跳到主要內容

臺灣博碩士論文加值系統

(34.226.244.254) 您好!臺灣時間:2021/08/01 04:08
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:蔡翠聯
研究生(外文):TSAI, TSUI-LIEN
論文名稱:美國經濟政策不確定性對S&P 500和期貨市場的影響
論文名稱(外文):The Impact of US Economic Policy Uncertainty on S&P 500 and Futures Markets.
指導教授:蕭榮烈蕭榮烈引用關係
指導教授(外文):HSIAO, JUNG-LIEH
口試委員:邱建良蕭榮烈涂登才李佩芳郭淑惠
口試委員(外文):CHIU,CHIEN-LIANGHSIAO, JUNG-LIEHTU, TENG-TSAILI, PEI-FANGKUO, SHEW HUEI
口試日期:2021-03-05
學位類別:碩士
校院名稱:國立臺北大學
系所名稱:國際企業研究所
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:英文
論文頁數:84
中文關鍵詞:經濟政策不確定性相關性避險比率DCC GARCH模型
外文關鍵詞:Economic Policy UncertaintyCorrelation,Hedge RatiosDCC GARCH Model
相關次數:
  • 被引用被引用:0
  • 點閱點閱:15
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
  本研究探討 Baker等人 (2016)所建立的新指標-美國經濟政策不確定性是否對現貨以及期貨市場產生影響,採用現貨市場的標普 500指數(S&P 500)作為長期部位,以期貨市場的布蘭特原油、西德州原油、黃金、債券以及標準普爾高盛商品指數(S&P GSCI)作為短期部位。首先研究美國經濟政策不確定性對現貨以及期貨兩兩之間的動態條件相關係數的影響。第二,加入重大事件,分別為歐債危機、中美貿易戰以及 COVID-19,探討在重大事件期間,不確定性對相關性的影響是否更加明顯。第三,了解不確定性對避險比率的影響。 根據實證結果表明,美國經濟政策不確定性對動態條件相關性以及避險比率為正相關,當存在更多不確定性時,現貨和期貨市場的相關性將會增加,並且增加避險部位以避高度 不確定性的市場風險。以上結果提供投資人作為決策參考要素。
  This paper adopts the new indicator of US Economic Policy Uncertainty proposed by Baker et al. (2016). We explore whether US Economic Policy Uncertainty has an impact on the spot and futures markets. The S&P 500 in the spot market is used as the long position, while Brent oil, WTI oil, gold, bond and the Goldman Sachs Commodity Index (S&P GSCI) are short positions in the futures markets. First, we research the impact of US Economic Policy Uncertainty on the dynamic conditional correlation coefficient between spot and futures data. Second, we add major events, such as the European debt crisis, the U.S.-China trade war, and COVID-19, to explore the joint effect of US Economic Policy Uncertainty and correlation during major events. Third, we want to understand the impact of US Economic Policy Uncertainty on the hedge ratios. According to the empirical results, US Economic Policy Uncertainty is positively correlated with the dynamic condition correlation and the hedge ratios. When there is more uncertainty, the correlation between the spot and futures markets would increase. Thus, we should increase the hedge positions to avoid the high uncertainty. The above results provide investors as reference for decision-making.
CONTENT
Chapter 1 Introduction 1
1.1 Research Background and Motivation 1
1.2 Research Purpose 5
1.3 Research Process 6
Chapter 2 Literature Review 8
2.1 The Related Hedged Beta 8
2.2 Economic Policy Uncertainty 10
2.3 The Effect of The Major Events 13
Chapter 3 Methodology 15
3.1 Unit Root Test 16
3.1.1 Phillips-Perron Test (PP Test) 16
3.1.2 Kwiatkowski-Phillips-Schmidt-Shin Test (KPSS Test) 17
3.1.3 Serial Correlation Teat and Heteroscedasticity Test 18
3.2 Estimation of Hedge Ratio 19
3.3 Asymmetric test 21
3.4 DCC GARCH 22
3.5 Hypotheses 24
3.6 Empirical Method 25
3.6.1 Uncertainty and Hedge Ratio 25
3.6.2 Joint Effect of Economic Policy Uncertainty and Major Events 26
3.6.3 Hedge Ratios and Economic Policy Uncertainty 27
3.6.4 Threshold Regression Model 27
Chapter 4 Empirical Results 29
4.1 Descriptive Statistics 29
4.2 Results 36
4.2.1 Unit root test 36
4.2.2 Serial Correlation Test and Heteroscedasticity Test 40
4.2.3 Asymmetric Test 41
4.2.4 Dynamic Conditional Correlation 44
4.2.5 Results of Empirical Model 46
4.2.6 Results of Threshold Regression Model 63
Chapter 5 Conclusion and Suggestion 64
Reference 67
Appendix 71
Appendix A Estimates of model 1 between S&P 500 and S&P GSCI in Threshold Regression 71
Appendix B Estimates of model 2 between S&P 500 and S&P GSCI in Threshold Regression 71
Appendix C Estimates of model 3 between S&P 500 and gold in Threshold Regression 72
Appendix D Estimates of model 3 between S&P 500 and gold in Threshold Regression 72
Appendix E Estimates of model 3 between S&P 500 and gold in Threshold Regression 73
Appendix F Estimates of model 3 between S&P 500 and S&P GSCI in Threshold Regression 73
Appendix G Estimates of model 3 between S&P 500 and S&P GSCI in Threshold Regression 74
Appendix H Estimates of model 4 between S&P 500 and WTI oil in Threshold Regression 74
Appendix I Estimates of model 4 between S&P 500 and S&P GSCI in Threshold Regression 75
Appendix J Estimates of model 4 between S&P 500 and S&P GSCI in Threshold Regression 76
Appendix K Estimates of model 5 between S&P 500 and WTI in Threshold Regression 76
Appendix L Estimates of model 5 between S&P 500 and bond in Threshold Regression 77
Appendix M Estimates of model 1 between S&P 500 and futures markets 77
Appendix N Estimates of model 2 between S&P 500 and futures markets 78
Appendix O Estimates of model 3 between S&P 500 and futures markets during European debt crisis 79
Appendix P Estimates of model 3 between S&P 500 and futures markets during U.S.-China trade war 80
Appendix Q Estimates of model 3 between S&P 500 and futures markets during COVID-19 81
Appendix R Estimates of model 4 between S&P 500 and futures markets during European debt crisis 82
Appendix S Estimates of model 4 between S&P 500 and futures markets during U.S.-China trade war 83
Appendix T Estimates of model 4 between S&P 500 and futures markets during COVID-19 84

