Journals / Doğuş Üniversitesi Dergisi / 2015 / Cilt: 16 - Sayı: 1

FRACTALITY ANALYSIS of BIST100 INDEX RETURNS and VOLUME

BİST100 ENDEKSİ FİYAT ve İŞLEM HACMİNİN FRAKTALLIK ANALİZİ

Pages
35–50
DOI
—
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AI Summary & Key Takeaways

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

Results showed that all findings of the different methods consistent with each other, and there is no fractality in the BIST100 index returns and volume

📌 3 Key Takeaways

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Objective & Hypothesis:In this study, we examined the fractal structure of the BIST100 index returns and volume during the period of 04.01.2000-19.03.2014.
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Key Findings & Data:Long memory analysis was conducted via Rescaled Range R/S analysis, Detrended Fluctuaiton Analysis DFA and Smith’s 2005 modified GPH analysis; fractal dimension calculations were performed with Box-Counting, Semi-Periodogram and Variogram methods.
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Conclusion & Impact:Results showed that all findings of the different methods consistent with each other, and there is no fractality in the BIST100 index returns and volume
🔬Design:Research Article
📍Scope:2000 Verileri
⚙️Variables:Uzun Dönemli Bellek · Fraktal Boyut · Kendine Benzerlik

Abstract

In this study, we examined the fractal structure of the BIST100 index returns and volume during the period of 04.01.2000-19.03.2014. In the fractality tests we used long memory analysis and the fractal dimension calculation methods. Long memory analysis was conducted via Rescaled Range R/S analysis, Detrended Fluctuaiton Analysis DFA and Smith’s 2005 modified GPH analysis; fractal dimension calculations were performed with Box-Counting, Semi-Periodogram and Variogram methods. Results showed that all findings of the different methods consistent with each other, and there is no fractality in the BIST100 index returns and volume

Özet

Bu çalışmada 04.01.2000-19.03.2014 dönemi için BIST100 Endeksi’nin getirileri ve işlem hacminin fraktal yapısı incelenmiştir. Fraktallık testlerinde uzun dönemli bellek analizleri ve fraktal boyut hesaplama yöntemleri kullanılmıştır. Uzun dönemli bellek hesaplamalarında Dönüştürülmüş Genişlik analizi, Eğilimden Arındırılmış Dalgalanma Analizi ve Smith’in 2005 modifiye GPH analizi kullanılırken, fraktal boyut hesaplamalarında Kutu Sayım,Yarı-Periyodogram ve Variogram yöntemleri uygulanmıştır. Elde edilen sonuçlar tüm yöntemler için tutarlı olup, hem BIS100 endeks getirlerinde hem de işlem hacminde fraktal bir yapı olmadığı görülmüştür.

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Cite This Article

GÜNAY, S. (2015). FRACTALITY ANALYSIS of BIST100 INDEX RETURNS and VOLUME. Doğuş Üniversitesi Dergisi, Cilt: 16 - Sayı: 1, 35–50. https://ajindex.com/articles/127181

References (1)

  1. BAILLIE, R.T., BOLLERSLEV, T., MIKKELSEN, H.O. (1996). Fractionally integrated generalized autoregressive conditional heteroscedasticity. Journal of Econometrics, 74, 3- 30. ss. BARKOULAS, J.T., BAUM, C.F. (1997). Long memory and forecasting in euroyen deposit rates. Financial Engineering and the Japanese Markets, 4 (3), 189-201. ss. BARKOULAS, J.T., BAUM, C.F., ÇAĞLAYAN, M. (1998). Fractional monetary dynamics. Boston College Working Papers in Economics, 321, 1-21. ss. BAYRAKTAR, E., POOR, H.V., SIRCAR, K.R. (2004). Estimating the fractal dimension of the S&P 500 index using wavelet analysis. International Journal of Theoretical and Applied Finance, 7(5), 613-643.ss. BO, C., ZHONGYONG, Y. (2007). On the fractal analysis of listed military industry capital market. International Conference on Management Science and Engineering, Finance Analysis, 281-288. ss. CAVALCANTE, J., ASSAF, A. (2002). Long range dependence in the returns and volatility of the Brazilian stock market. Working Paper, Banco Nacional do Desenvolvimiento, Rio de Janeiro. FAMA, E. (1965). Random walks in stock market prices. Financial Analysis Journal, 76, 75- 80. ss. FOCARDI, S.M., FABOZZI, F.J. (2004). The mathematics of financial modeling and investment management. New Jersey, John Wiley & Sons. FOO, D.A.C. (2009). Modeling market memory as potential indicators of market informational efficiency. 18th World Imacs / Modsim Congress, Australia, 2009, s.1307- 1313. ss. GAYATHRI, M., SELVAM, M., LINGARAJA, K., VASANTH V., KARPAGAM, V. (2013). Fractal dimension Of S&P Cnx nifty stock returns. Asian Journal of Empirical Research, 3(9), 1166-1190. ss. GEWEKE, J., PORTER-HUDAK, S. (1983). The estimation and appication of long memory time series models. Journal of Time Series Analysis, 4 (4), 221-238. ss. GIRAITIS, L., KOKOSZKA, P., LEIPUS, R., TEYSSIERE, G. (2003). Rescaled variance and related tests for long memory in volatility and levels. Journal of Econometrics, 112 (2), 265-294. ss. GNEITING, T., SEVCIKOVA, H., PERCIVAL, D.B. (2012). Estimators of fractal dimension: assessing the roughness of time series and spatial data. Statistical Science, 27 (2), 247– 277. ss. GRANGER, C.W.J., JOYEUX, R. (1980). An introduction to loag memory time series models and fractional differencing. Journal of Time Series Analysis, 1, 5-39. ss. GREENE, M.T., FIELITZ, B.D. (1977). Long-term dependence in common stock returns. Journal of Financial Economics, 4 (3), 339-349. ss. HERGARTEN, S. (2002). Self-organized