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INVESTIGATION OF CHAOTIC DYNAMICS IN BRENT CRUDE OIL RETURNS / Brent Ham Petrol Getirilerinde Kaotik Dinamiklerin Araştırılması

Year 2019, Volume: 3 Issue: 1, 69 - 82, 30.04.2019

Emre Ürkmez

https://doi.org/10.29216/ueip.540147
Cited By: 1

Abstract

In this study, we investigate the chaotic dynamics in the returns of
Brent Crude Oil prices, based on three tests of nonlinearity and chaos. We use
the daily data of Brent Crude Oil prices for the period of 2009-2019. Firstly,
the nonlinearity of the returns is tested by employing the BDS test that shows
the evidences for the existence of the nonlinear structure. Afterwards, the long
memory of the returns is proved as a result of the GPH test. Lastly, we found
that daily returns have sensitivity to initial conditions by using the
correlation dimension analysis. These findings show that the daily returns of
Brent Crude Oil price returns can be characterized by chaotic dynamics and the
efficient market hypothesis does not hold. Hence, we can conclude that
short-term forecasts can be made but it is difficult to make long-term forecasts
for daily returns.

Keywords

Brent Crude Oil Nonlinear Chaos

References

  • Abhyankar A., Copeland L.S., ve Wong W. (1995). Nonlinear Dynamics in Real-Time Equity Market Indices: Evidence from the United Kingdom. The Economic Journal, 105, 864-880.
  • Adrangi, B., Chatrath, A., Dhanda, K.K., ve Raffiee, K. (2001). Chaos in Oil Prices? Evidence From Futures Markets. Energy Economics, 23(4), 405-425.
  • Aghababa, H. ve Barnett, W.A. (2016). Dynamic Structure of The Spot Price of Crude Oil: Does Time Aggregation Matter?. Energy Economics, 59, 227-237.
  • Barkoulas, J. ve Travlos, N. (1998). Chaos in An Emerging Capital Market? The Case of the Athens Stock Exchange. Applied Financial Economics, 8, 231-243.
  • Bildirici, M. (2018), The Chaotic Behavior Among The Oil Prices, Expectation of Investors and Stock Returns: TAR-TR-GARCH Copula and TAR-TR-TGARCH Copula. Petroleum Science, 16(4), 217-228.
  • Blank, S.C. (1991). Chaos in Futures Markets? A Nonlinear Dynamical Analysis. The Journal of Futures Markets, 11(6), 711-728.
  • Brock, W. A., Dechert, W.D., Scheinkman, J. ve LeBaron, B. (1996). A Test For Independence Based On Correlation Dimension. Econometric Reviews, 15(3), 197-235.
  • Brockett, P.L., Hinich, M.J. ve Patterson, D. (1988). Bispectral Based Tests for the Detection of Gaussianity and Linearity in Time Series. Journal of American Statistical Association, 83(403), 657-664.
  • Caraiani, P. (2012). Nonlinear Dynamics in CEE Stock Markets Indices. Economics Letters, 114, 329-331.
  • Chu, P.K.K. (2003). Study on The Non-random and Chaotic Behavior of Chinese Equities Market. Review of Pacific Basin Financial markets and Policies, 6(2), 1-24.
  • DeCoster, G.P., Labys, W.C., and Mitchell, D.W. (1992). Evidence of Chaos in Commodity Futures Prices. The Journal of Futures Markets, 12(3), 291-305.
  • Diaz, J.F.T. (2013). Evidence of Noisy Chaotic Dynamics in the Returns of Four Dow Jones Stock Indices. Annual Review of Chaos Theory, Bifurcations and Dynamical Systems, 4, 1-15.
  • Enders, W. (2010). Applied Econometric Time Series (3. Edition). New Jersey: Wiley.
  • Eser, R. (2013). Finansal Piyasalarda Kompleksite, Kaos ve Düzenleme. Mülkiyeliler Birliği Yayını, 11(1), 281-304.
