Analysis of efficiency for Shenzhen stock market: Evidence from the source of multifractality

The high frequency multifractal properties of Bitcoin

“…This led Peters (1994) to coin the term Fractal Market Hypothesis (FMH) as an alternative to the Efficient Market Hypothesis (EMH), focusing on the scaling properties of the distribution of returns rather than informational efficiency, and as a consequence standard asset pricing and risk management models may need to be reassessed (Onali and Goddard, 2009). Multifractality has been examined in a variety of financial time series including stock markets (Pasquini and Serva, 1999;Bouchaud et al 2000;Andreadis and Serletis, 2002;Matia, et al, 2003;Di Matteo et al 2003;Oświęcimka et al, 2005;Moyano et al, 2006;Cajueiro et al, 2009;Stavroyiannis et al, 2010;Maganini et al, 2018;;Uddin et al 2018), foreign exchange markets (Vandewalle, and Ausloos, 1998;Bershadskii, 2003;Norouzzadeh and Rahmani, 2006;Stavroyiannis et al, 2011), commodities , interest rates (Cajueiro and Tabak, 2007), emerging markets (Liu et al, 2010;Benbachir and El Alaoui, 2011;Samadder et al, 2013;Lahmiri S. 2017), and air pollution (Manimaran and Narayana, 2018). The degree of multifractality has been recently associated to the inefficiency of a market (Zunino et al, (2008), since small or emerging markets indicate richer multifractality than the developed ones, and Stavroyiannis et al (2011) using high frequency data for the Euro/JPY exchange rate, introduced the local multifractality sensitivity index as an attempt to a pre-crisis signal.…”

Multifractal characteristics and return predictability in the Chinese stock markets

“…Two stylized facts in financial series, including fat-tailed distributions and long range dependence, are considered as the sources of multifractality (Zhou, 2009;Jiang and Zhou, 2008b;Zhou, 2012;Grahovac and Leonenko, 2014). Multifractal nature in returns makes the price dynamics differ from the Brownian process, triggering the studies of applying multifractality on uncovering market efficiency (Wang and Wu, 2013;Liu et al, 2010), designing trading strategies (Dewandaru et al, 2015), constructing measures for improving volatility forecasts (Wei and Wang, 2008;Chen and Wu, 2011;Wei et al, 2013;Chen et al, 2014), and to list a few. New theoretical models including multifractal random walk (MRW) (Bacry et al, 2001), the multifractal model of asset returns (MMAR) (Calvet and Fisher, 2002), and Markov-switching multifractal model (MSM) (Calvet and Fisher, 2001) have been proposed to replicate the multi-scaling price behaviors.…”