Copula-based agricultural conditional value-at-risk modelling for geographical diversifications in wheat farming portfolio management

Nguyen-Huy, Thong and Deo, Ravinesh C. and Mushtaq, Shahbaz and Kath, Jarrod and Khan, Shahjahan (2018) Copula-based agricultural conditional value-at-risk modelling for geographical diversifications in wheat farming portfolio management. Weather and Climate Extremes, 21. pp. 76-89. ISSN 2212-0947

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An agricultural producer's crop yield and subsequent farming revenues are affected by many complex factors, including price fluctuations, government policy and climate (e.g., rainfall and temperature) extremes. Geographical diversification is identified as a potential farmer adaptation and decision support tool that could assist producers to reduce unfavourable financial impacts due to variabilities in crop price and yield, associated with climate variations. There has been limited research performed on the effectiveness of this strategy. The paper proposed a new statistical approach to investigate whether the geographical spread of wheat farm portfolios across three climate broad-acre (i.e., rain-fed) zones could potentially reduce financial risks for producers in Australian agro-ecological zones. A suite of popular and statistically robust tools applied in finance based on well-established statistical theories, comprised of the Conditional Value-at-Risk (CVaR) and the joint copula model were employed to evaluate the effectiveness geographical diversification. CVaR is utilised to benchmark the loss (i.e., downside risk), while the copula function is employed to model joint distribution among marginal returns (i.e., profit in each zone). The mean-CVaR optimisations indicate that geographical diversification could be a feasible agricultural risk management approach for wheat farm portfolio managers in achieving their optimised expected returns while controlling the risks (i.e., targeting levels of risk). Further, in this study, the copula-based mean-CVaR model is seen to better simulate extreme losses compared to the conventional multivariate-normal models, which underestimate the minimum risk levels at a given target of expected return. Among the suite of tested copula-based models, the vine copula in this study is found to be a superior in capturing the tail dependencies compared to the other multivariate copula models investigated.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( The project was financed by University of Southern Queensland Post Graduate Research Scholarship [USQPRS 2015-2018]; School of Agricultural, Computational and Environmental Sciences and Drought and Climate Adaptation [DCAP] Projects (Producing Enhanced Crop Insurance Systems and Associated Financial Decision Support Tools) under Principal Supervision of Dr Ravinesh Deo, and Associate Supervision of A/Prof Shahbaz Mushtaq, Prof Shahjahan Khan and Dr Jarrod Kath.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 20 Sep 2018 02:48
Last Modified: 11 Mar 2019 01:42
Uncontrolled Keywords: copula models; portfolio optimisation; conditional value-at-risk; agriculture management; crop decision; geographical diversification
Fields of Research : 07 Agricultural and Veterinary Sciences > 0701 Agriculture, Land and Farm Management > 070105 Agricultural Systems Analysis and Modelling
04 Earth Sciences > 0401 Atmospheric Sciences > 040105 Climatology (excl.Climate Change Processes)
07 Agricultural and Veterinary Sciences > 0703 Crop and Pasture Production > 070302 Agronomy
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences
Identification Number or DOI: 10.1016/j.wace.2018.07.002

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