(2):599-615.
  • Özer, M. Ve Türkyılmaz, S. (2004). Türkiye Finansal Piyasalarında Oynaklık- ların ARCH Modelleri ile Analizi. TC. Anadolu Üniversitesi, İktisadi ve İdari Bilimler Fakültesi Yayınları, No:1593, Sayı:186. Eskişehir.
  • Phillips, P. C. B., (1999). Discrete Fourier Transforms of Fractional Process, Unpublished Working Paper, No.1243, Cowles Foundation for Research in Economics, Yale University.
  • Robınson, P. M. (1995). Log-Periodogram Regression Of Time Series With Long Range Dependence. Annals of Statistics, 23, 1048-1072.
  • Robinson, P. M. and Henry, M. (1999). Long and short memory conditional heteroskedasticity in estimating the memory parameter of levels. Economet- ric Theory, 15 (03), 299-336.
  • Sansó, A., Aragó, V. and Carrion, J. L. (2004). Testing for Changes in the Unconditional Variance of Financial Time Series. Revista de Economía Finan- ciera,4, 32-53.
  • Shittu O.I and Yaya O. S. (2009). Measuring Forecast Performance of ARMA and ARFIMA Models: An Application to US Dollar/ UK pound Foreign Exchan- ge Rate. European Journal of Scientific Reaearch, 32 (2), 167-176.
  • Stengos, T., Yazgan, M.E. (2012). Persistence in Real Exchange Rate Conver- gence. Studies in Nonlinear Dynamics & Econometrics. 18(1), 1-27.
  • Tabak, M.B. and Cajueiro, D.O., 2006. Assessing Inefficiency in Euro Bilateral Exchange Rates. Physica A Statistical Mechanics and Its Applications, 367: 319-327.
  • Türkyılmaz, S. Özer, M. (2007). Türkiye’de Döviz Kuru Oynaklığının Uzun Hafıza Özelliklerinin Analizi. İktisat İşletme ve Finans İnceleme Araştırma. 22, 99-113.
  • Türkyılmaz, S., Balıbey, M. (2014). Türkiye Hisse Senedi Piyasası Getiri ve Oynaklığındaki Uzun Dönem Bağımlılık İçin Ampirik Bir Analiz. Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 16(2), 281-302.
  • Ürkmez, E. (2017).Reel Döviz Kurlarında Uzun Dönem Bağımlılık. Uluslararası Sosyal Araştırmalar Dergisi.10(49).
  • Vats, A. (2011). Long Memory in Returns and Volatility: Evidence from Fore- ign Exchange Market of Asian Countries. The International Journal of Appli- ed Economics and Finance. 5(4), 245-256.

The Role of Long Memory on the Efficiency of Foreign Exchange Markets: An Ampirical Research in the Turkish Foreign Exchange Market

Year 2018, Volume: 12 Issue: 1, 87 - 107, 01.06.2018

Abstract

The Role of Long Memory on the Efficiency of Foreign Exchange Markets: An Ampirical Research in the Turkish Foreign Exchange MarketReflecting all the information reaching the markets in the prices, regardless of this any reason and direction, prevents predicting the future with reference to historical data and obtaining abnormal return against other investors. The aim of this paper is to reveal whether the Turkish Exchange Markets are efficient in weak form by using dual long memory models. The results of ARFIMA-FIGARCH model, which was established to examine the dual long memory, show the volatility of return has a long memory property. According to results of the research, it is determined that Turkish Foreign Exchange Market is not efficient in weak form for related analysis period. Although the future volatility of return is predictiable by taking advantage of historical data, also the volatility arisen by Central Bank’s interventions to exchange rates has been detected to fade in long term, Turkish Foreign Exchange Market which is stable, has long term equilibrium character in furtherance of the purchasing power parity theory, it can be clearly understood from the coefficient of the d parameter, which represents the long memory in volatility

