Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/286865 
Year of Publication: 
2021
Citation: 
[Journal:] Decisions in Economics and Finance [ISSN:] 1129-6569 [Volume:] 44 [Issue:] 2 [Publisher:] Springer International Publishing [Place:] Cham [Year:] 2021 [Pages:] 1063-1085
Publisher: 
Springer International Publishing, Cham
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.
Subjects: 
Classification
Clustering
Commodities
Hybrid model
Machine learning
Stochastic price model
JEL: 
C53
G13
Q11
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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