Evaluating and study of the Fractal Properties of Capital Markets Based on DE trended Fluctuation Analysis (Case Study: Exchange Market and Stock Index of Tehran)
Subject Areas : Financial Knowledge of Securities Analysis
Arash Azaryoun
1
(
PhD Student in Industrial Management, Financial Orientation, Roodehen Branch, Islamic Azad University, Roodehen, Iran.
)
narges yazdanian
2
(
Corresponding Author: Assistant Professor of Accounting, Department of Accounting, Roudehen Branch, Islamic Azad University, Roudehen, Iran
)
seyedalireza mirarab baygi
3
(
Assistant Professor of Accounting, Department of Accounting, Roudehen Branch, Islamic Azad University, Roudehen, Iran
)
hoda hemmati
4
(
Assistant Professor of Accounting, Department of Accounting, Roudehen Branch, Islamic Azad University, Roudehen, Iran
)
Keywords: long-term memory, Multifractal properties, exchange rate, Stock Exchange Index,
Abstract :
In this study, the long-term memory of the stock market index and exchange rate (dollar) was estimated using detrended fluctuation analysis. In order to detrend the data, the GARCH approach was proposed and the long-term memory estimation model was implemented separately for both conventional and GARCH methods. For this purpose, daily data of stock market index and dollar exchange rate in the market during the years 2014 to 2020 were used. The results showed that the conventional method in calculating the detrended fluctuations is not able to estimate the long-term memory of the exchange rate, while the results for the stock index showed the existence of short-term memory. The results showed that the proposed method in detrending data and calculating detrended fluctuations based on Garch model has a higher power in controlling changes in market fluctuations and according to the findings of this method, stock index and dollar exchange rate have long-term memory. The results showed that these two methods provide significantly different estimates of long-term memory of the market and according to the results of the correlation test between the values of long-term memory of data and the value of parameter q in detrended fluctuation analysis; it was observed that the stock market index and exchange rate in Iran have multifractal properties.
* خیاط سرکار، وحید (1395). اثر اجرای برجام بر حافظه بلند مدت سری های زمانی مالی( مطالعه ای در بورس اوراق بهادار تهران)، کارشناسی ارشد، دانشگاه یزد، دانشکده مدیریت و حسابداری.
* رستمی، علی (1395). بکارگیری نظریه ارزشِ فرین و حافظه بلندمدت در بازار سهام ایراندرچارچوب مدل (GARCH)، کارشناسی ارشد، دانشگاه شیخ بهایی، دانشکده علوم ریاضی.
* رضائی، مهدیه (1397). برآورد پارامتر هرست وابسته به زمان در سری‌های زمانی مالی، کارشناسی ارشد، دانشگاه الزهرا (س)، دانشکده علوم ریاضی.
* روشن، رحمت اله (1397). بررسی رفتار نامتقارن چندفراکتالی در شاخص‌های بورس اوراق بهادار تهران با استفاده از تحلیل چند فراکتالی نوسانات بدون روند شده نامتقارن، کارشناسی ارشد، دانشگاه ارومیه، دانشکده اقتصاد و مدیریت.
* نسیم‌سبحان، محمد (1395). تحلیل فراکتالی سری‌های زمانی مالی با استفاده از نمای هرست (مطالعه‌ای در بازار سرمایه ایران)، کارشناسی ارشد، دانشگاه علم و هنر یزد، دانشکده علوم انسانی.
* نیک‌بین، بهنام (1394). بررسی توسعه مالی بورس اوراق بهادار تهران: رهیافت حافظه بلند مدت برای آزمون EMH، کارشناسی ارشد، دانشگاه علامه طباطبائی، دانشکده اقتصاد.
* نیکخواه بهرامی، الهام (1390). آزمون کارایی در سطح ضعیف در بورس اوراق بهادار تهران با استفاده از پارامتر حافظه و انحراف از فرضیه گاوسی، کارشناسی ارشد، دانشگاه الزهرا (س)، دانشکده اقتصاد و حسابداری.
* Aktan, C.; Iren, P.; Omay, T. (2019). Market development and market e_ciency: Evidence based on nonlinear panel unit root tests. Eur. J. Financ., 25, 979–993
* Anagnostidis, P.; Emmanouilides, C.; Varsakelis, C.(2016). Has the 2008 financial crisis affected stock market e_ciency? The case of Eurozone. Phys. A Stat. Mech. Appl., 447, 116–128.
* Fama, E.F.(1970). Efficient capital markets: A review of theory and empirical work. J. Financ., 25, 383–417.
* Ferreira, P. (2018). Long-range dependencies of Eastern European stock markets: A dynamic detrended analysis. Phys. A Stat. Mech. Appl., 505, 454–470.
* Gbenro, N.; Moussa, K.R. (2019). Asymmetric mean reversion in low liquid markets: Evidence from BRVM. J. Risk Financ. Manag., 12, 38.
* Han C., Wang Y., Ning Y., (2019). Comparative analysis of the multifractality and efficiency of exchange markets: Evidence from exchange rates dynamics of major world currencies, Physica A: Statistical Mechanics and its Applications, Vol. 535, 12236.
* Kristoufek, L.; Vosvdra, M.(2013). Measuring capital market e_ciency: Global and local correlations structure. Phys. A Stat. Mech. Appl., 392, 184–193.
* Li S., Xu N., Hui X., (2020). International investors and the multifractality property: Evidence from accessible and inaccessible market, Physica A: Statistical Mechanics and its Applications, Vol. 559, 125029.
* McKinley, W. (2015). Python and Data Analysis / W. McKinley; Translated from English. A.A. Slinkin. - Moscow: DMK Press, p. 482.
* Milo¸s L.R., et. Al., (2020). Multifractal Detrended Fluctuation Analysis (MF-DFA) of Stock Market Indexes. Empirical Evidence from Seven Central and Eastern European Markets, sustainability, 12, 535, 1-15.
* Stosic D., Stosica D., Mattos P., TatijanaStosic N., (2019). Multifractal characterization of Brazilian market sectors, Physica A: Statistical Mechanics and its Applications, Vol. 525, pp 956-964.
* Toki´c, S.; Bolfek, B.; Radman Peša, A. (2018). Testing e_cient market hypothesis in developing Eastern European countries. Invest. Manag. Financ. Innov., 15, 281–291.
* Yan R., Yue D., Chen X., Wu X., (2020). Non-linear characterization and trend identification of liquidity in China's new OTC stock market based on multifractal detrended fluctuation analysis, Chaos, Solitons & Fractals, Vol. 139, 110063.
* Yuan Y., Zhang T., (2020). Forecasting stock market in high and low volatility periods: a modified multifractal volatility approach, Chaos, Solitons & Fractals, Vol. 140, 110252.
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