References

  • Aidoo, E. N., Saeed, B.I.I., Ababio, K.A., Nuamah, N.N., Louis, M. (2012). Analysis of Long Memory Dynamics in Exchange Rate, The Emprical Econo- mics Letters, 11(7), 745-754.
  • Alptekin, N.(2007). Long Memory Analysis of USD/TRL Exchange Rate. World Academy of Science, Engineering and Technology, 3, 298-300.
  • Baillie, R. T., Bollerslev, T., & Mikkelsen, H. O. (1996). Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity. Journal Of Eco- nometrics, 74(1), 3-30.
  • Bai, J. & Perron, P. (1998). Estimating and Testing Linear Models with Multip- le Structural Changes. Econometrica, 66, 47–78.
  • Barkoulas, J. T., Baum, C. F. (1997). Long Memory And Forecasting In Euro- yen Deposit Rates. Financial Engineering And The Japanese Markets, 4(3), 189-201.
  • Bhar, R. (2000). Testing for Long-Term Memory in Yen/Dollar Exchange Rate, Working Paper, Department of Economics and Finance, University of Wes- tern Sydney.
  • Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasti- city. Journal Of Econometrics, 31(3), 307-327.
  • Cheng, T. C. K. (2001). Long Memory Features In The Exchange Rates Of Asia-Pacific Countries, Working Paper, Department of Economics, National University of Singapore.
  • Cheung, Y. W., (1993). Long Memory in Foreign Exchange Rates, Journal of Business and Economics Statistics, 11, 93-101.
  • Cheung,Yin-Wong, Kon. S. Lai (2001).Long Memory and Nonlinear Mean Reversion in Japanese Yen-Based Real Exchange Rates. Journal of Internatio- nal Money and Finance 20, 115-132.
  • Chiang, S.M., Lee, Y.H., Su, Y.H. and Tzou, Y.P.(2011). Efficiency Test of Foreign Exchange Market for four Asian Countries. Research in International Business and Finance, 24(3), 284-294.
  • Chortareas, G., Jiang, Y. and Nankervis, J.C. (2011).The Random-Walk Be- haviour of the Euro Exchange Rate. Finance Research Letters, 8(3), 158-162.
  • Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity With Esti- mates Of The Variance Of United Kingdom Inflation. Econometrica: Journal of the Econometric Society, 987-1007.
  • Erlat, H. (2003). The Nature of Persistence in Turkish Real Exchange Rates, Emerging Markets Finance And Trade,39 (2), 70-97.
  • Geweke J. and Porter-Hudak S., (1983). The Estimation And Application Of Long Memory Time Series Models. Journal of Time Series Analysis, 4, 221– 238.
  • Granger, C. W., & Joyeux, R. (1980). An Introduction To Long‐Memory Time Series Models And Fractional Differencing. Journal of Time Series Analy- sis, 1(1), 15-29.
  • Floros, C. (2008). Long Memory in Exchange Rates: International Evidence, The International Journal of Business and Finance Research, 2(1), 31-39.
  • Han, Y.W. (2005). Long Memory Volatility Dependence, Temporal aggrega- tion and the Korean Currency Crisis: The Role of a High Frequency Korean Won (KRW)–US Dollar ($) Exchange Rate, Japan and World Economy,17(1): 97-109.
  • Hosking, J. R. (1981). Fractional Differencing. Biometrika, 68(1), 165-176.
  • Inclan, C., and Tiao, G.C. (1994). Use of Cumulative Sums of Squares for Retrospective Detection of Changes of Variance. Journal of the American Statistical Association, 89, 913–923.
  • Hurst H. (1951). Long Term Storage Capacity Of Reservoirs. Transactions of the American Society of Civil Engineers, 116, 770–799.
  • Kaya, H., Çelik, İ. (2018). Türkiye’de Satın Alma Gücü Paritesi Hipotezinin Ge- çerliliği: Uzun Hafıza Testlerinden Kanıtlar. Mehmet Akif Ersoy Üniversitesi, İİBF Dergisi. 5(2), 351-365.
