LOBSTER: high-frequency, easy-to-use and latest limit order book data for your research.
Lobster Data Demo Codes | January 23, 2020
Great news! Demo codes on processing lobster data by using various programming languages are available in Internet.
Many thanks to the contributors.go to blog.
Estimation of the discontinuous leverage effect: Evidence from the NASDAQ order book | February 14, 2019
Markus Bibinger from the University of Marburg, Christopher Neely from the Federal Reserve Bank of St. Louis and Lars Winkelmann from Free University Berlin published a paper using LOBSTER data. It is titled Estimation of the discontinuous leverage effect: Evidence from the NASDAQ order book and is forthcoming in the Journal of Econometrics.
Abstract: An extensive empirical literature documents a generally negative relation, named the “leverage effect,” between asset returns and changes of volatility. It is more challenging to establish such a return–volatility relationship for jumps in high-frequency data. We propose new nonparametric methods to assess and test for a discontinuous leverage effect — i.e. a covariation between contemporaneous jumps in prices and volatility. The methods are robust to market microstructure noise and build on a newly developed price-jump localization and estimation procedure. Our empirical investigation of six years of transaction data from 320 NASDAQ firms displays no unconditional negative covariation between price and volatility cojumps. We show, however, that there is a strong and significant discontinuous leverage effect if one conditions on the sign of price jumps and whether the price jumps are market-wide or idiosyncratic.
You can find the article here.go to blog.
Information of Liquidity Deeply in the Order Book | March 12, 2018
Although LOBSTER has the capability to generate the entire limit order book, it is currently restricted to a maximum of 200 quote level. As a result a frequently asked question is “Why can’t I generate the whole book?” Besides the data size consideration, the main rational behind this decision is that the liquidity deeply in the book is not likely to be informative.
First, algorithmic traders are not likely to react to deep liquidity, because:
Second, the lower frequency traders are incapable to rationally react to deep liquidity, since
Third, the deep liquidity in book is typically from uninformative sources:
The the above screen shot from Ivo Zeba’s LOBSTER visualisation tool shows the liquidity distribution and price dynamics over a period.
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