Evaluation of Parallel Market's Long-term Memory Based on DFA and ARDL-Based Detrending (case study: Stock Market and Exchange Rate)

Document Type : Original Article

Authors

1 Ph.D. student of industrial management, Roodehen branch, Islamic azad university, Roodehen, Iran.

2 Assistant professor of accounting, Accounting department, Roodehen branch, Islamic azad university, Roodehen, Iran.

10.30495/ijfma.2023.21136

Abstract

In this study, the relationship between stock market long-term memory and exchange rate was studied. For this purpose, the analysis of detrended fluctuations was used and in order to detrend the data, two common detrending methods and cross-detrending were used. The research data included daily information of the stock market index and the dollar exchange rate during the period 2014/03/25 to 2021/02/07 and the data analysis was performed using the regression models. The results showed that the cross-trending of parallel markets produces different results in estimating the long-term memory of the data. According to the research findings, the stock index has a short-term memory under the conventional detrending method, while the cross-detrending method shows long-term memory for this index. The results for the exchange rate showed that under the conventional detrending method, the long-term memory of the exchange rate cannot be estimated in all market volatilities situations, while the cross-detrending method showed that the exchange rate loses its long-term memory in the face of increasing market fluctuations. The results also showed that under the cross-trending method, there is a direct and significant relationship between the long-term memory of the stock market index and the exchange rate.

Keywords

References

  • Aggarwal, R., (1981). Exchange rates and stock prices: a study of the United States capital markets under floating exchange rates. Akron Bus. Econ. Rev. 12, 7-12.
  • Aktan, C., Iren, P. & Omay, T. (2019). Market development and market e_ciency: Evidence based on nonlinear panel unit root tests. J. Financ., 25, 979-993.
  • Bahmani-Oskooee, M. & Sohrabian, A., (1992). Stock prices and the effective exchange rate of the dollar. Econ. 24 (4), 459-464.
  • Bastan, Frank (2017). Investigating the dynamic conditional correlation between oil prices and exchange rates in Iran with emphasis on long-term memory: FIEGARCH-DCC approach, PhD, University of Tabriz, Faculty of Economics and Management
  • Chkir I., Guesmi K., Ben Brayek A. & Naoui K., (2020). Modelling the nonlinear relationship between oil prices, stock markets, and exchange rates in oil-exporting and oil-importing countries, Research in International Business and Finance, Vol., 101274.
  • Ding L., Luo Y., Lin Y., Huang Y., (2021). Revisiting the relations between Hurst exponent and fractional differencing parameter for long memory, Physica A: Statistical Mechanics and its Applications, Vol. 566, 125603.
  • Dragota, V. & Tilica, E.V. (2014). Market efficiency of the post communist East European stock markets. Eur. J. Oper. Res., 22, 307-337.
  • Falahati, Farnaz (2019). Investigating the Effect of Exchange Rate Fluctuation on Tehran Stock Exchange Market Return, M.Sc., North Strategy Higher Education Institute, Department of
  • Fama, E.F.(1970). Efficient capital markets: A review of theory and empirical work. Financ., 25, 383-417.
  • Ferreira, P. (2018). Long-range dependencies of Eastern European stock markets: A dynamic detrended analysis. A Stat. Mech. Appl., 505, 454-470.
  • Gbenro, N. & Moussa, K.R. (2019). Asymmetric mean reversion in low liquid markets: Evidence from BRVM. Risk Financ. Manag., 12, 38.
  • Gómez-Gómez J., et al. (2021). Multifractal detrended fluctuation analysis of temperature in Spain (1960–2019), Physica A: Statistical Mechanics and its Applications, Vol. 578, 126118.
  • Grivel E., Berthelot B., Legrand P., Giremus A., (2021). DFA-based abacuses providing the Hurst exponent estimate for short-memory processes, Digital Signal Processing, Vol. 116, 103102.
  • Hosseini Ara, Farzaneh (2014). Investigating the effects of asymmetry and long-term memory on conditional fluctuations between the real exchange rate and stock returns on the stock exchange, M.Sc., Islamic Azad University, Central Tehran Branch, Faculty of Management and Accounting.
  • Jia Q., Zhang X. & Zhu Y. (2020). Multifractal analysis of the impact of US–China trade friction on US and China soy futures markets, Physica A: Statistical Mechanics and its Applications, Vol. 542, 123222.
  • Marques, L.M., Fuinhas, J.A. & Marques, A.C. (2013). Does the stock market cause economic growth? Portuguese evidence of economic regime change. Model. 32, 316-324.
  • Mensi W., Lee Y., Vo X. V., Yoon S., (2021). Does oil price variability affect the long memory and weak form efficiency of stock markets in top oil producers and oil Consumers? Evidence from an asymmetric MF-DFA approach, The North American Journal of Economics and Finance, Vol. 57, 101446.
  • Mnif E., Jarbouib A. & Mouakhar K., (2020). How the cryptocurrency market has performed during COVID 19? A multifractal analysis, Finance Research Letters, Vol. 36, 101647.
  • Nurunnabi, M. (2012). Testing weak form efficiency of emerging economies: A critical review of the literature. Bus. Econ. Manag., 13, 167-188.
  • Olayeni O. R., Kumar A. & Wohar T. M., (2020). Global economic activity, crude oil price and production, stock market behaviour and the Nigeria-US exchange rate, Energy Economics, vol. 92, 104938.
  • Tiwari A. K., Umar Z., Alqahtani F., (2021). Existence of long memory in crude oil and petroleum products: Generalised Hurst exponent approach, Research in International Business and Finance, Vol. 57, 101403.
  • Toki´c, S., Bolfek, B. & Radman Peša, A. (2018). Testing e_cient market hypothesis in developing Eastern European countries. Manag. Financ. Innov., 15, 281-291.
  • Xie Z., Chen S. W. & Wu A. C., (2020). The foreign exchange and stock market nexus: New international evidence, International Review of Economics & Finance, 67, 240-266.
  • Yao C. Z., Liu C. & Ju W. J. (2020). Multifractal analysis of the WTI crude oil market, US stock market and EPU, Physica A: Statistical Mechanics and its Applications, Vol 550, 124096.
  • Zhang X., Yang L. & Zhu Y. (2019). Analysis of multifractal characterization of Bitcoin market based on multifractal detrended fluctuation analysis, Physica A: Statistical Mechanics and its Applications, Vol. 523, pp 973-983.

Files

Share

How to cite

Statistics