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JRFM
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Agribusiness Financial Risk Management
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Agribusiness Financial Risk Management

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Applied Economics and Finance".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 12929

Special Issue Editors

Prof. Dr. William Nganje

E-Mail Website
Guest Editor
Department of Agribusiness and Applied Economics, North Dakota State University, 811 2nd Ave N, Fargo, ND 58108, USA
Interests: risk management; financial analysis; food risks (economics of obesity, food safety and food terrorism); experimental economics; and consumer choice theory
Dr. Xudong Rao

E-Mail Website
Guest Editor
Department of Agribusiness and Applied Economics, North Dakota State University, 1340 Administration Ave, Fargo, ND 58105, USA
Interests: agribusiness management; risk & uncertainty; technology adoption; international development; agri-food policy

Special Issue Information

Dear Colleagues,

The Journal of Risk and Financial Management (JRFM) is a leading scholarly international peer-reviewed journal on risk and financial management. The goal of JRFM is to enable rapid dissemination of high impact research to the scientific community. The journal is highly visible and ranked B by the ABDC—Australian Business Deans Council. It is indexed in the Emerging Sources Citation Index (ESCI - Web of Science) and other databases.

This Special Issue has the potential to make a substantial impact on an important area of research in agribusiness financial risk management. Food and agribusiness accounts for approximately 17% of the United States GDP and a significantly higher percent in most countries. This segment accounts for food security and value added all along the food supply chain, with major implications on the economic stability of a nation. Risk in this segment of the economy has evolved significantly with emerging technologies, a changing policy environment, mergers and acquisitions, and globalization. Risk and financial management are pivotal for the success or failure of firms and the economic development of a nation. The literature on risk and financial management is limited on critical emerging issues in agribusiness. Papers should include but are not limited to agribusiness topics related to advancements in methods to measure risks and financial tools to mitigate risks. These topics include, but are not limit to:

  • Predicting risks with value-at-risk measures, including joint distributions and copulas;
  • Comparing risky investment with stochastic dominance, stochastic efficiency, and convex set stochastic dominance;
  • Advancements in risk–returns measure (with CAPM and APT) and risk premiums;
  • Machine Learning and cross validation refinement of risk measures;
  • Return on agribusiness investments;
  • Credit risk management for agricultural/agribusiness banks;
  • Liquidity risk management;
  • Incentives as a response to risk;
  • Policy as a response to risk;
  • Insurance as a response to risk;
  • Diversification as a response to risk.
Prof. Dr. William Nganje
Dr. Xudong Rao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • risk measurement
  • risk management
  • agribusiness finance
  • financial reporting
  • valuing food technologies
  • data mining
  • food supply chain
  • information sharing
  • food safety
  • equity allocation strategies

Published Papers (4 papers)

