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  1. NTU Theses and Dissertations Repository
  2. 管理學院
  3. 管理學院企業管理專班(Global MBA)
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/101462
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor堯里昂zh_TW
dc.contributor.advisorLeon Van Jaarsveldten
dc.contributor.author米嘉昇zh_TW
dc.contributor.authorJoshua Max Milleren
dc.date.accessioned2026-02-03T16:28:14Z-
dc.date.available2026-02-04-
dc.date.copyright2026-02-03-
dc.date.issued2025-
dc.date.submitted2026-01-14-
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Andrén, N., Jankensgård, H., & Oxelheim, L. (2005). Exposure-based cash-flow-at-risk under macroeconomic uncertainty (IUI Working Paper No. 635). Research Institute of Industrial Economics (IFN). https://hdl.handle.net/10419/81256
Brinley, Stephanie. (2025, February 4). Auto Industry Tariffs Loom, Risking Substantial Disruptions. SP Global. https://www.spglobal.com/automotive-insights/en/rapid-impact-analysis/auto-industry-tariffs-risk-substantial-disruptions
Buchholz, Katharina. (2020, August 17). U.S.-Chinese Trade War: A Timeline. Statista https://www.statista.com/chart/15199/us-chinese-trade-war-escalates/
Crutsinger, Martin. (2021, January 28). US economy shrank 3.5% in 2020 after growing 4% last quarter. AP. https://apnews.com/article/us-economy-shrink-in-2020-b59f9be06dcf1da924f64afde2ce094c
de Querol Cumbrera, Fernando. (2024, December 20). Share of used and new vehicles withfinancing in the United States from 2022 to 2024. Statista. https://www.statista.com/statistics/453000/share-of-new-vehicles-with-financing-usa/
Durbin, J., & Watson, G. S. (1950). Testing for Serial Correlation in Least Squares Regression. Biometrika, 37 (3/4), 409–428.
Hall-Geisler, Kristen (2025, March 4) Which Automakers Will Be Affected by Tariffs? US News. https://cars.usnews.com/cars-trucks/advice/which-automakers-will-be-affected-by-tariffs
Howlett, Alexandra. (2019, June 18). How does the US-China trade war hurt carmakers? Financial Times. https://www.ft.com/content/18709830-82c8-11e9-a7f0-77d3101896ec
IEA. (2021, May 5). Minerals used in electric cars compared to conventional cars. IEA. https://www.iea.org/data-and-statistics/charts/minerals-used-in-electric-cars-compared-to-conventional-cars
Isidore, Chris. (2025, May 1). GM CEO Mary Barra: Tariffs will cost us $5 billion, and prices ‘will stay at the same level’. CNN. https://edition.cnn.com/2025/05/01/business/gm-ceo-barra-tariffs-cost
ITA. (2024). U.S. International Trade Administration, Automotive Parts Trade Database https://www.trade.gov/data-visualization/automotive-parts-trade-data-visualization
Jiang, Maggie Ying. (2024). Consumer Nationalism in China: Examining Its Critical Impact On Multinational Businesses. Anthem Press. https://www.jstor.org/stable/jj.15729469
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Kaplan, R. S., & Mikes, A. (2012, June). Managing Risks: A New Framework. Harvard
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Kelly, Megan. (2025, January). Flooding caused by climate change is set to be the biggest threat to the automotive supply chain in 2025. Automotive Logistics.. https://www.automotivelogistics.media/sustainability/climate-change-set-to-be-the-biggest-supply-chain-risk-in-2025/46635.article?adredir=1
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Korosec, Kirsten. (2021, April). GM idles more North American plants as chip shortage drags on. techcrunch.com. https://techcrunch.com/2021/04/08/gm-idles-more-north-american-plants-as-chip-shortage-drags-on/?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuYmluZy5jb20v&guce_referrer_sig=AQAAALlb4q1wz_664ofCj2Jn3Muj-cQmsMpBfHicQbk7Qktdz2zgFk3ULwfOp7f3FrsjHLwWGcFISdUShNlVHyjzHhnOVpNBqZq8msoeFqoj3P19OVVrP5uykmRgU547pfDRv5LNLUwVhe7D6hgbrrYrEExskiNtgnC8vJS5DfMSaE72.
