The Econometrics of Sequential Trade Models - Theory and Applications Using High Frequency Data (Paperback, 2004 ed.)

The book inquires the consequences of speculative trading based on private information about financial asset markets. It presents an extensive and thorough discussion of theoretical and empirical methods used in previous studies on sequential trade models. The text also introduces a new framework for estimation and hypothesis testing that extends earlier work in the field substantially. Several market microstructure models in the spirit of Easley, Kiefer, O'Hara and Paperman (Journal of Finance, 1996) are reviewed. The common theme of these papers is the focus on the consequences of information based trading on the price setting behaviour of the market maker. Assuming that some traders have private information about a security's true value, a number of relations between observable quantities like the spread, the volume, timing of trades and volatility of asset prices can be established. The authors introduce a number of improved methods for estimation and hypothesis testing for sequential trade models and apply this econometric framework employing a high frequency transaction data set for a number of stocks traded on the New York Stock Exchange during August 1996. All results that are necessary for understanding the empirical framework introduced are derived step-by-step. The text is ideally suited as a reference work on old and new results as well as a textbook for graduate courses on Market Microstructure Theory, Empirical Methods in Finance or Econometrics.

General

Imprint:	Springer-Verlag
Country of origin:	Germany
Series:	Lecture Notes in Economics and Mathematical Systems, 538
Release date:	February 2004
First published:	2004
Authors:	Stefan Kokot
Dimensions:	233 x 155 x 11mm (L x W x T)
Format:	Paperback
Pages:	196
Edition:	2004 ed.
ISBN-13:	978-3-540-20814-3
Categories:	Books > Business & Economics > Economics > Econometrics > General Promotions
LSN:	3-540-20814-3
Barcode:	9783540208143

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