Nonlinear analysis of Casablanca Stock Exchange, Dow Jones and S&amp;P500 industrial sectors with a comparison

Lahmiri, Salim; Bekiros, Stelios

doi:10.1016/j.physa.2019.122923

Cited by 16 publications

(12 citation statements)

References 51 publications

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“…Lahmiri, Bekiros, and Bezzina [17], Lahmiri and Bekiros [18] tested the presence of long memories in the stock markets. Lahmiri, Bekiros, and Bezzina [17] show that longterm price fluctuations are persistent, while short-term price fluctuations are anti-persistent.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In addition, the level of anti-persistence and the content of information in short-term fluctuations are similar in the four European markets. Lahmiri and Bekiros [18] tested the efficient market hypothesis, in its weak form, on the Casablanca Stock Exchange (CSE), Dow Jones and S&P500 stock markets. The authors show that prices are potentially predictable in the 3 markets and, in all industrial sectors.…”

Section: Literature Reviewmentioning

confidence: 99%

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Long memory in stock returns: Evidence from the Eastern European markets

et al. 2021

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This essay aims to analyze the impact of the 2020 global pandemic on the memory properties of the Eastern Europe stock markets, from the period between 1 January 2016 to 2 September 2020, the sample was divided in two subperiods: 1 January 2016 to 30 August 2019 (before Covid 19) and 2 September 2019 to 2 September 2020 (after Covid 19). To perform this analysis, different approaches were undertaken to analyze whether if: (i) the global pandemic (Covid-19) accentuated the exponents Detrended Fluctuation Analysis (DFA) and the Detrended Cross-Correlation Analysis (𝑝𝐷𝐶𝐶𝐴) in the Eastern European stock markets?. The daily returns do not have normal distributions, they have negative asymmetries, leptocubtic, and also exhibit conditional heteroscedasticity. The exponents Detrended Fluctuation Analysis (DFA), during the Covid-19 period, range from 0.64 to 0.75, showing significant long memories in all markets, except for the SLOVAKIA market (0.45). When we compared the 2 subperiods, we found that 41 pairs of markets have cross-correlation coefficients without trend ( λDCCA) strong (out of 45 possible), and 4 pairs of markets decreased the 𝑟ℎ𝑜𝐷𝐶𝐶𝐴 in particular the markets ESTONIA-SLOVAKIA, LITHUANIA-SLOVAKIA, HUNGARY-SLOVAKIA, POLAND-SLOVAKIA. These findings show that the assumption of the market efficiency hypothesis may be in question, since the prediction of market movement can be improved if we consider the out-of-lag movements of the other markets, enabling the occurrence of arbitrage operations and some difficulties in portfolio diversification.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Long memory in stock returns: Evidence from the Eastern European markets

et al. 2021

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“…The hierarchical clustering algorithm is a methodology that robustly explores the clustering of a dataset to mine this information for connectedness visualization. It is worth nothing that GARCH-based models, entropy, and hierarchical clustering have successfully been applied to model volatility [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ], to evaluate randomness in financial and economic data [ 29 , 30 , 31 , 32 , 33 , 34 , 35 ], and to cluster financial data [ 36 , 37 , 38 , 39 , 40 , 41 ].…”

Section: Introductionmentioning

confidence: 99%

Randomness, Informational Entropy, and Volatility Interdependencies among the Major World Markets: The Role of the COVID-19 Pandemic

Lahmiri

Bekiros

2020

Entropy

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The main purpose of our paper is to evaluate the impact of the COVID-19 pandemic on randomness in volatility series of world major markets and to examine its effect on their interconnections. The data set includes equity (Bitcoin and Standard and Poor’s 500), precious metals (Gold and Silver), and energy markets (West Texas Instruments, Brent, and Gas). The generalized autoregressive conditional heteroskedasticity model is applied to the return series. The wavelet packet Shannon entropy is calculated from the estimated volatility series to assess randomness. Hierarchical clustering is employed to examine interconnections between volatilities. We found that (i) randomness in volatility of the S&P500 and in the volatility of precious metals were the most affected by the COVID-19 pandemic, while (ii) randomness in energy markets was less affected by the pandemic than equity and precious metal markets. Additionally, (iii) we showed an apparent emergence of three volatility clusters: precious metals (Gold and Silver), energy (Brent and Gas), and Bitcoin and WTI, and (iv) the S&P500 volatility represents a unique cluster, while (v) the S&P500 market volatility was not connected to the volatility of Bitcoin, energy, and precious metal markets before the pandemic. Moreover, (vi) the S&P500 market volatility became connected to volatility in energy markets and volatility in Bitcoin during the pandemic, and (vii) the volatility in precious metals is less connected to volatility in energy markets and to volatility in Bitcoin market during the pandemic. It is concluded that (i) investors may diversify their portfolios across single constituents of clusters, (ii) investing in energy markets during the pandemic period is appealing because of lower randomness in their respective volatilities, and that (iii) constructing a diversified portfolio would not be challenging as clustering structures are fairly stable across periods.

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“…[42] apply multiple state-of-the-art efficiency tests for developed stock markets and validate the idea of dynamic and time-variant efficiency. Other recent papers investigating the EMH and using nonlinearities and chaos tests are those of [43][44][45].…”

Section: Literature Reviewmentioning

confidence: 99%

Nonlinearities and Chaos: A New Analysis of CEE Stock Markets

2021

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After a long transition period, the Central and Eastern European (CEE) capital markets have consolidated their place in the financial systems. However, little is known about the price behavior and efficiency of these markets. In this context, using a battery of tests for nonlinear and chaotic behavior, we look for the presence of nonlinearities and chaos in five CEE stock markets. We document, in general, the presence of nonlinearities and chaos which questions the efficient market hypothesis. However, if all tests highlight a chaotic behavior for the analyzed index returns, there are noteworthy differences between the analyzed stock markets underlined by nonlinearity tests, which question, thus, their level of significance. Moreover, the results of nonlinearity tests partially contrast the previous findings reported in the literature on the same group of stock markets, showing, thus, a change in their recent behavior, compared with the 1990s.

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Nonlinear analysis of Casablanca Stock Exchange, Dow Jones and S&P500 industrial sectors with a comparison

Cited by 16 publications

References 51 publications

Long memory in stock returns: Evidence from the Eastern European markets

Long memory in stock returns: Evidence from the Eastern European markets

Randomness, Informational Entropy, and Volatility Interdependencies among the Major World Markets: The Role of the COVID-19 Pandemic

Nonlinearities and Chaos: A New Analysis of CEE Stock Markets

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