Multifractal Analysis of African Stock Markets During the 2007–2008 US Crisis

2019 ◽  
Vol 22 (04) ◽  
pp. 1950022
Author(s):  
Oussama Tilfani ◽  
My Youssef El Boukfaoui
Keyword(s):  
Stock Markets ◽  
Global Financial Crisis ◽  
Fluctuation Analysis ◽  
Subprime Crisis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Multi Scale ◽  
Rolling Window ◽  
The Global Financial Crisis ◽  
Fractal Market Hypothesis ◽  
Detrended Fluctuation

In this paper, we examine the effects of subprime crisis on the largest African stock markets (South Africa, Nigeria, Egypt, and Morocco) by testing the fractal market hypothesis. We use a rolling window Multifractal Detrended Fluctuation Analysis, and find decline in local Hurst exponent and an increase in short-term trading activity for all considered stock markets during the global financial crisis. We furthermore investigate the interrelationships of African and the American stock markets using multi-scale contagion test. Findings suggest that the cross-correlation of African stock markets increases with American markets becoming higher during the crisis sub-period. However, the presence of contagion or interdependence effects are country and time horizon-dependent. Implications of the results are discussed.

scholarly journals Investigating Long-Range Dependence of Emerging Asian Stock Markets Using Multifractal Detrended Fluctuation Analysis

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1157
Author(s):  
Faheem Aslam ◽  
Saima Latif ◽  
Paulo Ferreira
Keyword(s):  
Time Series ◽  
Long Range ◽  
Stock Markets ◽  
Detrended Fluctuation Analysis ◽  
Financial Time Series ◽  
Long Range Dependence ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Financial Time ◽  
Detrended Fluctuation

The use of multifractal approaches has been growing because of the capacity of these tools to analyze complex properties and possible nonlinear structures such as those in financial time series. This paper analyzes the presence of long-range dependence and multifractal parameters in the stock indices of nine MSCI emerging Asian economies. Multifractal Detrended Fluctuation Analysis (MFDFA) is used, with prior application of the Seasonal and Trend Decomposition using the Loess (STL) method for more reliable results, as STL separates different components of the time series and removes seasonal oscillations. We find a varying degree of multifractality in all the markets considered, implying that they exhibit long-range correlations, which could be related to verification of the fractal market hypothesis. The evidence of multifractality reveals symmetry in the variation trends of the multifractal spectrum parameters of financial time series, which could be useful to develop portfolio management. Based on the degree of multifractality, the Chinese and South Korean markets exhibit the least long-range dependence, followed by Pakistan, Indonesia, and Thailand. On the contrary, the Indian and Malaysian stock markets are found to have the highest level of dependence. This evidence could be related to possible market inefficiencies, implying the possibility of institutional investors using active trading strategies in order to make their portfolios more profitable.

scholarly journals Multifractal Behaviors of Stock Indices and Their Ability to Improve Forecasting in a Volatility Clustering Period

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1018
Author(s):  
Shuwen Zhang ◽  
Wen Fang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Stock Markets ◽  
Short Term Memory ◽  
Fluctuation Analysis ◽  
Test Results ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Volatility Clustering ◽  
Detrended Fluctuation ◽  
Gated Recurrent Unit

The financial market is a complex system, which has become more complicated due to the sudden impact of the COVID-19 pandemic in 2020. As a result there may be much higher degree of uncertainty and volatility clustering in stock markets. How does this “black swan” event affect the fractal behaviors of the stock market? How to improve the forecasting accuracy after that? Here we study the multifractal behaviors of 5-min time series of CSI300 and S&P500, which represents the two stock markets of China and United States. Using the Overlapped Sliding Window-based Multifractal Detrended Fluctuation Analysis (OSW-MF-DFA) method, we found that the two markets always have multifractal characteristics, and the degree of fractal intensified during the first panic period of pandemic. Based on the long and short-term memory which are described by fractal test results, we use the Gated Recurrent Unit (GRU) neural network model to forecast these indices. We found that during the large volatility clustering period, the prediction accuracy of the time series can be significantly improved by adding the time-varying Hurst index to the GRU neural network.

scholarly journals Evidence of Intraday Multifractality in European Stock Markets during the Recent Coronavirus (COVID-19) Outbreak

