عنوان مقاله [English]
نویسنده [English]چکیده [English]
Fractal analyzing of continuous processes have recently emerged in literatures in various domains. Existence of long memory in many processes including financial time series have been evidenced via different methodologies in many literatures in past decade, which has inspired many recent literatures on quantifying the fractional Brownian motion (fBm) characteristics of financial time series. This paper questions the accuracy of commonly applied fractal analyzing methods on explaining persistent or antipersistent behavior of time series understudy. Rescaled range (R/S) and power spectrum techniques produce fractal dimensions for daily returns of twelve Malaysian stocks from the most well performed firms in Kuala Lumpur stock exchange. Zipf’s law generates linear and logarithmic power-law distribution plots to evaluate the validity of estimated fractal dimensions on prescribing persistent and antipersistent characteristics with less ambiguity. Findings of this study recommend a more thoughtful approach on classifying persistent and antipersistent behaviors of financial time series by utilizing existing fractal analyzing methods.
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