A tripartite inquiry into volatility-efficiency-integration nexus - case of emerging markets

Rizvi, Syed Aun R.; Arshad, Shaista; Alam, Nafis

doi:10.1016/j.ememar.2017.11.005

Cited by 30 publications

(21 citation statements)

References 55 publications

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“…Bakaert et al, 2012), we thereby fit the Markov regime-switching model with a first-order 1 Wang et al (2011) and Li and Giles (2015) show the standard deviation of Chinese stock market index is higher than that of other international stock markets. Rizvi, et al (2018) show the conditional volatility in Chinese stock market is higher than that of the other emerging stock markets. 2 We use the terms firm volatility and idiosyncratic volatility interchangeably.…”

Section: Introductionmentioning

confidence: 87%

“…Chinese stock market, the largest emerging stock market in the world, has been documented to be more volatile than the other international stock markets (e.g. Wang et al, 2011;Li and Giles, 2015;Rizvi et al, 2018) 1 . The recent 2015-2016 stock market crash has further attracted the attentions of researchers on the investigation of the volatile feature in Chinese stock markets (Tian et al, 2018;Darby et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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The Idiosyncratic Risk in Chinese Stock Market

Darby

Zhang

2019

SSRN Journal

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Using daily stock return data of all listed firms in Chinese stock market from 1998 to 2018, we disaggregate the volatility of common stocks at the market, industry and firm levels. We find market volatility, on average, is the highest while firm volatility tends to lead to market and industry volatility series. None long-term trend time series behaviour exists for all three volatility series and firm volatility is best described by an autoregressive process with regime shifts associated with the structural market reforms or volatile market movements. We further proceed to identify the source of volatility at the industry level and find the idiosyncratic volatility in the largest manufacturing industry not only accounts for the largest proportion in the aggregate firm volatility but also is the lead indicator for the idiosyncratic volatility of other industries. Finally, unlike Brandt et al. [Review of Financial Studies 23(2): 863-899 (2010)], we find the idiosyncratic volatility in Chinese stock market is associated with high stock trading activities by institutional investors, the result of which is also robust when using other measures of idiosyncratic volatility.

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Section: Introductionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

The Idiosyncratic Risk in Chinese Stock Market

Darby

Zhang

2019

SSRN Journal

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“…The results could also be used by the authorities, since multifractality could be caused by the existence of some kind of inefficiencies caused by stock markets' complexity (Kumar & Deo, 2009;Matia et al, 2003;Norouzzadeh & Rahmani, 2006) but also by the lesser development of the financial markets themselves (Dewandaru et al, 2014;Rizvi et al, 2018). So, despite the multifractality inherent to the turbulent moments identified, in this case the authorities of CEEC countries should make some efforts to develop their markets, all the more so because of their importance in the economy (Wei & Wang, 2008).…”

Section: Concluding Remarks and Policy Implicationsmentioning

confidence: 99%

“…Finally, continuous monitoring of stock markets is relevant, even once the Covid-19 crisis ends, as the presence of multifractality could be related to the predictability of a crash due to speculative bubbles (see, for example, Grech & Pamuła, 2008;Rizvi et al, 2018;Wei & Huang, 2005). This could imply increased market risk (Jiang et al, 2019), making it crucial to continue to evaluate these patterns in the future.…”

Section: Concluding Remarks and Policy Implicationsmentioning

confidence: 99%

The footprints of COVID-19 on Central Eastern European stock markets: an intraday analysis

Aslam

Nogueiro

Brasil

et al. 2020

Post-Communist Economies

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This study analyses the intraday multifractal behaviour of three Central Eastern European stock markets by deploying five-minute index data ranging from December 2019 to May 2020. With the analysis of multifractality, we can evaluate the degree of efficiency of the stock markets analysed. We divided the whole sample into three different periods of about two months each. Data for the Czech Republic, Hungary and Poland are used and their behaviour is compared with Germany (as a benchmark of the European Union) and Italy and Spain (as the most affected countries by Covid-19 in Europe). For the analysis, we employ multifractal detrended fluctuation analysis after using seasonal-trend decompositions using the loess method. The results confirm that the degree of multifractality varies in the different periods, with increasing multifractality in February-March and a recovery in April-May.

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“…The main reasons for multifractality in stock markets may include inefficient market systems [16,35,77], liquidity problems [78], crash predictability [79,80], volatility predictability [80], speculative bubbles [78], and complexity [22,81,82] of the market, which could have some impact on their efficiency. This could be improved through a stronger legal environment, economic freedom and financial intermediaries [83].…”

Section: Discussionmentioning

confidence: 99%

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

2020

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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.

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A tripartite inquiry into volatility-efficiency-integration nexus - case of emerging markets

Cited by 30 publications

References 55 publications

The Idiosyncratic Risk in Chinese Stock Market

The Idiosyncratic Risk in Chinese Stock Market

The footprints of COVID-19 on Central Eastern European stock markets: an intraday analysis

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

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