2008/08/12 by Kostas Triantafyllopoulos, Triantafyllopoulos, Kostas, Giovanni Montana +1
Economics, Econometrics and Finance · Mathematics · #Algorithm #Algorithmic trading #Applications (stat.AP) #Arbitrage #Capital asset pricing model #Complex Systems and Time Series Analysis #Computer science #Econometrics #Economics #FOS: Computer and information sciences #FOS: Economics and business #Finance #Financial Markets and Investment Strategies #Financial Risk and Volatility Modeling #Mathematical optimization #Mathematics #Mean reversion #Methodology (stat.ME) #Monte Carlo method #Pairs trade #Portfolio Management (q-fin.PM) #Predictability #Realization (probability) #Risk arbitrage #State space #State-space representation #Statistical Finance (q-fin.ST) #Statistical arbitrage #Statistics #Trading strategy #q-fin.PM #q-fin.ST #stat.AP #stat.ME
paper · pdf · doi:10.48550/arxiv.0808.1710
published in RePEc: Research Papers in Economics (Federal Reserve Bank of St. Louis) · 34 pages, 6 figures. Submitted
openalex publication_date 2008/08/12 · arxiv created 2009/05/19 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.