Forecasting commodity returns by exploiting climate model forecasts of the El Niño Southern Oscillation

Abstract

The physical and socioeconomic environments in which we live are intrinsically linked over a wide range of time and space scales. On monthly intervals, the price of many commodities produced predominantly in tropical regions covary with the dominant mode of climate variability in this region, namely the El Niño Southern Oscillation (ENSO). Here, for the spot prices returns of vegetable oils produced in Asia, we develop autoregressive (AR) models with exogenous ENSO indices, where for the first time these indices are generated by a purpose-built state-of-the-art general circulation model (GCM) climate forecasting system. The GCM is a numerical simulation which couples together the atmosphere, ocean, and sea ice, with the initial conditions tailored to maximize the climate forecast skill at multiyear timescales in the tropics. To serve as additional benchmarks, we also test commodity forecasts using: (a) no ENSO information as a lower bound; (b) perfect future ENSO knowledge as a reference upper bound; and (c) an econometric AR model of ENSO. All models adopting ENSO factors outperform those that do not, indicating the value here of incorporating climate knowledge into investment decision-making. Commodity forecasts adopting perfect ENSO factors have statistically significant skill out to 2 years. When adopting the GCM-ENSO factors, there is predictive power of the commodity beyond 1 year in the best case, which consistently outperforms commodity forecasts adopting an AR econometric model of ENSO.

Recent applications and potential of near-term (interannual to decadal) climate predictions

“…In addition, there are documented relationships between ENSO and movements in commodity prices (Brunner, 2002;Ubilava and Holt, 2009), agricultural production (Gutierrez, 2017), and broader macroeconomic indicators (Cashin et al, 2017). In that regard, Kitsios et al (2022), have demonstrated that econometric forecasts of certain commodity spot prices can be further improved with the inclusion of climate predictions of the relevant indices of climate variability i.e., exogenous factors. Here the highlighted commodity is coconut oil, and the exogenous factor is the Niño4 index (Rasmusson and Carpenter, 1982) provided by climate simulations.…”

“…ENSO forecast data was generated using the Climate re-Analysis and Forecast Ensemble (CAFE) system (O' Kane et al, 2019Kane et al, , 2020Kane et al, , 2021a. The model representation of the logreturns of the commodity price allows for additive seasonality, autoregressive processes, and lagged exogenous ENSO (Niño4) factors (for details see Kitsios et al, 2022). The autoregressive models are built using available data from January 1980 to December 2020, with all combinations of lags assessed up to a lag of 12 months.…”

Impact Of Droughts On Financial Markets And The Economy: Evidence From Dairy, Sheep And Beef Farming

“…Further discussions on auto-correlation and alternative model test results are presented in section 2.9.2 Appendix -Tables. We took guidance from Kitsios, De Mello, and Matear (2022) to test our alternative model. 3 GDP implicit deflator shows the rate of price change in the economy as a whole measured as nominal GDP divided by real GDP and multiplied by 100.…”