Dual Long Memory in Stock Market Prices: New Evidence Based on Bull and Bear Markets

By Tan, Siow-Hooi; Ong, Hway-Boon et al. | IUP Journal of Applied Economics, July 2013 | Go to article overview

Dual Long Memory in Stock Market Prices: New Evidence Based on Bull and Bear Markets


Tan, Siow-Hooi, Ong, Hway-Boon, Khong, Roy-Wye-Leong, IUP Journal of Applied Economics


This study examines the dual long memory properties in Malaysian stock market during bull and bear periods for the period 1993:10 to 2009:12. Both semi-parametric and ARFIMA-FIGARCH models are applied for the diagnosis of long memory. The study finds no evidence of long memory for stock returns. On the contrary the long memory in stock volatility for all the bear periods and three of the bull periods is supported. Besides, the long-range dependence is more persistent in the early years of the sample, in particular, prior to the imposition of capital controls by the Malaysian government in September 1998. The presence of long memory in volatility provides evidence against the efficient market hypothesis and thus offers arbitrage opportunities to reap excess profits in stock market.

(ProQuest: ... denotes formulae omitted.)

Introduction

After a series of bad news which led to the plunging of the US stock price index from 13,930 points (October 2007) to as low as 9,325 points (October 2008), many stock markets worldwide, then, also experienced a downturn shift. As a consequence, individual and institutional investors attempted to investigate the behavior of stock return series, particularly the long memory property.

Long memory property indicates not only the violation of the market efficiency, but its implications on technical trading rules. The weak form of the Efficient Market Hypothesis (EMH) asserts that the stock market is efficient based on past prices information (Fama, 1970) and thus, stock price should follow a 'random walk' or a process with no memory. Long memory property, on the contrary, indicates that the arrival of new market information cannot be fully arbitraged away (Mandelbrot, 1971 ; Granger and Joyeux, 1980; and Hosking, 1981). In other words, considering that what happens today may affect the future, if the information is fully utilized, there is a possibility to outperform the market and make consistent speculative profits. Therefore, the validity of EMH is questionable when current data is correlated with all past data in varying degrees.

Technical trading rule is built on the conception that the price movements follow a trend and are not random; thus, the movements are often predictable. Moving average, in particular, is one of the popular technical trading tools used by institutional investors and practitioners when proposing profitable strategies. With the presence of long memory component in stock prices, a higher-order moving average trading rule can be recommended to gain abnormal profits in the stock market.

Due to the significant implications of long memory in theory and practices, several methods have been developed to estimate the long memory property. Rescaled range or modified rescaled range statistics (Higuchi, 1988; and Lo, 1991), semi-parametric models (Geweke and Porter-Hudak, 1983 ; and Robinson, 1995), Autoregressive Fractional Integrated Moving Average, ARFIMA(p, d, q) and Autoregressive Conditional Heteroscedasticity (ARCH) or Generalized ARCH (GARCH) types of related models (Sowell, 1992; and Tanaka, 1999), are the existing methods used to diagnose the long memory dependency of data. Examples of studies using these methods to address the long-term dependency in returns series are, among many others, Lo (1991), Mills (1993), Lobato and Savin (1998), Tolvi (2003), Caporale and Gil-Alana (2004), Gil-Alana (2006), and Lux and Kaizoji (2007). While Lo (1991), Lobato and Savin (1998) as well as Caporale and Gil-Alana (2004) found no evidence of long-term dependency, the other researchers did.

Apart from assessing long memory in stock returns, many stock market researchers and practitioners have, recently, examined the presence of long memory in stock volatility. These existing studies on long memory in stock volatility were derived largely from the work of Baillie et al. (1996). In the literature, squared or absolute returns are commonly used as proxies for stock volatility. …

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