Saeed Rasekhi; Mahdi Shahrazi
Volume 18, Issue 57 , February 2014, , Pages 1-26
Abstract
Based on efficient market hypothesis, financial markets are impossible to forecast. The purpose of this paper is to examine the weak-form efficiency of the Iranian foreign exchange rate (defined by the Rial/Dollar) during time period 1999:25:01 to 2010:17:06 from long memory viewpoint. For this, we have ...
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Based on efficient market hypothesis, financial markets are impossible to forecast. The purpose of this paper is to examine the weak-form efficiency of the Iranian foreign exchange rate (defined by the Rial/Dollar) during time period 1999:25:01 to 2010:17:06 from long memory viewpoint. For this, we have employed three methods of scaling analysis including classical rescaled range (R/S) analysis, modified rescaled range (M-R/S) analysis and detrended fluctuation analysis (DFA). We have divided the time period into two sub-periods, 1999:25:01-2002:21:03 and 2002:21:03-2010:17:06. In the former time period, Iran had a fixed exchange rate regime and in the latter period, the country followed a managed floating exchange rate regime. The obtained results from these methods are not the same. To achieve more explicit conclusions, we’ve used two more widely applied econometric tests namely augmented Dickey-Fuller (ADF) test and Phillips-Perron (PP) test to determine whether or not the time series under consideration behave as random walk consistent with the weak-form efficiency. The findings indicate that the result of DFA is in line with the econometric approach. We conclude that the Iranian foreign currency market at the first sub-period is less efficient relative to the second sub-period. Another important result is that relying on only one method to make a conclusion about market efficiency may be very misleading. Therefore, one should first carefully select more reliable methods and then compare their results to achieve a reliable conclusion.
Gholamreza Keshavarz Haddad; Seyed Babak Ebrahimi; Akbar Jafar Abadi
Volume 16, Issue 47 , July 2011, , Pages 129-162
Abstract
Long memory in asset returns and volatilities is a new research area, both in theoretical and empirical modeling of high frequent financial time series. The most popular techniques of time series modeling with long memory is the ARFIMA-FIGARCH, but this fractionality in the integration of time series ...
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Long memory in asset returns and volatilities is a new research area, both in theoretical and empirical modeling of high frequent financial time series. The most popular techniques of time series modeling with long memory is the ARFIMA-FIGARCH, but this fractionality in the integration of time series modeling has not been extended to the Multivariate GARCH models yet. The present paper aims to extend the BEKK’s MGARCH models to take into account the presence of long memory in daily financial time series. Although the proposed procedure is highly non-linear in the fractionality parameters with a serious computational burden, it estimates all the parameters of mean and variance equations in a nonlinear framework and finds a unique solution, by numerical optimization procedures. In the empirical part of the paper a multivariate FIGARCH is used to check the transmission of volatility among the automobile industry, machinery leasing and equipment indices in the Tehran Stock Exchange. The results confirm the existence of short memory in both conditional means and conditional variances, and moreover the magnitude of estimated d parameter is remarkably different from those of resulted from GPH and single ARFIMA-FIGARCH. Empirical findings of the MFIGARCH specification were compared with those of BEKK, and the comparison shows that MFIGARCH estimations are consistent with theoretical considerations. Moreover, our findings confirm the presence of lead and lag effects and information flow between the returns and volatilities of automobile industries and machinery leasing stock prices, and a multilateral information transmission from machinery leasing’s stock towards the Auto industry and machinery parts manufacturing share prices is observed.