  • Fama, E.F. (1965). The Behavior of Stock Market Prices. The Journal of Business, 38(1), 34-105.
  • Fama, E.F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383-417.
  • Frank, A.M. ve Stengos, T. (1989). Measuring The Strangeness of Gold and Silver Rates of Return. Review of Economic Studies, 56, 553-567.
  • Geweke, J. ve S.P. Hudak. (1983). The Estimation and Application of Long Memory Time Series Models. Journal of Time Series Analysis, 4, 221-238.
  • Grassberger, P. ve Procaccia, I. (1983). Measuring the Strangeness of Strange Attractors. Physica, 9, 189-208.
  • Günay, S. (2013). Finansal Piyasaların Fraktal Yapısı ve BIST-100 Endeksi'nin Fraktallığının Ölçümü. (Yayınlanmamış Doktora Tezi), İstanbul Üniversitesi Sosyal Bilimler Enstitüsü, İstanbul.
  • He, L.Y. (2011). Chaotic Structures in Brent & WTI Crude Oil Markets: Empirical Evidence. International Journal of Economics and Finance, 3(5), 242-249.
  • Hsieh, D.A. (1991). Chaos and Nonlinear Dynamics: Application to Financial Markets. The Journal of Finance, 46(5), 1839-1877.
  • Kyrtsou, C., Labys, W.C., and Terraza, M. (2004). Noisy Chaotic Dynamics in Commodity Markets. Empirical Economics, 29, 489-502.
  • Mandelbrot, B.B. (1963). The Variation of Certain Speculative Prices. The Journal of Business, 36(4), 394-419.
  • Mandelbrot, B.B. (1977). The Fractal Geometry of Nature (First Edition). New York: W.H. Freeman and Company.
  • Mobarek, A. ve Fiorante, A. (2014). The Prospects of BRIC Countries: Testing Weak Form Market Efficiency. Research in International Business and Finance, 30, 217-232.
  • Moshiri, S. ve Foroutan, F. (2006), Forecasting Nonlinear Crude Oil Futures Prices. The Energy Journal, 27(4), 81-95.
  • Oswiecimka, P., Drozdz, S., Kwapien, J., and Gorski, A.Z. (2010). Fractals, Log-Periodicity and Financial Crashes. Acta Physica Polonica A, 117(4), 637-639.
  • Panas, E. ve Ninni, V. (2000). Are Oil Markets Chaotic? A Nonlinear Dynamic Analysis. Energy Economics, 22, 549-568.
  • Peters, E.E. (1994). Fractal Market Analysis: Applying Chaos Theory to Investment and Economics (First Edition.). New York: Wiley.
  • Ramirez, S.C., Arreola, L.G., ve Grajeda, M.R. (2012), Nonlinear Dependence in Oil Price Behavior. Journal of Mathematics and System Science, 2, 110-118.
  • Scheinkman, J.A. ve LeBaron, B. (1989). Nonlinear Dynamics and Stock Returns. The Journal of Business, 62(3), 311-337.
  • Sülkü, S.N. ve Ürkmez, E. (2018). Hisse Senedi Getirilerinde Doğrusal Olmayan Dinamikler: Türkiye’den Kanıtlar. Uluslararası İktisadi ve İdari İncelemeler Dergisi, 18, 473-484.
  • Urquhart, A. ve McGroarty, F. (2016). Are Stock Markets Really Efficient? Evidence of The Adaptive Market Hypothesis. International Review of Financial Analysis, 47, 39-49.
  • Ürkmez, E. (2018). Gelişmekte Olan Ülkelerin Borsa Endekslerinin Kaotik Yapısının İncelenmesi. (Yayınlanmamış Doktora Tezi), Gazi Üniversitesi Sosyal Bilimler Enstitüsü, Ankara.
  • Wei, A. ve Leuthold, R.M. (1998). Long Agricultural Futures Prices: ARCH, Long Memory, or Chaos Processes?. OFOR Paper.
  • Willey, T. (1992). Testing For Nonlinear Dependence in Daily Stock Indices. Journal of Economics and Business, 44, 63-74.
  • Yavuz, N.Ç. (2014). Finansal Ekonometri (Birinci Baskı). İstanbul: Der Kitabevi.

BRENT HAM PETROL GETİRİLERİNDE KAOTİK DİNAMİKLERİN ARAŞTIRILMASI / Investigation of Chaotic Dynamics In Brent Crude Oil Returns

