xmonths. The second group focuses on the price impact of trades. Amihud (2002) develops a price impact measure based on the daily price response associated with one dollar of trading volume. Pastor and Stambaugh (2003) focus on the temporary price change accompanying order flow and construct a Gamma measure of liquidity using a regression approach. The Amivest liquidity measure is the average ratio of volume to absolute returns. The hypothesis that various low-frequency liquidity proxies are able to capture the underlying liquidity is rarely tested until recently. Lesmond, Ogden, and Trzcinka (1999) compare their zero return measure to the sum of the proportional bid-ask spread and a representative commission (S+C). The time-series analysis shows that the zero return measure is significantly and positively correlated with the S+C measure for the time period of 1963 through 1990 for stocks listed on the NYSE/AMEX. Hasbrouck (2009) tests various measures of transaction costs estimated from both high-frequency and low-frequency data for the sample period of 1993 to 2003 for the US stock market. His results
8 indicate that the posted spreads and the effective spreads are highly correlated but price impact measures and other statistics from dynamic models are only moderately correlated with each other. The Gibbs estimator, among the set of proxies constructed from daily data, performs best with a correlation of 0.944 with the corresponding TAQ estimate. Goyenko, Holden and Trzcinka (2009) propose several new liquidity measures at both low-frequency and high-frequency levels and do a comprehensive comparison analysis of various liquidity measures using the effective spread, the realized spread and the price impact based on both TAQ and Rule 605 data as liquidity benchmarks. The results show that, during the sample period of 1993 to 2005, there is a close relationship between many of the liquidity measures constructed from the low-frequency data and the liquidity benchmarks. Their results indicate that the assumption that liquidity proxies measure liquidity generally holds. However, these studies focus on the US market which is believed to be the most liquid market in the world. There is a growing literature with the focus on liquidity in emerging markets. However, different studies use different liquidity measures.3Very little work is done on the comparison of liquidity measures in emerging markets. Lesmond (2005) uses hand-collected quarterly bid-ask quotes data and compares the bid-ask spread to low-frequency liquidity proxies such as the Roll measure, the LOT measure (see Lesmond, Ogden, and Trzcinka, 1999), the Amihud measure, the Amivest measure and turnover during the period from 1987 to 2000 for 31 emerging markets. The within-country analysis shows that bid-ask spread is significantly correlated with all the low-frequency liquidity proxies 3For example, trading volume in Bailey and Jagtiani (1994), the Amivest measure in Amihud, Mendelson and Lauterach (1997) and Berkman and Eleswarapu (1998), a variation of the Roll measure in Domowitz, Glen and Madhavan (1998), turnover in Rouwenhorst (1999) and Levine and Schmukler (2006), and the proportion of zero daily returns in Bekaert, Harvey, and Lundblad (2007) and Lee (2011).