Forecasting and Trading Commodity Contract Spreads with Gaussian Processes

Gaussian Processes are general statistical models for nonlinear regression and classification that have recently received wide attention in the machine learning community, having originally been introduced in geostatistics (where they are known under the name “Kriging”.) They differ from neural networks in that they engage in a full Bayesian treatment, supplying a complete posterior distribution of forecasts. For regression, they are also computationally relatively simple to implement, the basic model requiring only solving a system of linear equations, albeit one of size equal to the number of training examples (requiring O(N3) computation). This paper examines the use Gaussian Processes to forecast the evolution of futures contracts spreads arising on the commodities markets. Contrarily to most forecasting techniques which rely on modeling the short-term dynamics of a time series (e.g. arima and most neural-network models), an appropriate representation of the input and target variables allows the Gaussian Process to forecast the complete future trajectory of the spread. Furthermore, as a customary outcome of using Gaussian Processes, the forecast includes not only the expectation of future spread prices (across time-steps), but their joint autocovariance matrix as well. We introduce a technique to exploit this joint autocovariance matrix in order to profitably trade spreads, based on maximizing an information ratio criterion between candidate entry-exit points and constantly monitoring the position with revised forecasts as the spread realization unfolds. This approach results in a qualitatively very different methodology than a classical mean-variance portfolio construction based on short-term forecasts, yielding models that do not overtrade yet react quickly to changes in market conditions. We present simultation results on historical data to validate the approach.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.