Year 2019, Volume: 3 Issue: 1, 69 - 82, 30.04.2019

Emre Ürkmez

https://doi.org/10.29216/ueip.540147
Cited By: 1

Abstract

Bu çalışmada Brent ham petrol
getirilerindeki kaotik dinamiklerin varlığı doğrusal olmama ve kaos testleri
yardımıyla araştırılmıştır. Bu amaçla 2009-2019 dönemleri arasında Brent ham
petrol günlük kapanış fiyat getirilerinden oluşan veri seti kullanılmıştır.
Öncelikle, BDS testi kullanılarak getirilerdeki doğrusal olmama test edilmiş ve
doğrusal olmayan yapının varlığına yönelik kanıt elde edilmiştir. Daha sonra,
getirilerin uzun hafızaya sahip olduğu GPH testi ile tespit edilmiştir. Son
olarak, korelasyon boyutu analizi kullanılarak günlük getirilerin başlangıç
durumlarına hassas bağlılık özelliği gösterdikleri görülmüştür. Tüm bulgular
bir arada değerlendirildiğinde Brent ham petrol günlük getirilerinin kaotik
dinamikler tarafından karakterize edildiği ve etkin piyasa hipotezinin geçerli
olmadığı tespit edilmiştir. Çalışmadaki tüm ampirik bulgular getiri serileri
için kısa dönemde öngörü yapılabileceğini, ancak uzun dönemli öngörü yapmanın
zor olduğu sonucuna işaret etmektedir.

