Predicting Liquidity from Order Book Data

2004 ◽  
Author(s):  
Knut Griese ◽  
Alexander Kempf
Keyword(s):  
Order Book

A multilayer model of order book dynamics

2016 ◽  
Vol 2 (3) ◽  
pp. 37-52 ◽  
Author(s):  
Alessio Emanuele Biondo ◽  
Alessandro Pluchino ◽  
Andrea Rapisarda
Keyword(s):  
Order Book ◽  
Multilayer Model

Volatility Regimes and the Provision of Liquidity in Order Book Markets

2004 ◽  
Author(s):  
Helena M. Beltran-Lopez ◽  
Alain C. J. Durré ◽  
Pierre Giot
Keyword(s):  
Order Book ◽  
Volatility Regimes ◽  
Book Markets

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

scholarly journals Temporal mixture ensemble models for probabilistic forecasting of intraday cryptocurrency volume

2021 ◽  
Author(s):  
Nino Antulov-Fantulin ◽  
Tian Guo ◽  
Fabrizio Lillo
Keyword(s):  
Econometric Models ◽  
Gradient Boosting ◽  
Point Estimate ◽  
Order Book ◽  
Probabilistic Forecasting ◽  
Short Term ◽  
Ensemble Models ◽  
Gradient Boosting Machine ◽  
Different Sources ◽  
Better Than

AbstractWe study the problem of the intraday short-term volume forecasting in cryptocurrency multi-markets. The predictions are built by using transaction and order book data from different markets where the exchange takes place. Methodologically, we propose a temporal mixture ensemble, capable of adaptively exploiting, for the forecasting, different sources of data and providing a volume point estimate, as well as its uncertainty. We provide evidence of the clear outperformance of our model with respect to econometric models. Moreover our model performs slightly better than Gradient Boosting Machine while having a much clearer interpretability of the results. Finally, we show that the above results are robust also when restricting the prediction analysis to each volume quartile.

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