Does the Efficient Market Hypothesis Hold?: Evidence from Six Transition Economies

Jagrič, Timotej; Podobnik, Boris; Kolanović, Marko

doi:10.1080/00128775.2005.11041112

Cited by 30 publications

(19 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Long-range correlations were first observed by Barkoulas et al in the discussion of Greek stock market [1]. In recent years, many researchers have found evidence of long-range correlations for stock markets [2][3][4][5]. To analyze the longrange correlations, previous studies presented various methods, such as rescaled range analysis (R/S analysis), wavelet transform modulus maxima (WTMM), and detrended fluctuation analysis (DFA) [6,7].…”

Section: Introductionmentioning

confidence: 99%

Effects of Exponential Trends on Correlations of Stock Markets

Lin

Shang

Zhou

2014

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Detrended fluctuation analysis (DFA) is a scaling analysis method used to estimate long-range power-law correlation exponents in time series. In this paper, DFA is employed to discuss the long-range correlations of stock market. The effects of exponential trends on correlations of Hang Seng Index (HSI) are investigated with emphasis. We find that the long-range correlations and the positions of the crossovers of lower order DFA appear to have no immunity to the additive exponential trends. Further, our analysis suggests that an increase in the DFA order increases the efficiency of eliminating on exponential trends. In addition, the empirical study shows that the correlations and crossovers are associated with DFA order and magnitude of exponential trends.

show abstract

Section: Introductionmentioning

confidence: 99%

Effects of Exponential Trends on Correlations of Stock Markets

Lin

Shang

Zhou

2014

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…(2005), Elder and Serletis (2007) and Ozun and Cifter (2007) employed the Wavelet analysis to investigate the price behaviour of share prices and indices. Jagric et al. (2005) concluded that four of the eastern European markets that were investigated exhibit long memory behaviour, which oppose the weak‐form EMH.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast to these local studies, international reviews such as Caporale and Gil-Alana (2002) and Narayan (2006) used more advanced statistical methods (fractional integration and regime switching models), to test the weak-form EMH. Jagric et al (2005), Elder and Serletis (2007) and Ozun and Cifter (2007) employed the Wavelet analysis to investigate the price behaviour of share prices and indices. Jagric et al (2005) concluded that four of the eastern European markets that were investigated exhibit long memory behaviour, which oppose the weak-form EMH.…”

Section: Introductionmentioning

confidence: 99%

“…Jagric et al (2005), Elder and Serletis (2007) and Ozun and Cifter (2007) employed the Wavelet analysis to investigate the price behaviour of share prices and indices. Jagric et al (2005) concluded that four of the eastern European markets that were investigated exhibit long memory behaviour, which oppose the weak-form EMH. Elder and Serletis (2007) concluded that the Dow Jones industrial average time series does not exhibit random walk or long memory behaviour, but is only a stationary process.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Further Evidence of Long Memory in the South African Stock Market

Morris

Vuuren

Styger

2009

South African J Economics

View full text Add to dashboard Cite

This paper expands and augments the results of the paper by Jefferis and Thupayagale ) and tests the efficiency of the South African stock market with Wavelet and Markov Switching Regime analyses of selected shares and the a ALSI 40 data. The Wavelet analysis indicated that most of the individual share prices and the share index time series are mean reverting over the long run and follow a long memory process, offering evidence against weak-form efficient market hypothesis (EMH). The Markov model modelled the financial and prevalent economic conditions accurately and established the presence of patterns in the historic time series, providing additional support against the weak-form EMH. Copyright (c) 2009 The Authors. Journal compilation (c) 2009 Economic Society of South Africa.

show abstract

“…In Ref. [19] it is found the long-range power-law dependence in the capital markets of six European transition economies. Clearly, one may expect that some of these markets are mutually related and thus a process like the one defined in Eqs.…”

mentioning

confidence: 99%

Modeling long-range cross-correlations in two-component ARFIMA and FIARCH processes

Podobnik

Horvatić

et al. 2008

Physica A: Statistical Mechanics and its Applications

Self Cite

136

View full text Add to dashboard Cite

We investigate how simultaneously recorded long-range power-law correlated multivariate signals cross-correlate. To this end we introduce a two-component ARFIMA stochastic process and a two-component FIARCH process to generate coupled fractal signals with long-range power-law correlations which are at the same time long-range cross-correlated. We study how the degree of cross-correlations between these signals depends on the scaling exponents characterizing the fractal correlations in each signal and on the coupling between the signals. Our findings have relevance when studying parallel outputs of multiple component of physical, physiological and social systems.Many empirical data are characterized by long-range power-law auto-correlations as well as by long-range crosscorrelations. Such a scale-invariant organization in both auto-correlations and cross-correlations can be observed either for the data variables or for their absolute values [1-9].Scale-invariant power-law auto-correlations in stochastic variables can be modeled by the fractionally autoregressive integrated moving-average process (ARFIMA) [10,11]:where d ∈ (−0.5, 0.5) is a scaling parameter, t denotes independent and identically distributed (i.i.d.) Gaussian variables with t = 0 and 2 t = 1, a n (d) are the weights defined by a n (d) = d (n − d)/( (1 − d) (n + 1)), where denotes the Gamma function and n is the time scale. We denote the auto-correlation function for x t as A(x t , x t−n ) ≡ A(n). For d = 0, the generated variable x t becomes random.

show abstract

Does the Efficient Market Hypothesis Hold?: Evidence from Six Transition Economies

Cited by 30 publications

References 30 publications

Effects of Exponential Trends on Correlations of Stock Markets

Effects of Exponential Trends on Correlations of Stock Markets

Further Evidence of Long Memory in the South African Stock Market

Modeling long-range cross-correlations in two-component ARFIMA and FIARCH processes

Contact Info

Product

Resources

About