2020 ◽  
Vol 8 (2) ◽  
pp. 31 ◽  
Author(s):  
Faheem Aslam ◽  
Wahbeeah Mohti ◽  
Paulo Ferreira
Keyword(s):  
Stock Market ◽  
Market Efficiency ◽  
Stock Markets ◽  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Policy Makers ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Index Data ◽  
European Stock Markets ◽  
Detrended Fluctuation

This study assesses how the coronavirus pandemic (COVID-19) affects the intraday multifractal properties of eight European stock markets by using five-minute index data ranging from 1 January 2020 to 23 March 2020. The Hurst exponents are calculated by applying multifractal detrended fluctuation analysis (MFDFA). Overall, the results confirm the existence of multifractality in European stock markets during the COVID-19 outbreak. Furthermore, based on multifractal properties, efficiency varies among these markets. The Spanish stock market remains most efficient while the least efficient is that of Austria. Belgium, Italy and Germany remain somewhere in the middle. This far-reaching outbreak demands a comprehensive response from policy makers to improve market efficiency during such epidemics.

Investigating efficiency of frontier stock markets using multifractal detrended fluctuation analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Faheem Aslam ◽  
Paulo Ferreira ◽  
Wahbeeah Mohti
Keyword(s):  
Stock Returns ◽  
Long Range ◽  
Stock Markets ◽  
Detrended Fluctuation Analysis ◽  
Long Range Dependence ◽  
Fluctuation Analysis ◽  
Content Type ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Multifractal Behavior ◽  
Detrended Fluctuation

PurposeThe investigation of the fractal nature of financial data has been growing in the literature. The purpose is to investigate the multifractal behavior of frontier markets using multifractal detrended fluctuation analysis (MFDFA).Design/methodology/approachThis study used daily closing prices of nine frontier stock markets up to 31-Aug-2020. A preliminary analysis reveals that these markets exhibit fat tails and clustering patterns. For a more robust analysis, a combination of Seasonal and Trend Decomposition using Loess (STL) and MFDFA has been employed. The former method is used to decompose daily stock returns, where later detected the long rang dependence in the series.FindingsThe results confirm varying degree of multifractality in frontier stock markets, implying that they exhibit long-range dependence. Based on these multifractality levels, Serbian and Romanian stock markets are the ones exhibiting least long-range dependence, while Slovenian and Mauritius stock markets indicating highest dependence in their series. Furthermore, the markets of Kenya, Morocco, Romania and Serbia exhibit mean reversion (anti-persistent) behavior while the remaining frontier markets show persistent behaviors.Practical implicationsThe information given by the detection of the fractal measure of data can support for investment and policymaking decisions.Originality/valueFrontier markets are of great potential from the perspective of international diversification. However, most of the research focused on other emerging and developed markets, especially in the context of multifractal analysis. This study combines the STL method and a physics-based robust technique, MFDFA to detect the multifractal behavior of frontier stock markets.

scholarly journals Multifractal Detrended Fluctuation Analysis (MF-DFA) of Stock Market Indexes. Empirical Evidence from Seven Central and Eastern European Markets

2020 ◽  
Vol 12 (2) ◽  
pp. 535 ◽  
Author(s):  
Laura Raisa Miloş ◽  
Cornel Haţiegan ◽  
Marius Cristian Miloş ◽  
Flavia Mirela Barna ◽  
Claudiu Boțoc
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Detrended Fluctuation Analysis ◽  
Sustainable Growth ◽  
Mature Stage ◽  
Fluctuation Analysis ◽  
Eastern European ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Long Range Correlations ◽  
Detrended Fluctuation

In this paper, we present a comparative investigation of the multifractal properties of seven Central and Eastern European (CEE) stock markets using recent financial data up to August 2018 by employing seasonal and trend decompositions before applying multifractal detrended fluctuation analysis. We find that stock indices returns exhibit long-range correlations, supporting the idea that the stock markets in question are not efficient markets and have not reached a mature stage of market development. The results of the paper are of interest to investors looking for opportunities in these stock exchanges and also to policy makers in their endeavour of realizing institutional reforms in order to increase stock market efficiency and to support the sustainable growth of the financial markets.

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