  • Kumar, D. (2014). Long Memory in the Volatility of Indian Financial Market: An Empirical Analysis Based on Indian Data. diplom. de.
  • Kutlu, S., Yurttagüler, İ.M. (2014). Türkiye’de Reel Döviz Kurlarının Uzun Ha- fıza Özellikleri: Kesirli Bütünleşme Analizi. Marmara Üniversitesi İ.İ.B.F Dergi- si, XXXVI(I), 373-389.
  • Laurini, M. P. and M. S. Portugal (2003). Long Memory in The R$/US$ Exc- hange Rate: A Robust Analysis. Fınancelab Working Paper, Ibmec Sao Paulo, FLWP-03-2003.
  • Lo, A. W. (1991). Long Term Memory In Stock Market Prices. Econometrica, 59, 1279–1313.
  • Mandelbrot, B.B. (1972) Statistical Methodology For Nonperiodic Cycles: From The Covariance To R/S Analysis. Annals of Economic and Social Measu- rements, 1, 259–290.
  • Nakamura, T. and Small, M., 2007. Tests of the Random Walk Hypothe- sis for Financial Data. Physica A: Statistical Mechanics and Its Applications, 377(2):599-615.
  • Özer, M. Ve Türkyılmaz, S. (2004). Türkiye Finansal Piyasalarında Oynaklık- ların ARCH Modelleri ile Analizi. TC. Anadolu Üniversitesi, İktisadi ve İdari Bilimler Fakültesi Yayınları, No:1593, Sayı:186. Eskişehir.
  • Phillips, P. C. B., (1999). Discrete Fourier Transforms of Fractional Process, Unpublished Working Paper, No.1243, Cowles Foundation for Research in Economics, Yale University.
  • Robınson, P. M. (1995). Log-Periodogram Regression Of Time Series With Long Range Dependence. Annals of Statistics, 23, 1048-1072.
  • Robinson, P. M. and Henry, M. (1999). Long and short memory conditional heteroskedasticity in estimating the memory parameter of levels. Economet- ric Theory, 15 (03), 299-336.
  • Sansó, A., Aragó, V. and Carrion, J. L. (2004). Testing for Changes in the Unconditional Variance of Financial Time Series. Revista de Economía Finan- ciera,4, 32-53.
  • Shittu O.I and Yaya O. S. (2009). Measuring Forecast Performance of ARMA and ARFIMA Models: An Application to US Dollar/ UK pound Foreign Exchan- ge Rate. European Journal of Scientific Reaearch, 32 (2), 167-176.
  • Stengos, T., Yazgan, M.E. (2012). Persistence in Real Exchange Rate Conver- gence. Studies in Nonlinear Dynamics & Econometrics. 18(1), 1-27.
  • Tabak, M.B. and Cajueiro, D.O., 2006. Assessing Inefficiency in Euro Bilateral Exchange Rates. Physica A Statistical Mechanics and Its Applications, 367: 319-327.
  • Türkyılmaz, S. Özer, M. (2007). Türkiye’de Döviz Kuru Oynaklığının Uzun Hafıza Özelliklerinin Analizi. İktisat İşletme ve Finans İnceleme Araştırma. 22, 99-113.
  • Türkyılmaz, S., Balıbey, M. (2014). Türkiye Hisse Senedi Piyasası Getiri ve Oynaklığındaki Uzun Dönem Bağımlılık İçin Ampirik Bir Analiz. Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 16(2), 281-302.
  • Ürkmez, E. (2017).Reel Döviz Kurlarında Uzun Dönem Bağımlılık. Uluslararası Sosyal Araştırmalar Dergisi.10(49).
  • Vats, A. (2011). Long Memory in Returns and Volatility: Evidence from Fore- ign Exchange Market of Asian Countries. The International Journal of Appli- ed Economics and Finance. 5(4), 245-256.
There are 40 citations in total.

Details

Primary Language Turkish
Journal Section Research Article
Authors

Arife Özdemir This is me

Gizem Vergili This is me

İsmail Çelik This is me

Publication Date June 1, 2018
Published in Issue Year 2018 Volume: 12 Issue: 1

Cite

APA Özdemir, A., Vergili, G., & Çelik, İ. (2018). Döviz Piyasalarının Etkinliği Üzerinde Uzun Hafızanın Rolü: Türk Döviz Piyasasında Ampirik Bir Araştırma. BDDK Bankacılık Ve Finansal Piyasalar Dergisi, 12(1), 87-107.