Keywords

Brent Ham Petrol Doğrusal Olmama Kaos Kaos

References

  • Abhyankar A., Copeland L.S., ve Wong W. (1995). Nonlinear Dynamics in Real-Time Equity Market Indices: Evidence from the United Kingdom. The Economic Journal, 105, 864-880.
  • Adrangi, B., Chatrath, A., Dhanda, K.K., ve Raffiee, K. (2001). Chaos in Oil Prices? Evidence From Futures Markets. Energy Economics, 23(4), 405-425.
  • Aghababa, H. ve Barnett, W.A. (2016). Dynamic Structure of The Spot Price of Crude Oil: Does Time Aggregation Matter?. Energy Economics, 59, 227-237.
  • Barkoulas, J. ve Travlos, N. (1998). Chaos in An Emerging Capital Market? The Case of the Athens Stock Exchange. Applied Financial Economics, 8, 231-243.
  • Bildirici, M. (2018), The Chaotic Behavior Among The Oil Prices, Expectation of Investors and Stock Returns: TAR-TR-GARCH Copula and TAR-TR-TGARCH Copula. Petroleum Science, 16(4), 217-228.
  • Blank, S.C. (1991). Chaos in Futures Markets? A Nonlinear Dynamical Analysis. The Journal of Futures Markets, 11(6), 711-728.
  • Brock, W. A., Dechert, W.D., Scheinkman, J. ve LeBaron, B. (1996). A Test For Independence Based On Correlation Dimension. Econometric Reviews, 15(3), 197-235.
  • Brockett, P.L., Hinich, M.J. ve Patterson, D. (1988). Bispectral Based Tests for the Detection of Gaussianity and Linearity in Time Series. Journal of American Statistical Association, 83(403), 657-664.
  • Caraiani, P. (2012). Nonlinear Dynamics in CEE Stock Markets Indices. Economics Letters, 114, 329-331.
  • Chu, P.K.K. (2003). Study on The Non-random and Chaotic Behavior of Chinese Equities Market. Review of Pacific Basin Financial markets and Policies, 6(2), 1-24.
  • DeCoster, G.P., Labys, W.C., and Mitchell, D.W. (1992). Evidence of Chaos in Commodity Futures Prices. The Journal of Futures Markets, 12(3), 291-305.
  • Diaz, J.F.T. (2013). Evidence of Noisy Chaotic Dynamics in the Returns of Four Dow Jones Stock Indices. Annual Review of Chaos Theory, Bifurcations and Dynamical Systems, 4, 1-15.
  • Enders, W. (2010). Applied Econometric Time Series (3. Edition). New Jersey: Wiley.
  • Eser, R. (2013). Finansal Piyasalarda Kompleksite, Kaos ve Düzenleme. Mülkiyeliler Birliği Yayını, 11(1), 281-304.
  • Fama, E.F. (1965). The Behavior of Stock Market Prices. The Journal of Business, 38(1), 34-105.
  • Fama, E.F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383-417.
  • Frank, A.M. ve Stengos, T. (1989). Measuring The Strangeness of Gold and Silver Rates of Return. Review of Economic Studies, 56, 553-567.
  • Geweke, J. ve S.P. Hudak. (1983). The Estimation and Application of Long Memory Time Series Models. Journal of Time Series Analysis, 4, 221-238.
  • Grassberger, P. ve Procaccia, I. (1983). Measuring the Strangeness of Strange Attractors. Physica, 9, 189-208.
  • Günay, S. (2013). Finansal Piyasaların Fraktal Yapısı ve BIST-100 Endeksi'nin Fraktallığının Ölçümü. (Yayınlanmamış Doktora Tezi), İstanbul Üniversitesi Sosyal Bilimler Enstitüsü, İstanbul.
  • He, L.Y. (2011). Chaotic Structures in Brent & WTI Crude Oil Markets: Empirical Evidence. International Journal of Economics and Finance, 3(5), 242-249.
  • Hsieh, D.A. (1991). Chaos and Nonlinear Dynamics: Application to Financial Markets. The Journal of Finance, 46(5), 1839-1877.
  • Kyrtsou, C., Labys, W.C., and Terraza, M. (2004). Noisy Chaotic Dynamics in Commodity Markets. Empirical Economics, 29, 489-502.
  • Mandelbrot, B.B. (1963). The Variation of Certain Speculative Prices. The Journal of Business, 36(4), 394-419.
  • Mandelbrot, B.B. (1977). The Fractal Geometry of Nature (First Edition). New York: W.H. Freeman and Company.
  • Mobarek, A. ve Fiorante, A. (2014). The Prospects of BRIC Countries: Testing Weak Form Market Efficiency. Research in International Business and Finance, 30, 217-232.
  • Moshiri, S. ve Foroutan, F. (2006), Forecasting Nonlinear Crude Oil Futures Prices. The Energy Journal, 27(4), 81-95.
  • Oswiecimka, P., Drozdz, S., Kwapien, J., and Gorski, A.Z. (2010). Fractals, Log-Periodicity and Financial Crashes. Acta Physica Polonica A, 117(4), 637-639.
  • Panas, E. ve Ninni, V. (2000). Are Oil Markets Chaotic? A Nonlinear Dynamic Analysis. Energy Economics, 22, 549-568.
  • Peters, E.E. (1994). Fractal Market Analysis: Applying Chaos Theory to Investment and Economics (First Edition.). New York: Wiley.
  • Ramirez, S.C., Arreola, L.G., ve Grajeda, M.R. (2012), Nonlinear Dependence in Oil Price Behavior. Journal of Mathematics and System Science, 2, 110-118.
  • Scheinkman, J.A. ve LeBaron, B. (1989). Nonlinear Dynamics and Stock Returns. The Journal of Business, 62(3), 311-337.
  • Sülkü, S.N. ve Ürkmez, E. (2018). Hisse Senedi Getirilerinde Doğrusal Olmayan Dinamikler: Türkiye’den Kanıtlar. Uluslararası İktisadi ve İdari İncelemeler Dergisi, 18, 473-484.
  • Urquhart, A. ve McGroarty, F. (2016). Are Stock Markets Really Efficient? Evidence of The Adaptive Market Hypothesis. International Review of Financial Analysis, 47, 39-49.
  • Ürkmez, E. (2018). Gelişmekte Olan Ülkelerin Borsa Endekslerinin Kaotik Yapısının İncelenmesi. (Yayınlanmamış Doktora Tezi), Gazi Üniversitesi Sosyal Bilimler Enstitüsü, Ankara.
  • Wei, A. ve Leuthold, R.M. (1998). Long Agricultural Futures Prices: ARCH, Long Memory, or Chaos Processes?. OFOR Paper.
  • Willey, T. (1992). Testing For Nonlinear Dependence in Daily Stock Indices. Journal of Economics and Business, 44, 63-74.
  • Yavuz, N.Ç. (2014). Finansal Ekonometri (Birinci Baskı). İstanbul: Der Kitabevi.
There are 38 citations in total.

Details

Primary Language Turkish
Subjects Economics
Journal Section RESEARCH ARTICLES
Authors

Emre Ürkmez 0000-0002-2171-5027

Publication Date April 30, 2019
Published in Issue Year 2019 Volume: 3 Issue: 1

Cite

APA Ürkmez, E. (2019). BRENT HAM PETROL GETİRİLERİNDE KAOTİK DİNAMİKLERİN ARAŞTIRILMASI / Investigation of Chaotic Dynamics In Brent Crude Oil Returns. Uluslararası Ekonomi İşletme Ve Politika Dergisi, 3(1), 69-82. https://doi.org/10.29216/ueip.540147

Cited By

Beklenen Enflasyon Verilerinde Kaos Varlığının İncelenmesi
Yönetim ve Ekonomi Dergisi
https://doi.org/10.18657/yonveek.1034185
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Recep Tayyip Erdogan University
Faculty of Economics and Administrative Sciences
Department of Economics
RIZE / TURKEY