Dynamic modeling of mean-reverting spreads for statistical arbitrage

Abstract

Statistical arbitrage strategies, such as pairs trading and its generalizations rely on the construction of mean-reverting spreads enjoying a certain degree of predictability. Gaussian linear state-space processes have recently been proposed as a model for such spreads under the assumption that the observed process is a noisy realization of some hidden states. Real-time estimation of the unobserved spread process can reveal temporary market inefficiencies which can then be exploited to generate excess returns. We embrace the state-space framework for modeling spread processes and extend this methodology along three different directions. First, we introduce time-dependency in the model parameters, which allows for quick adaptation to changes in the data generating process. Second, we provide an on-line estimation algorithm that can be constantly run in real-time. Being computationally fast, the algorithm is particularly suitable for building aggressive trading strategies based on high-frequency data and may be used as a monitoring device for mean- reversion. Finally, our framework naturally provides informative uncertainty measures of all the estimated parameters. Experimental results based on Monte Carlo simulations and historical equity data are discussed, including a co-integration relationship involving two exchange-traded funds.

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Triantafyllopoulos, K., Montana, G. Dynamic modeling of mean-reverting spreads for statistical arbitrage. Comput Manag Sci 8, 23–49 (2011). https://doi.org/10.1007/s10287-009-0105-8

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Keywords

  • Mean reversion
  • Statistical arbitrage
  • Pairs trading
  • State space model
  • Time-varying autoregressive processes
  • Dynamic regression
  • Bayesian forecasting

Mathematics Subject Classification (2000)

  • 91B84
  • 91B28
  • 62M10