Abstract
In recent times of noticeable climate change the consideration of external factors, such as weather and economic key figures, becomes even more crucial for a proper valuation of derivatives written on agricultural commodities. The occurrence of remarkable price changes as a result of severe changes in these factors motivates the introduction of different price states, each describing different dynamics of the price process. In order to include external factors we propose a two-step hybrid model based on machine learning methods for clustering and classification. First, we assign price states to historical prices using K-means clustering. These price states are also assigned to the corresponding data of external factors. Second, predictions of future price states are then obtained from short-term predictions of the external factors by means of either K-nearest neighbors or random forest classification. We apply our model to real corn futures data and generate price scenarios via a Monte Carlo simulation, which we compare to Sørensen (J Futures Mark 22(5):393–426, 2002). Thereby we obtain a better approximation of the real futures prices by the simulated futures prices regarding the error measures MAE, RMSE and MAPE. From a practical point of view, these simulations can be used to support the assessment of price risks in risk management systems or as decision support regarding trading strategies under different price states.
Highlights
In recent years the consequences of severe natural and economic incidents have become more noticeable for the cultivation of agricultural commodities
For the prediction of price states we focus on the following external factors, which are commonly considered for the pricing of agricultural commodity futures (Geman and Nguyen 2005; Garcia et al 1997; Karali 2012; Karali et al 2019)
We have proposed a two-step hybrid model that incorporates information on external factors into the simulation of futures prices on corn
Summary
In recent years the consequences of severe natural and economic incidents have become more noticeable for the cultivation of agricultural commodities. Due to the nonlinear relation between price-driving factors and the price process, linear time series models are not able to fully capture the complex relations For this purpose, hybrid approaches that combine classical statistical models with methods of machine learning have been developed and have recently gained attention in. Based on historical data on futures prices and a selection of their price-driving factors, our approach allows for a characterization of different price behavior and development into different price states To make this more precise we explain its three major components as given in Fig. 1: 1. For a rigorous explanation of the parameter estimation technique using the Kalman filter we refer the reader to Harvey (1990)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
