Gwen Kamrud , William W. Wilson , David W. Bullock
{"title":"美国和巴西对中国大豆运输的物流竞争:一种优化的蒙特卡罗模拟方法","authors":"Gwen Kamrud , William W. Wilson , David W. Bullock","doi":"10.1016/j.jcomm.2022.100290","DOIUrl":null,"url":null,"abstract":"<div><p>The United States and Brazil<span> fiercely compete with each other in the Chinese soybean import market. Logistical functions and costs are volatile and risky and influence the export competition between the two countries. This study analyzes commodity trading strategies and the effect of logistical functions and costs in the United States and Brazil for shipments to China using an Optimized Monte Carlo Simulation model accounting for a large number of random and correlated variables. Base case results approximate the actual monthly data between 2013 and 2019. These results indicate that the United States captures a larger share of soybean export shipments between December and March while Brazil is dominant from April to November. Sensitivity analyses were performed on logistical variables in the United States (ocean shipping costs, U.S. secondary rail car market, and rail unload incentives) and Brazil (improving logistical infrastructure and wait times) to illustrate their impacts on optimal trading strategies.</span></p></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"31 ","pages":"Article 100290"},"PeriodicalIF":3.7000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Logistics competition between the U.S. and Brazil for soybean shipments to China: An optimized Monte Carlo simulation approach\",\"authors\":\"Gwen Kamrud , William W. Wilson , David W. Bullock\",\"doi\":\"10.1016/j.jcomm.2022.100290\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The United States and Brazil<span> fiercely compete with each other in the Chinese soybean import market. Logistical functions and costs are volatile and risky and influence the export competition between the two countries. This study analyzes commodity trading strategies and the effect of logistical functions and costs in the United States and Brazil for shipments to China using an Optimized Monte Carlo Simulation model accounting for a large number of random and correlated variables. Base case results approximate the actual monthly data between 2013 and 2019. These results indicate that the United States captures a larger share of soybean export shipments between December and March while Brazil is dominant from April to November. Sensitivity analyses were performed on logistical variables in the United States (ocean shipping costs, U.S. secondary rail car market, and rail unload incentives) and Brazil (improving logistical infrastructure and wait times) to illustrate their impacts on optimal trading strategies.</span></p></div>\",\"PeriodicalId\":45111,\"journal\":{\"name\":\"Journal of Commodity Markets\",\"volume\":\"31 \",\"pages\":\"Article 100290\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2023-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Commodity Markets\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2405851322000472\",\"RegionNum\":4,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BUSINESS, FINANCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Commodity Markets","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2405851322000472","RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
Logistics competition between the U.S. and Brazil for soybean shipments to China: An optimized Monte Carlo simulation approach
The United States and Brazil fiercely compete with each other in the Chinese soybean import market. Logistical functions and costs are volatile and risky and influence the export competition between the two countries. This study analyzes commodity trading strategies and the effect of logistical functions and costs in the United States and Brazil for shipments to China using an Optimized Monte Carlo Simulation model accounting for a large number of random and correlated variables. Base case results approximate the actual monthly data between 2013 and 2019. These results indicate that the United States captures a larger share of soybean export shipments between December and March while Brazil is dominant from April to November. Sensitivity analyses were performed on logistical variables in the United States (ocean shipping costs, U.S. secondary rail car market, and rail unload incentives) and Brazil (improving logistical infrastructure and wait times) to illustrate their impacts on optimal trading strategies.
期刊介绍:
The purpose of the journal is also to stimulate international dialog among academics, industry participants, traders, investors, and policymakers with mutual interests in commodity markets. The mandate for the journal is to present ongoing work within commodity economics and finance. Topics can be related to financialization of commodity markets; pricing, hedging, and risk analysis of commodity derivatives; risk premia in commodity markets; real option analysis for commodity project investment and production; portfolio allocation including commodities; forecasting in commodity markets; corporate finance for commodity-exposed corporations; econometric/statistical analysis of commodity markets; organization of commodity markets; regulation of commodity markets; local and global commodity trading; and commodity supply chains. Commodity markets in this context are energy markets (including renewables), metal markets, mineral markets, agricultural markets, livestock and fish markets, markets for weather derivatives, emission markets, shipping markets, water, and related markets. This interdisciplinary and trans-disciplinary journal will cover all commodity markets and is thus relevant for a broad audience. Commodity markets are not only of academic interest but also highly relevant for many practitioners, including asset managers, industrial managers, investment bankers, risk managers, and also policymakers in governments, central banks, and supranational institutions.