A Non-Stationary Model for Statistical Arbitrage Trading

2010 ◽  
Author(s):  
William Karel Bertram
Keyword(s):  
Stationary Model ◽  
Statistical Arbitrage ◽  
Arbitrage Trading

scholarly journals On cointegration and cryptocurrency dynamics

2021 ◽  
Author(s):  
Georg Keilbar ◽  
Yanfen Zhang
Keyword(s):  
Correction Model ◽  
Vector Error Correction ◽  
Statistical Arbitrage ◽  
Dynamic Setting ◽  
Vector Error ◽  
Out Of Sample ◽  
Joint Dynamics ◽  
Arbitrage Trading

AbstractThis paper aims to model the joint dynamics of cryptocurrencies in a nonstationary setting. In particular, we analyze the role of cointegration relationships within a large system of cryptocurrencies in a vector error correction model (VECM) framework. To enable analysis in a dynamic setting, we propose the COINtensity VECM, a nonlinear VECM specification accounting for a varying systemwide cointegration exposure. Our results show that cryptocurrencies are indeed cointegrated with a cointegration rank of four. We also find that all currencies are affected by these long term equilibrium relations. The nonlinearity in the error adjustment turned out to be stronger during the height of the cryptocurrency bubble. A simple statistical arbitrage trading strategy is proposed showing a great in-sample performance, whereas an out-of-sample analysis gives reason to treat the strategy with caution.

scholarly journals Optimal statistical arbitrage trading of Berkshire Hathaway stock and its replicating portfolio

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244541
Author(s):  
An-Sing Chen ◽  
Che-Ming Yang
Keyword(s):  
Transaction Costs ◽  
Factor Model ◽  
Hedge Fund ◽  
Fund Managers ◽  
Statistical Arbitrage ◽  
Model Average ◽  
Trading Model ◽  
Arbitrage Trading ◽  
A New Technique ◽  
Replicating Portfolio

In this paper, we make use of the replicating asset for statistical arbitrage trading, where the replicating asset is constructed by a portfolio that mimics the returns from a factor model. Using the replicating asset in the context of statistical arbitrage has never been done before in the literature. A novel optimal statistical arbitrage trading model is applied, and we derive the average transaction length and return for the Berkshire A stock and its replicating asset. The results show that the statistical arbitrage method proposed by Bertram (2010) is profitable by using the replicating asset. We also compute the average returns under different transaction costs. For the statistical arbitrage using the replicating asset of the factor model, average annual returns were at least 33%. Robustness is examined with the S&P500. Our results can provide hedge fund managers with a new technique for conducting statistical arbitrage.

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