Abstract
The primary objective of this paper is to assess the behavior of long memory in price, volume, and price-volume cross-correlation series across structural breaks. The secondary objective is to find the appropriate structural breaks in the price series. The structural breaks in the series are identified using the Bai and Perron procedure, and in each segment, Multifractal Detrended Fluctuation Analysis (MFDFA) and Multifractal Detrended Cross-Correlation Analysis (MFDCCA) are conducted to capture the long memory in each series. The price series is persistent in small fluctuations and anti-persistent in large fluctuations across all the structural segments. This confirms that long memory in the series is not affected by the structural breaks. Both volume and price-volume cross-correlation are anti-persistent in all the structural segments. In other words, volume acts as a carrier of the information only in the non-volatile (normal) market. The varying Hurst exponent across the structural segments indicates the varying levels of persistence and signifies the volatile market. The findings of the study are useful for understanding the practical implications of the Adaptive Market Hypothesis (AMH).
Highlights
Prices in an informationally efficient market have no memory and present no opportunities for investors to make excess returns for the level of risk taken (Fama 1970)
The weak form of the market efficiency is studied in terms of linear auto-correlation, unit root, and variance ratio tests in a return series (see (Gozbasi et al 2014; Kim 2009; Konak and Seker 2014)) and in terms of contemporaneous and causal studies in the price-volume relationship (e.g., (Chen 2012; Gupta and Yang 2011; Lee and Swaminathan 2000))
This paper conducts a multifractal analysis of price, volume, and price-volume cross-correlation across structural breaks
Summary
Prices in an informationally efficient market have no memory and present no opportunities for investors to make excess returns for the level of risk taken (Fama 1970). The focal point of the weak form of market efficiency is the past value of prices, trading volume (Rizvi and Arshad 2017). Since investors make their allocation decisions based on the market information, the presence of an inefficient market affects their investment horizon and portfolio allocation decisions (Lo 1991). The weak form of the market efficiency is studied in terms of linear auto-correlation, unit root, and variance ratio tests in a return series (see (Gozbasi et al 2014; Kim 2009; Konak and Seker 2014)) and in terms of contemporaneous and causal studies in the price-volume relationship (e.g., (Chen 2012; Gupta and Yang 2011; Lee and Swaminathan 2000)). Many studies have been conducted to capture the market inefficiency using this multifractal nature of the assets (Kantelhardt et al 2002; Zunino et al 2008, 2009)
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