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A functional time series analysis of forward curves derived from commodity futures

Lajos Horvath, Zhenya Liu, Gregory Rice and Shixuan Wang ()

International Journal of Forecasting, 2020, vol. 36, issue 2, 646-665

Abstract: We study forward curves formed from commodity futures prices listed on the Standard and Poor’s-Goldman Sachs Commodities Index (S&P GSCI) using recently developed tools in functional time series analysis. Functional tests for stationarity and serial correlation suggest that log-differenced forward curves may be generally considered as stationary and conditionally heteroscedastic sequences of functions. Several functional methods for forecasting forward curves that more accurately reflect the time to expiry of contracts are developed, and we found that these typically outperformed their multivariate counterparts, with the best among them using the method of predictive factors introduced by Kargin and Onatski (2008).

Keywords: Forward curves; S&P GSCI; Commodity futures; Functional data analysis; Functional autoregressive models; Functional principal component analysis (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:36:y:2020:i:2:p:646-665

DOI: 10.1016/j.ijforecast.2019.08.003

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