Kuti, M. (2011). Cash Flow at Risk, Financial Flexibility and Financing Constraint. Corvinus University of Budapest. < https://unipub.lib.uni-corvinus.hu/9009/1/a_505_517_kutim.pdf>
Mayco International. (2019, July). What Are Cars Made Of? 10 Of The Top Materials Used In Auto Manufacturing. Mayco. https://maycointernational.com/blog/what-are-cars-made-of/
Oxelheim, L., & Wihlborg, C. (2008). Corporate decision-making with macroeconomic uncertainty: Performance and risk management. Oxford University Press.
Reklaitis, Victor. (2025, April 12). GM, Ford and Stellantis face extra $5,000 cost for each car made in America, thanks to Trump's tariff on parts. Morning Star. https://www.morningstar.com/news/marketwatch/20250411291/gm-ford-and-stellantis-face-extra-5000-cost-for-each-car-made-in-america-thanks-to-trumps-tariff-on-parts
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White House. (2024, May 14). Fact Sheet President Biden Takes Action to Protect American Workers and Businesses from China’s Unfair Trade Practices. Whitehouse.gov. https://www.whitehouse.gov/briefing-room/statements-releases/2024/05/14/fact-sheet-president-biden-takes-action-to-protect-american-workers-and-businesses-from-chinas-unfair-trade-practices/
Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach. Cengage Learning.
Xue, Selina. (2024). China’s automotive odyssey: From joint ventures to global EV dominance. IMD. https://www.imd.org/ibyimd/innovation/chinas-automotive-odyssey-from-joint-ventures-to-global-ev-dominance/
Yan, M., Hall, M. J. B., & Turner, P. (2014). Estimating liquidity risk using the exposure-based cash-flow-at-risk approach: An application to the UK banking sector. International Journal of Finance and Economics, 19(3), 225–238. https://doi.org/10.1002/ijfe.1495
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/101462-
dc.description.abstract本論文運用現金流風險(CFaR)模型,結合Oxelheim和Wihlborg的MUST框架及CFaR方法,分析了中美貿易戰對福特和通用汽車(GM)的影響,並量化了宏觀經濟風險敞口。透過分析2016年至2024年的季度數據,研究揭示了兩家公司截然不同的風險特徵:福特對投入成本通膨(PPI運輸設備:β = -48.65,p值 = 0.036)極度敏感,供應鏈危機預計會導致CFaR = 7.25%;而通用汽車則表現出出乎意料的抗跌性,預計會導致609.25%;而通用汽車則表現出出乎意料的抗跌性價格為新價格。與理論預期相反,滯後宏觀經濟效應並不顯著(R² < 0.1),顯示該模型能夠解釋營運現金流對宏觀經濟衝擊的即時反應。研究結果凸顯了福特汽車易受中國供應鏈中斷(人民幣貶值)的影響,以及通用汽車相對不易受影響——這一關鍵因素凸顯了各公司特定的風險管理需求。鑑於川普連任前景不明朗以及貿易戰再度升級,本研究旨在深入分析美國汽車製造商在應對貿易戰帶來的地緣政治和宏觀經濟不確定性時所面臨的脆弱性和應對策略。zh_TW
dc.description.abstractThis thesis examines the repercussions of the U.S.-China trade war on Ford and General Motors (GM) through a Cash Flow at Risk (CFaR) lens, applying Oxelheim and Wihlborg’s MUST framework and CFaR methodology to quantify macroeconomic exposures. Analyzing quarterly data (2016–2024), the study reveals divergent risk profiles: Ford exhibits extreme sensitivity to input cost inflation (PPI Transportation Equipment: β = -48.65, p-value = 0.036), with supply chain crises projecting a 7.25% downside CFaR, while GM shows unexpected resilience to steel price shocks (β = +1.07, p-value = 0.06). Counter to theoretical expectations, lagged macroeconomic effects proved insignificant (R^2 < 0.1), suggesting this model explains that operational cash flows respond immediately to macroeconomic shocks. The findings highlight Ford’s vulnerability to Chinese supply chain disruptions (CNY depreciation) and GM’s relative insulation - a critical factor underscoring firm-specific risk management needs. Given the uncertainty surrounding Trump's second term and the escalation of a renewed trade war, this study aims to provide insights into the vulnerabilities and resilience strategies of U.S. automakers in navigating the geopolitical and macroeconomic uncertainties of the trade war.en