Antonakakis, N., Cunado, J., Filis, G., Gabauer, D., & de Gracia, F. P. (2020). Oil and asset classes implied volatilities: Investment strategies and hedging effectiveness. Energy Economics, 91, 104762. https://doi.org/10.1016/j.eneco.2020.104762
Badshah, I., Demirer, R., & Suleman, M. T. (2019). The effect of economic policy uncertainty on stock-commodity correlations and its implications on optimal hedging. Energy Economics, 84, 104553. https://doi.org/10.1016/j.eneco.2019.104553
Baig, A. S., Butt, H. A., Haroon, O., & Rizvi, S. A. R. (2020). Deaths, panic, lockdowns and US equity markets: The case of COVID-19 pandemic. Finance Research Letters, 101701. https://doi.org/10.1016/j.frl.2020.101701
Baillie, R. T., & Bollerslev, T. (1990). A multivariate generalized ARCH approach to modeling risk premia in forward foreign exchange rate markets. Journal of International Money and Finance, 9(3), 309–324. https://doi.org/10.1016/0261-5606(90)90012-O
Bakas, D., & Triantafyllou, A. (2018). The impact of uncertainty shocks on the volatility of commodity prices. Journal of International Money and Finance, 87, 96–111. https://doi.org/10.1016/j.jimonfin.2018.06.001
Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593–1636. https://doi.org/10.1093/qje/qjw024
Basher, S. A., & Sadorsky, P. (2016). Hedging emerging market stock prices with oil, gold, VIX, and bonds: A comparison between DCC, ADCC and GO-GARCH. Energy Economics, 54, 235–247. https://doi.org/10.1016/j.eneco.2015.11.022
Batten, J. A., Kinateder, H., Szilagyi, P. G., & Wagner, N. F. (2019). Hedging stocks with oil. Energy Economics, 104422. https://doi.org/10.1016/j.eneco.2019.06.007
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1
Chang, C.-L., McAleer, M., & Tansuchat, R. (2011). Crude oil hedging strategies using dynamic multivariate GARCH. Energy Economics, 33(5), 912–923. https://doi.org/10.1016/j.eneco.2011.01.009
Chkili, W., Aloui, C., & Nguyen, D. K. (2014). Instabilities in the relationships and hedging strategies between crude oil and US stock markets: Do long memory and asymmetry matter? Journal of International Financial Markets, Institutions and Money, 33, 354–366. https://doi.org/10.1016/j.intfin.2014.09.003
Choi, K., & Hammoudeh, S. (2010). Volatility behavior of oil, industrial commodity and stock markets in a regime-switching environment. Energy Policy, 38(8), 4388–4399. https://doi.org/10.1016/j.enpol.2010.03.067
Ciner, C., Gurdgiev, C., & Lucey, B. M. (2013). Hedges and safe havens: An examination of stocks, bonds, gold, oil and exchange rates. International Review of Financial Analysis, 29, 202–211. https://doi.org/10.1016/j.irfa.2012.12.001
Davis S. J. (2016). An Index of Global Economic Policy Uncertainty | NBER. https://www.nber.org/papers/w22740
Degiannakis, S., Filis, G., & Panagiotakopoulou, S. (2018). Oil price shocks and uncertainty: How stable is their relationship over time? Economic Modelling, 72, 42–53. https://doi.org/10.1016/j.econmod.2018.01.004
Ederington, L. H. (1979). The Hedging Performance of the New Futures Markets. The Journal of Finance, 34(1), 157–170. https://doi.org/10.1111/j.1540-6261.1979.tb02077.x
Elsayed, A. H., Nasreen, S., & Tiwari, A. K. (2020). Time-varying co-movements between energy market and global financial markets: Implication for portfolio diversification and hedging strategies. Energy Economics, 90, 104847. https://doi.org/10.1016/j.eneco.2020.104847
Engle, R. (2002). Dynamic Conditional Correlation. Journal of Business & Economic Statistics, 20(3), 339–350. https://doi.org/10.1198/073500102288618487
Engle, R. F., & Bollerslev, T. (1986). Modelling the persistence of conditional variances. Econometric Reviews, 5(1), 1–50. https://doi.org/10.1080/07474938608800095
Engle, R. F., & Ng, V. K. (1993). Measuring and Testing the Impact of News on Volatility. The Journal of Finance, 48(5), 1749–1778. https://doi.org/10.1111/j.1540-6261.1993.tb05127.x
