Detecting Statistical Arbitrage Opportunities Using a Combined Neural Network - GARCH Model

2012 ◽  
Author(s):  
Nikos S. Thomaidis ◽  
Nick Kondakis
Keyword(s):  
Neural Network ◽  
Garch Model ◽  
Statistical Arbitrage ◽  
Arbitrage Opportunities

scholarly journals A GARCH Model with Artificial Neural Networks

Information ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 489
Author(s):  
Wing Ki Liu ◽  
Mike K. P. So
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Garch Model ◽  
Network Models ◽  
Variable Importance ◽  
Neural Network Models ◽  
Out Of Sample ◽  
Importance Analysis ◽  
Artificial Neural ◽  
T Distribution

In this paper, we incorporate a GARCH model into an artificial neural network (ANN) for financial volatility modeling and estimate the parameters in Tensorflow. Our goal was to better predict stock volatility. We evaluate the performance of the models using the mean absolute errors of powers of the out-of-sample returns between 2 March 2018 and 28 February 2020. Our results show that our modeling procedure with an ANN can outperform the standard GARCH(1,1) model with standardized Student’s t distribution. Our variable importance analysis shows that Net Debt/EBITA is among the six most important predictor variables in all of the neural network models we have examined. The main contribution of this paper is that we propose a Long Short-Term Memory (LSTM) model with a GARCH framework because LSTM can systematically take into consideration potential nonlinearity in volatility structure at different time points. One of the advantages of our research is that the proposed models are easy to implement because our proposed models can be run in Tensorflow, a Python package that enables fast and automatic optimization. Another advantage is that the proposed models enable variable importance analysis.

scholarly journals Informed traders

2008 ◽  
Vol 465 (2104) ◽  
pp. 1103-1122 ◽  
Author(s):  
Dorje C Brody ◽  
Mark H.A Davis ◽  
Robyn L Friedman ◽  
Lane P Hughston
Keyword(s):  
Mutual Information ◽  
Signal To Noise Ratio ◽  
Information Source ◽  
Asset Price ◽  
Market Participant ◽  
Asset Return ◽  
Additional Information ◽  
Statistical Arbitrage ◽  
Noisy Information ◽  
Arbitrage Opportunities

An asymmetric information model is introduced for the situation in which there is a small agent who is more susceptible to the flow of information in the market than the general market participant, and who tries to implement strategies based on the additional information. In this model market participants have access to a stream of noisy information concerning the future return of an asset, whereas the informed trader has access to a further information source which is obscured by an additional noise that may be correlated with the market noise. The informed trader uses the extraneous information source to seek statistical arbitrage opportunities, while at the same time accommodating the additional risk. The amount of information available to the general market participant concerning the asset return is measured by the mutual information of the asset price and the associated cash flow. The worth of the additional information source is then measured in terms of the difference of mutual information between the general market participant and the informed trader. This difference is shown to be non-negative when the signal-to-noise ratio of the information flow is known in advance. Explicit trading strategies leading to statistical arbitrage opportunities, taking advantage of the additional information, are constructed, illustrating how excess information can be translated into profit.

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