Testing and Modelling Market Microstructure Effects with an Application to the Dow Jones Industrial Average

摘要: It is a well accepted fact that stock returns data are often contaminated by market microstructure effects, such as bid-ask spreads, liquidity ratios, turnover, and asymmetric information. This particularly relevant when dealing with high frequency data, which used to compute model free measures of volatility, realized volatility. In this paper we suggest two test statistics. The first for the null hypothesis no noise. If rejected, proceed perform noise variance independent sampling at recorded. We provide empirical evidence based on stocks included in Dow Jones Industrial Average, period 1997-2002. Our findings that, while presence induces severe bias estimating volatility using grows less than linearly number intraday observations.