Fang, L., Bouri, E., Gupta, R., & Roubaud, D. (2019). Does global economic uncertainty matter for the volatility and hedging effectiveness of Bitcoin? International Review of Financial Analysis, 61, 29–36. https://doi.org/10.1016/j.irfa.2018.12.010
Fang, L., Chen, B., Yu, H., & Xiong, C. (2018). The effect of economic policy uncertainty on the long-run correlation between crude oil and the U.S. stock markets. Finance Research Letters, 24, 56–63. https://doi.org/10.1016/j.frl.2017.07.007
Fang, L., Yu, H., & Li, L. (2017). The effect of economic policy uncertainty on the long-term correlation between U.S. stock and bond markets. Economic Modelling, 66, 139–145. https://doi.org/10.1016/j.econmod.2017.06.007
Gao, R., & Zhang, B. (2016). How does economic policy uncertainty drive gold–stock correlations? Evidence from the UK. Applied Economics, 48(33), 3081–3087. https://doi.org/10.1080/00036846.2015.1133903
Hansen, B. E. (2000). Sample Splitting and Threshold Estimation. Econometrica, 68(3), 575–603. https://doi.org/10.1111/1468-0262.00124
Hill, J., & Schneeweis, T. (1981). A note on the hedging effectiveness of foreign currency futures. Journal of Futures Markets, 1(4), 659–664. https://doi.org/10.1002/fut.3990010408
Huang, Z., Liang, F., & Tong, C. (2020). The predictive power of macroeconomic uncertainty for commodity futures volatility. International Review of Finance, irfi.12310. https://doi.org/10.1111/irfi.12310
Junttila, J., Pesonen, J., & Raatikainen, J. (2018). Commodity market based hedging against stock market risk in times of financial crisis: The case of crude oil and gold. Journal of International Financial Markets, Institutions and Money, 56, 255–280. https://doi.org/10.1016/j.intfin.2018.01.002
Kroner, K. F., & Sultan, J. (1993). Time-Varying Distributions and Dynamic Hedging with Foreign Currency Futures. The Journal of Financial and Quantitative Analysis, 28(4), 535. https://doi.org/10.2307/2331164
Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54(1–3), 159–178. https://doi.org/10.1016/0304-4076(92)90104-Y
Liu, L., & Zhang, T. (2015). Economic policy uncertainty and stock market volatility. Finance Research Letters, 15, 99–105. https://doi.org/10.1016/j.frl.2015.08.009
Ljung and Box. (1978). On a measure of lack offitin time series models. 7.
Park, T. H., and Switzer, L. N. (1995). Bivariate GARCH estimation of the optimal hedge ratios for stock index futures: A note—Park—1995—Journal of Futures Markets—Wiley Online Library. https://onlinelibrary.wiley.com/doi/abs/10.1002/fut.3990150106
Pástor, Ľ., & Veronesi, P. (2013). Political uncertainty and risk premia. Journal of Financial Economics, 110(3), 520–545. https://doi.org/10.1016/j.jfineco.2013.08.007
Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. 12.
Qin, M., Su, C.-W., Hao, L.-N., & Tao, R. (2020). The stability of U.S. economic policy: Does it really matter for oil price? Energy, 198, 117315.
https://doi.org/10.1016/j.energy.2020.117315
Raza, S. A., Shah, N., & Shahbaz, M. (2018). Does economic policy uncertainty
influence gold prices? Evidence from a nonparametric causality-in-quantiles
approach. Resources Policy, 57, 61–68.
https://doi.org/10.1016/j.resourpol.2018.01.007
Xu, Y., & Lien, D. (2020). Dynamic exchange rate dependences: The effect of the U.S.-
China trade war. Journal of International Financial Markets, Institutions and
Money, 68, 101238. https://doi.org/10.1016/j.intfin.2020.101238
Yu, H., Fang, L., Du, D., & Yan, P. (2017). How EPU drives long-term industry beta.
Finance Research Letters, 22, 249–258.
https://doi.org/10.1016/j.frl.2017.05.012
Zhang, Y.-J., & Yan, X.-X. (2020). The impact of US economic policy uncertainty on
WTI crude oil returns in different time and frequency domains. International
Review of Economics & Finance, 69, 750–768.
https://doi.org/10.1016/j.iref.2020.04.001
電子全文 電子全文(網際網路公開日期:20260621)
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關論文
 
無相關期刊
 
無相關點閱論文