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dc.description.tableofcontentsAcknowledgment i
Abstract ii
Chinese Abstract iii
Table of Contents iv
List of Abbreviations vii
1. Introduction 1
1.1 Risk Management in Business 2
1.2 Limitations of Traditional Risk Management Frameworks 3
1.3 Background 3
1.3.1 Selection of Ford and GM 5
1.3.2 Exposure to U.S.-China Trade War Dynamics 5
1.3.3 Trump 2.0 6
1.3.4 Supply Chain Vulnerabilities and Amplifying Macro Risks 8
2. Literature Review 12
2.1 The Evolution of CFaR 12
2.2 Application to Ford and GM 14
2.3 Theoretical Framework 14
2.3.1 The MUST Framework 14
2.3.2 Exposure and Sensitivity Analysis, and Risk Quantification 15
2.4 Introduction to Hypotheses 15
2.4.1 Commodity Price Volatility 16
2.4.2 Auto Dependence on Steel and Aluminum 16
2.4.3 Macroeconomic Feedback Loops: Producer Prices and Demand 17
3. Methodology 18
3.1 Methodological Framework 18
3.1.1 Data Collection 18
3.1.2 Variable Selection 19
3.1.3 Model Specification 20
3.1.4 Regression Model 21
3.1.5 Calculating Cash Flow at Risk 21
3.1.6 Post-Regression CFaR Shock Simulation 22
3.1.7 Hypothesis Testing 22
3.1.8 Expected Contributions 22
3.2 Data Transformation and Rationale 23
3.2.1 Data Normalization 23
3.2.2 Autocorrelation Testing via Durbin-Watson Statistic 25
3.2.3 Rationale for Data Transformation 26
3.2.4 Analysis of the Durbin Watson Statistic 26
4. Results 28
4.1 Primary Regression Results 28
4.1.1 Ford Regression Results 28
4.1.2 GM Regression Results 30
4.2 Secondary Regression Results - Trump 1.0 vs. Biden 31
4.2.1 Ford Regression Results for Trump 1.0 31
4.2.2 GM Regression Results for Trump 1.0 32
4.2.3 Ford Regression Results for Biden 33
4.2.4 GM Regression Results for Biden 33
4.3 Testing Lagged Regression Models 33
4.3.1 Issues from Results 34
4.3.2 Diagnostic Implications for CFaR 35
4.4 Cash Flow at Risk Scenario Application 35
4.4.1 CFaR Application 35
4.4.2 Shock Scenario Magnitudes and Selection Rationale 37
4.4.3 CFaR Shock Scenario Results Table 38
5. Discussion 39
5.1 Discussion of Results - Ford 39
5.2 Discussion of Results - GM 39
5.3 Testing Trump 1.0 vs. Biden Analysis 40
5.4 CFaR Shock Scenario Analysis 41
5.5 Hypothesis Discussion 41
6. Conclusion 43
References 44
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dc.language.isoen-
dc.subject風險管理-
dc.subject風險現金流 (CFaR)-
dc.subject宏觀經濟不確定性策略 (MUST)-
dc.subject中美貿易戰-
dc.subjectRisk management-
dc.subjectCash Flow at Risk (CFaR)-
dc.subjectMacroeconomic Uncertainty Strategy (MUST)-
dc.subjectUS-China Trade War-
dc.title美國汽車製造商的現金流風險分析(CFaR):福特和通用汽車的供應鏈風險暴露於大宗商品價格和宏觀經濟指標zh_TW
dc.titleCash Flow at Risk Analysis (CFaR) of U.S. Automakers: Ford and GM's Supply Chain Risk Exposure to Commodity Prices and Macroeconomic Indicatorsen
dc.typeThesis-
dc.date.schoolyear114-1-
dc.description.degree碩士-
dc.contributor.oralexamcommittee簡睿哲 ; 盧信昌zh_TW
dc.contributor.oralexamcommitteeBryan Jean;Xin-Chang Luen
dc.subject.keyword風險管理,風險現金流 (CFaR)宏觀經濟不確定性策略 (MUST)中美貿易戰zh_TW
dc.subject.keywordRisk management,Cash Flow at Risk (CFaR)Macroeconomic Uncertainty Strategy (MUST)US-China Trade Waren
dc.relation.page48-
dc.identifier.doi10.6342/NTU202504359-
dc.rights.note同意授權(全球公開)-
dc.date.accepted2026-01-14-
dc.contributor.author-college管理學院-
dc.contributor.author-dept企業管理碩士專班-
dc.date.embargo-lift2026-02-04-
顯示於系所單位:管理學院企業管理專班(Global MBA)

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