BFS 2002

Contributed Talk

Using trade and quote data for Toronto Stock Exchange listed securities, this paper employs nonparametric estimation to measure the effect of being interlisted on a US exchange on the percentage bid-ask spread (and other trading properties). Unlike previous studies, I use kernel-based matching estimates in addition to variants of the standard nearest-neighbor approach for constructing matched samples of interlisted stocks and non-interlisted stocks. I explore the sensitivity of results to: (i) using different bandwidth parameters and caliper-matching criteria; (ii) using different matching characteristics; (iii) the exclusion/inclusion of firms. I highlight instances when kernel-based and nearest-neighbor matching estimation techniques produce significantly different results.       http://www.ismacentre.rdg.ac.uk/pdf/discussion/DP2001-11.pdf