Dynamic modeling of mean-reverting spreads for statistical arbitrage

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. Building on previous work, 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.Comment: 34 pages, 6 figures. Submitte

Assessing the predictive power of financial spreads in the euro area: does parameters instability matter?

Financial spreads, Bayesian VAR models, Bayesian analysis, Forecasting, C11, C32, C53,

Analysing shock transmission in a data-rich environment: a large BVAR for New Zealand

Bayesian VAR, Impulse responses, C11, C13, C33, C53,

DSGE priors for BVAR models

Similar to Ingram and Whiteman (J Monet Econ 34:497–510, 1994), De Jong et al. (in: Proceedings of the American Statistical Association Bayesian, 1993) and Negro and Schorfheide (Int Econ Rev 45:643–673, 2004) , this study proposes a methodology of constructing dynamic stochastic general equilibrium (DSGE) consistent prior distributions for Bayesian vector autoregressive (BVAR) models. The moments of the assumed Normal–Inverse–Wishart (no conjugate) prior distribution of the VAR parameter vector are derived using the results developed by Fernandez-Villaverde et al. (Am Econ Rev 97(1):21–26, 2007) , Christiano et al. (Assessing structural vars, 2006) and Ravenna (J Monet Econ 54(2):48–64, 2007) regarding structural VAR (SVAR) models and the normal prior density of the DSGE parameter vector. In line with the results from previous studies, BVAR models with theoretical priors seem to achieve forecasting performance that is comparable—if not better—to the one obtained using theory free ‘Minnesota’ priors (Doan, Econ Rev 3(1):1–100, 1984). Additionally, the marginal-likelihood of the time-series model with theory found priors—derived from the output of the Gibbs sampler—can be used to rank competing DSGE theories that aim to explain the same observed data (Geweke, Contemporary Bayesian econometrics and statistics, 2005). Finally, motivated by the work of Christiano et al. (Handbook of monetary economics, 2010a; Involuntary unemployment and the business cycle, 2010b) and Del Negro and Schorfheide (Int Econ Rev 45:643–673, 2004), we use the theoretical results developed by Chernozhukov and Hong (J Econom 115(2):293–346, 2003) and Theodoridis (An efficient minimum distance estimator for DSGE models, 2011) to derive the quasi-Bayesian posterior distribution of the DSGE parameter vector