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Research

14 pages, 2142 KiB  
Article
Price and Volatility Transmissions among Natural Gas, Fertilizer, and Corn Markets: A Revisit
by Zhengliang Yang, Xiaoxue Du, Liang Lu and Hernan Tejeda
J. Risk Financial Manag. 2022, 15(2), 91; https://doi.org/10.3390/jrfm15020091 - 21 Feb 2022
Cited by 10 | Viewed by 3430
Abstract
In this paper, we revisit price and volatility transmission among natural gas, fertilizer, and corn markets; an important issue was explored in previous work. An update of the results is urgently needed due to the recent enormous price volatility in the commodities, fertilizer, [...] Read more.
In this paper, we revisit price and volatility transmission among natural gas, fertilizer, and corn markets; an important issue was explored in previous work. An update of the results is urgently needed due to the recent enormous price volatility in the commodities, fertilizer, and energy markets. We followed the same methodology as previous work and used the vector error correction model and the multivariate generalized autoregressive heteroskedasticity model, but we adopted a new methodology to gather higher frequency data for fertilizer to estimate the interactions and examine the mechanisms between these market prices. Our results are consistent with previous research showing that natural gas price returns in the short-term are significantly affected by its lagged returns from itself and corn markets, and it will be affected by its lagged return sand fertilizer markets. However, we did not find a significant relationship among fertilizer, corn, and natural gas markets from May to November 2021. Moreover, the lagged conditional volatility of corn prices will affect the conditional volatility in the natural gas market but not vice versa. Full article
(This article belongs to the Special Issue Agribusiness Financial Risk Management)
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13 pages, 1360 KiB  
Article
Technology Adoption and Learning-by-Doing: The Case of Bt Cotton Adoption in China
by Guang Tian, Xiaoxue Du, Fangbin Qiao and Andres Trujillo-Barrera
J. Risk Financial Manag. 2021, 14(11), 524; https://doi.org/10.3390/jrfm14110524 - 2 Nov 2021
Cited by 1 | Viewed by 2093
Abstract
Although the benefits of genetically modified (GM) crops have been well documented, how do farmers manage the risk of new technology in the early stages of technology adoption has received less attention. We compare the total factor productivity (TFP) of cotton to other [...] Read more.
Although the benefits of genetically modified (GM) crops have been well documented, how do farmers manage the risk of new technology in the early stages of technology adoption has received less attention. We compare the total factor productivity (TFP) of cotton to other major crops (wheat, rice, and corn) in China between 1990 and 2015, showing that the TFP growth of cotton production is significantly different from all other crops. In particular, the TFP of cotton production increased rapidly in the early 1990s then declined slightly around 2000 and rose again. This pattern coincides with the adoption of Bt cotton process in China. To further investigate the decline of TFP in the early stages of Bt cotton adoption, using aggregate provincial-level data, we implement a TFP decomposition and show that the productivity of GM technology is higher, whereas the technical efficiency of GM technology is lower than that of traditional technologies. Especially, Bt cotton exhibited lower technical efficiency because farmers did not reduce the use of pesticide when they first started to adopt Bt cotton. In addition, we illustrate the occurrence of a learning process as GM technology diffuses throughout China: after farmers gain knowledge of Bt cotton, pesticide use declines and technical efficiency improves. Full article
(This article belongs to the Special Issue Agribusiness Financial Risk Management)
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17 pages, 1027 KiB  
Article
Stochastic Analysis and Neural Network-Based Yield Prediction with Precision Agriculture
by Humayra Shoshi, Erik Hanson, William Nganje and Indranil SenGupta
J. Risk Financial Manag. 2021, 14(9), 397; https://doi.org/10.3390/jrfm14090397 - 25 Aug 2021
Cited by 3 | Viewed by 2226
Abstract
In this paper, we propose a general mathematical model for analyzing yield data. The data analyzed in this paper come from a characteristic corn field in the upper midwestern United States. We derive expressions for statistical moments from the underlying stochastic model. Consequently, [...] Read more.
In this paper, we propose a general mathematical model for analyzing yield data. The data analyzed in this paper come from a characteristic corn field in the upper midwestern United States. We derive expressions for statistical moments from the underlying stochastic model. Consequently, we illustrate how a particular feature variable contributes to the statistical moments (and in effect, the characteristic function) of the target variable (i.e., yield). We also analyze the data with neural network techniques and provide two methods of data analysis. This mathematical model and neural network-based data analysis allow for better understanding of the variability within the data set, which is useful to farm managers attempting to make current and future decisions using the yield data. Lenders and risk management consultants may benefit from the insights of this mathematical model and neural network-based data analysis regarding yield expectations. Full article
(This article belongs to the Special Issue Agribusiness Financial Risk Management)
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18 pages, 3637 KiB  
Article
Bitcoin Return Volatility Forecasting: A Comparative Study between GARCH and RNN
by Ze Shen, Qing Wan and David J. Leatham
J. Risk Financial Manag. 2021, 14(7), 337; https://doi.org/10.3390/jrfm14070337 - 20 Jul 2021
Cited by 12 | Viewed by 4128
Abstract
One of the notable features of bitcoin is its extreme volatility. The modeling and forecasting of bitcoin volatility are crucial for bitcoin investors’ decision-making analysis and risk management. However, most previous studies of bitcoin volatility were founded on econometric models. Research on bitcoin [...] Read more.
One of the notable features of bitcoin is its extreme volatility. The modeling and forecasting of bitcoin volatility are crucial for bitcoin investors’ decision-making analysis and risk management. However, most previous studies of bitcoin volatility were founded on econometric models. Research on bitcoin volatility forecasting using machine learning algorithms is still sparse. In this study, both conventional econometric models and a machine learning model are used to forecast the bitcoin’s return volatility and Value at Risk. The objective of this study is to compare their out-of-sample performance in forecasting accuracy and risk management efficiency. The results demonstrate that the RNN outperforms GARCH and EWMA in average forecasting performance. However, it is less efficient in capturing the bitcoin market’s extreme events. Moreover, the RNN shows poor performance in Value at Risk forecasting, indicating that it could not work well as the econometric models in explaining extreme volatility. This study proposes an alternative method of bitcoin volatility analysis and provides more motivation for economic researchers to apply machine learning methods to the less volatile financial market conditions. Meanwhile, it also shows that the machine learning approaches are not always more advanced than econometric models, contrary to common belief. Full article
(This article belongs to the Special Issue Agribusiness Financial Risk Management)
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