scholarly journals Studies of the limit order book around large price changes

2009 ◽  
Vol 71 (4) ◽  
pp. 499-510 ◽  
Author(s):  
B. Tóth ◽  
J. Kertész ◽  
J. D. Farmer
Keyword(s):  
Limit Order Book ◽  
Order Book ◽  
Limit Order ◽  
Price Changes

GENERAL SEMI-MARKOV MODEL FOR LIMIT ORDER BOOKS

2017 ◽  
Vol 20 (03) ◽  
pp. 1750019 ◽  
Author(s):  
ANATOLIY SWISHCHUK ◽  
TYLER HOFMEISTER ◽  
KATHARINA CERA ◽  
JULIA SCHMIDT
Keyword(s):  
Markov Model ◽  
Arbitrary Number ◽  
Limit Order Book ◽  
Numerical Results ◽  
Order Book ◽  
Limit Order ◽  
Price Changes ◽  
Diffusion Limits ◽  
Order Books ◽  
Limit Order Books

The paper considers a general semi-Markov model for limit order books with two states that incorporates price changes that are not fixed to one tick. Furthermore, we introduce an even more general case of the semi-Markov model for limit order books that incorporates an arbitrary number of states for the price changes. For both cases, the justifications, diffusion limits, implementations and numerical results are presented for different limit order book data: Apple, Amazon, Google, Microsoft, Intel on 21 June 2012 and Cisco, Facebook, Intel, Liberty Global, Liberty Interactive, Microsoft, Vodafone from 3 November 2014 to 7 November 2014.

Public Information Arrivals, Limit Order Book, and Stock Price Changes

2011 ◽  
Author(s):  
Huong Giang (Lily) Nguyen ◽  
Fariborz Moshirian ◽  
Peter K. Pham
Keyword(s):  
Stock Price ◽  
Public Information ◽  
Limit Order Book ◽  
Order Book ◽  
Limit Order ◽  
Price Changes

THE KEY ROLE OF LIQUIDITY FLUCTUATIONS IN DETERMINING LARGE PRICE CHANGES

2005 ◽  
Vol 05 (02) ◽  
pp. L209-L216 ◽  
Author(s):  
FABRIZIO LILLO ◽  
J. DOYNE FARMER
Keyword(s):  
Long Memory ◽  
Stock Exchange ◽  
Limit Order Book ◽  
Order Book ◽  
Financial Assets ◽  
Fat Tails ◽  
Limit Order ◽  
Price Changes ◽  
Long Memory Processes

Recent empirical analyses have shown that liquidity fluctuations are important for understanding large price changes of financial assets. These liquidity fluctuations are quantified by gaps in the order book, corresponding to blocks of adjacent price levels containing no quotes. Here we study the statistical properties of the state of the limit order book for 16 stocks traded at the London Stock Exchange (LSE). We show that the time series of the first three gaps are characterized by fat tails in the probability distribution and are described by long memory processes.

scholarly journals Temporal logistic neural Bag-of-Features for financial time series forecasting leveraging limit order book data

2020 ◽  
Vol 136 ◽  
pp. 183-189 ◽  
Author(s):  
Nikolaos Passalis ◽  
Anastasios Tefas ◽  
Juho Kanniainen ◽  
Moncef Gabbouj ◽  
Alexandros Iosifidis
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Limit Order Book ◽  
Time Series Forecasting ◽  
Order Book ◽  
Limit Order ◽  
Bag Of Features ◽  
Financial Time

Benchmark Dataset for Short-Term Market Prediction of Limit Order Book in China Markets

2021 ◽  
pp. jfds.2021.1.074
Author(s):  
Charles Huang ◽  
Weifeng Ge ◽  
Hongsong Chou ◽  
Xin Du
Keyword(s):  
Limit Order Book ◽  
Benchmark Dataset ◽  
Order Book ◽  
Short Term ◽  
Limit Order
Sign in / Sign up

Export Citation Format

Share Document