bibtype J - Journal Article
ARLID 0458944
utime 20240103212157.8
mtime 20160425235959.9
SCOPUS 84964422446
WOS 000382559300008
DOI 10.1080/14697688.2016.1149612
title (primary) (eng) Estimation of zero-intelligence models by L1 data
specification
page_count 23 s.
media_type P
serial
ARLID cav_un_epca*0039898
ISSN 1469-7688
title Quantitative Finance
volume_id 16
volume 9 (2016)
page_num 1423-1444
keyword Limit Order Market
keyword Stochastic Models
keyword Econometric Methods
author (primary)
ARLID cav_un_auth*0101206
full_dept (cz) Ekonometrie
full_dept (eng) Department of Econometrics
department (cz) E
department (eng) E
full_dept Department of Econometrics
share 100
name1 Šmíd
name2 Martin
institution UTIA-B
country CZ
garant K
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2016/E/smid-0458944.pdf
cas_special
project
ARLID cav_un_auth*0281000
project_id GBP402/12/G097
agency GA ČR
country CZ
abstract (eng) A unit volume zero-intelligence (ZI) model is defined and the distribution of its L1 process is recursively described. Further, a generalized ZI model allowing non-unit market orders, shifts of quotes and general in-spread events is proposed and a formula for the conditional distribution of its quotes is given, together with a formula for price impact. For both the models, MLE estimators are formulated and shown to be consistent and asymptotically normal. Consequently, the estimators are applied to data of six US stocks from nine electronic markets. It is found that more complex variants of the models, despite being significant, do not give considerably better predictions than their simple versions with constant intensities.
RIV BB
reportyear 2017
num_of_auth 1
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0260311
confidential S
mrcbC86 3+4 Article Business Finance|Economics|Mathematics Interdisciplinary Applications|Social Sciences Mathematical Methods
mrcbT16-e BUSINESSFINANCE|ECONOMICS|MATHEMATICSINTERDISCIPLINARYAPPLICATIONS|SOCIALSCIENCESMATHEMATICALMETHODS
mrcbT16-j 0.502
mrcbT16-s 0.659
mrcbT16-4 Q1
mrcbT16-B 35.687
mrcbT16-D Q3
mrcbT16-E Q2
arlyear 2016
mrcbU14 84964422446 SCOPUS
mrcbU34 000382559300008 WOS
mrcbU63 cav_un_epca*0039898 Quantitative Finance 1469-7688 1469-7696 Roč. 16 č. 9 2016 1423 1444