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Essays on Analyzing Agricultural Forecasts and Banking
Essays on Analyzing Agricultural Forecasts and Banking
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211153104
- ISBN
- 9798384092216
- DDC
- 630
- 저자명
- Ding, Kexin.
- 서명/저자
- Essays on Analyzing Agricultural Forecasts and Banking
- 발행사항
- [Sl] : The Ohio State University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 103 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
- 주기사항
- Advisor: Katchova, Ani L.
- 학위논문주기
- Thesis (Ph.D.)--The Ohio State University, 2024.
- 초록/해제
- 요약In the agricultural sector, forecasts are vital for producers, commodity and financial market participants, and researchers. My three dissertation essays focus on agricultural forecast and agricultural finance analysis, including the ex post evaluation and forecasting modeling.The first essay conducts optimality tests on the World Agricultural Supply and Demand Estimates (WASDE) forecasts for corn, soybeans, and wheat published from 1988 to 2019. Motivated by the long-lasting debate on whether WASDE forecasts are optimal, we employ an unknown loss method for ex post evaluation, which assumes that the United States Department of Agriculture (USDA) forecasters' loss function is unknown. Our results suggest that USDA forecasters generally realize optimality during the data-generating process. Our findings are consistent with previous studies when narrowing down the more general unknown loss function to the symmetric or asymmetric loss function which assumes a specific shape for the loss function. This study provides implications, based on the unknown loss function, that the USDA forecasters can boost their information set as an alternative way to improve the WASDE forecasts.The second essay aims to investigate the factors affecting the net interest income of U.S. agricultural banks, using data collected from the Uniform Bank Performance Report (UBPR). Several problems emerge along with the non-agricultural lending occurring in agricultural areas, drawing significant attention from market participants and researchers. The role of banks in the agricultural areas is decisive for financial stabilization. Their net interest income is fundamental for driving profitability and managing risk. Our results suggest that the real positive effect of agricultural loans on the net interest income may be long underestimated. Additionally, low liquidity eliminates the positive effect of loans on the net interest income.The third essay explores the performance of the innovative deep learning methods for forecasting the net interest income. Financial forecasting in banking becomes a crucial ingredient of decision-making to comply with regulations and governance. Particular priority was given to risk management when concentrating on agricultural banking. Although there are long-lasting concerns about applying deep learning in agricultural finance management, its contributions to banks' profitability and stability are significant. Our results document the deep learning method as complementary to the traditional linear model to achieve predictability and maintain interpretability. Additionally, a higher demand for loans, arising from the decline in farm income, may dampen the sustainability of agricultural banking due to increased credit risk.
- 일반주제명
- Agriculture
- 키워드
- Financial market
- 기타저자
- The Ohio State University Agricultural Environmental and Developmental Economics
- 기본자료저록
- Dissertations Abstracts International. 86-04B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■1001 ▼aDing, Kexin.
■24510▼aEssays on Analyzing Agricultural Forecasts and Banking
■260 ▼a[Sl]▼bThe Ohio State University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a103 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-04, Section: B.
■500 ▼aAdvisor: Katchova, Ani L.
■5021 ▼aThesis (Ph.D.)--The Ohio State University, 2024.
■520 ▼aIn the agricultural sector, forecasts are vital for producers, commodity and financial market participants, and researchers. My three dissertation essays focus on agricultural forecast and agricultural finance analysis, including the ex post evaluation and forecasting modeling.The first essay conducts optimality tests on the World Agricultural Supply and Demand Estimates (WASDE) forecasts for corn, soybeans, and wheat published from 1988 to 2019. Motivated by the long-lasting debate on whether WASDE forecasts are optimal, we employ an unknown loss method for ex post evaluation, which assumes that the United States Department of Agriculture (USDA) forecasters' loss function is unknown. Our results suggest that USDA forecasters generally realize optimality during the data-generating process. Our findings are consistent with previous studies when narrowing down the more general unknown loss function to the symmetric or asymmetric loss function which assumes a specific shape for the loss function. This study provides implications, based on the unknown loss function, that the USDA forecasters can boost their information set as an alternative way to improve the WASDE forecasts.The second essay aims to investigate the factors affecting the net interest income of U.S. agricultural banks, using data collected from the Uniform Bank Performance Report (UBPR). Several problems emerge along with the non-agricultural lending occurring in agricultural areas, drawing significant attention from market participants and researchers. The role of banks in the agricultural areas is decisive for financial stabilization. Their net interest income is fundamental for driving profitability and managing risk. Our results suggest that the real positive effect of agricultural loans on the net interest income may be long underestimated. Additionally, low liquidity eliminates the positive effect of loans on the net interest income.The third essay explores the performance of the innovative deep learning methods for forecasting the net interest income. Financial forecasting in banking becomes a crucial ingredient of decision-making to comply with regulations and governance. Particular priority was given to risk management when concentrating on agricultural banking. Although there are long-lasting concerns about applying deep learning in agricultural finance management, its contributions to banks' profitability and stability are significant. Our results document the deep learning method as complementary to the traditional linear model to achieve predictability and maintain interpretability. Additionally, a higher demand for loans, arising from the decline in farm income, may dampen the sustainability of agricultural banking due to increased credit risk.
■590 ▼aSchool code: 0168.
■650 4▼aAgriculture
■653 ▼aFinancial market
■653 ▼aFinancial forecasting
■653 ▼aAgricultural banking
■690 ▼a0501
■690 ▼a0473
■690 ▼a0770
■71020▼aThe Ohio State University▼bAgricultural, Environmental and Developmental Economics.
■7730 ▼tDissertations Abstracts International▼g86-04B.
■790 ▼a0168
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17164927▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
