期刊名称：Academy of Accounting and Financial Studies Journal
印刷版ISSN：1096-3685
出版年度：2018
卷号：22
期号：2
页码：1-11
语种：English
出版社：The DreamCatchers Group, LLC
摘要：Liquidity is an important consideration in any investment decision as investors normally claim liquidity premium when they expect the liquidity options for their investment may affect their investment return. The seminal work of Amihud and Mendelson (1986) attributed towards building a consensus on the relationship between illiquid assets and return premium. Liquidity is exigent in financial market-related research and an important consideration in asset pricing. In general parlance, the liquidity of a financial asset is its ability to be traded quickly without distorting the market price. A liquid market is the one which has a large number of buyers and sellers with their buy and sells orders and price, the cost of the transaction is minimal, and price volatility is low. Liquidity is a multidimensional concept, and it changes with asset class and type of markets. The tightness (low bid-ask spread or transaction cost), immediacy (speed of execution of order indirectly measuring the efficiency of the system), breadth (presence of ample voluminous orders), depth (ability to withstand large orders without price impact with volumes of pending buy and sell orders) and resiliency (ability to bounce back from temporary distortion in price and orders) of a financial market are often studied and analysed for explaining liquidity. These dimensions are overlapping to some extent and proxies used in empirical studies to measure them often measure them jointly. The liquidity of financial markets around the globe substantially varied over time, and the uncertainty and volatility of market liquidity is an important source of risk for investors. The issue of liquidity predictability in Indian stock market is explored through the existence of long memory or long-range dependence in multiple dimensions of liquidity. Long range dependence suggests nonlinear structure in the underlying data series. Such long-range dependence structure indicates that the parameter represented by the data series can be predicted. In the presence of long memory, application of standard linear econometric techniques for modelling and forecasting is challenged as it may lead to biased inferences. The impression of unpredictable stock returns is deep-rooted and long-standing in the world of finance. It means that stock returns do not exhibit long memory and no pattern can be extracted from the behaviour of stock prices that can be used for forecasting and to formulate trading strategies for abnormal gain. The burgeoning work on the existence of long memory in returns and volatility of financial markets is well documented in Bhattacharya and Bhattacharya (2012, 2013) and Hull and McGroarty (2014). However, similar studies on the liquidity of stock markets are limited, especially in the context of emerging countries..
