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| Title: | 恐慌指標與股價指數關聯性之研究
A Study of the Relationship between Fear Indicators and Stock Indexes |
| Authors: | 張耿榮
Jhang, Geng Rong |
| Contributors: | 林建秀
Lin, Chien Hsiu
張耿榮
Jhang, Geng Rong |
| Keywords: | 金價油價比
CBOE偏態指數
CSFB指數
泰德價差
ARDL
界限檢定
Gold to Oil Ratio
CBOE Skew Index
CSFB Index
TED Spread
ARDL
Bound Testing |
| Date: | 2016 |
| Issue Date: | 2016-07-11 17:04:10 (UTC+8) |
| Abstract: | 2015年下半年開始，許多有關市場黑天鵝的新聞佈滿各大媒體版面，其中不乏「某恐慌指標創歷史新高」此類令投資人恐懼的標題。然事實上卻未見到各國股價指數有大幅修正的現象，以MSCI全球指數而言，下半年總計僅修正6.49%。為了探討這些不同於傳統VIX指數的恐慌指標是否會顯著影響股價指數的表現。本論文透過VAR、VECM以及ARDL模型，探討金價油價比、CBOE偏態指數、瑞士信貸CSFB指數以及泰德價差這四種恐慌指標對於當前全球前四大經濟體股價指數的關聯性。
美國是全世界經濟的領頭羊，其經濟情勢與全球每一個國家的榮景息息相關，美國股價指數的表現亦是相當受到全球投資人所關注的。故本論文首先透過探討這四種恐慌指標對於S&P 500指數的影響，再利用S&P 500指數領先各國股價指數的特性進一步得出結論。實證結果發現，S&P 500指數對於其他三個股價指數確實具有短期同向的影響，長期而言亦具有穩定的線性關係。另外，金價油價比無論在短期及長期下皆無法有效代理市場的恐慌程度而影響S&P 500指數；CBOE偏態指數與瑞士信貸CSFB指數在長期下得以領先S&P 500指數的變化，當該二指數走高，代表 S&P 500指數在近期的波段高點可能即將來臨，亦即隱含該二指數對於S&P 500指數具有領先同向變化的現象；泰德價差為市場用以衡量信用風險的指標之一，當泰德價差擴大，隱含市場風險貼水增加，不利股市發展，其與S&P 500指數則具有長期穩定的負向關係。本論文最後也針對這四種恐慌指標的預測能力進行探討，發現瑞士信貸CSFB指數在預測S&P 500指數的能力上，相對其他三種恐慌指標優異。
There were so many hearsays about the potential black swan events dominating the news in the second half of 2015. Headlines were about some fear indicators hit historic high but, in realistic, world stock market did not be significantly influenced under this panic atmosphere. Take MSCI World Index for instance, the index dropped only 6.49% in the second half of 2015, which was relatively unreasonable under this condition. In order to find out whether or not the fluctuations of these fear indicators can significantly affect stock indexes, VAR, VAEM and ARDL model to discuss the relationships between 4 fear indicators and 4 stock indexes─gold to oil ratio, CBOE Skew Index, Credit Suisse Fear Barometer Index, TED spread, S&P 500 Index, MSCI Europe Index, SSE A Share Index and Nikkei 225 Index are adopted in this study.
Global investors pay close attention to the performance of the U.S. Stock indexes as U.S. economy condition can affect the economies of the rest of the world. Consequently, we investigated the effects of 4 fear indicators to the S&P 500 Index then employed relationships between S&P 500 Index and other 3 stock indexes to do further discussion. The results show S&P 500 positively affects the performances of other 3 stock indexes in short term and has a steady relationship with each of them respectively in the long term. The changes of gold to oil ratio could not significantly influence the performance of S&P 500 Index no matter in the short term or the long term. CBOE Skew Index and CSFB Index have significant positive influences on S&P 500 and are leading indicators to S&P 500 Index. Lastly, TED spread has a steady negative relationship with S&P 500 in long term, and CSFB Index has the highest predictive power among the 4 fear indicators. |
| Reference: | Banerjee A.J., Dolado J.W., Galbraith and Hendry D.F. 1993. “Cointegration, Error-Correction, and The Econometric Analysis of Non-Stationary Data”. New York: Oxford University Press.
Brunnermeier M.K., Nagel S. and Pedersen L. H. 2008. “Carry trades and currency crashes”. NBER. Working paper.
CBOE. 2011. “The CBOE Skew Index - SKEW”. Chicago Board Options Exchange. White Paper.
CBOE. 2011. “CBOE Skew Index FAQ”. Chicago Board Options Exchange.
Chien-Hsiu Lin. 2011. “The comovement between exchange rates and stock prices in the Asian emerging markets”. International Review of Economics and Finance. 22 (2012) 161–172.
Dickey D.A. and W.A. Fuller. 1979. “Distribution of the Estimation for Autoregressive Time Series with a Unit Root”. Journal of American Statistical Association. 74, 427-31.
Edward K. Tom and Dennis Davitt. 2009. “The Credit Suisse Fear Barometer (CSFB)”. Credit Suisse. Equity Derivatives Strategy.
Engle R.E. and Granger C.W.J. 1987. “Cointegration and error-correction：representation, estimation and testing”. Econometrica. Vol. 55. 251-76.
Gil Cohen and Mahmod Qadan. 2010. “Is gold still a shelter to fear?”. American Journal of Social and Management Sciences. 1(1): 39-43
Granger C.W.J. 1969. “Investigating Causal Relations by Econometrics Models and Spectral Methods”. Econometrica. 37, 424-438.
Granger C.W.J. and Newbold P. 1974. “Superious Regressions in Econometrics”. Journal of Econometrics. 2(2), 111-120.
Hamilton J.D. 1983. "Oil and the Macroeconomy since World War II". Journal of Political Economy. Vol.91, No.2, 228-248.
Ivailo Arsov, Elie Canetti, Laura Kodres, and Srobona Mitra. “Near-Coincident Indicators of Systemic Stress”. IMF. Working paper.13.115.
Johansen S. 1988. “Statistical Analysis of Cointegration Vectors”. Journal of Economic Dynamics and Control. 12, 231-254.
Lashgari M. 2009. “The role of TED spread and confidence index in explaining the behavior of stock prices”. American Business Review. 18(2), 9-11.
Lutz Kilian and Cheolbeom Park. 2009. “The Impact of Oil Price Shocks on the U.S. StockMarket”. International Economic Review. Vol. 50, No. 4,
Mahmod Qadan & Joseph Yagil. 2012. “Fear sentiments and gold price: testing causality in-mean and in-variance”. Applied Economics Letters. 19:4, 363-366
Mandy Xu. 2012. “How Predictive is the CS Fear Barometer vs. the VIX?”. Credit Suisse. Equity Derivatives Strategy.
Melvyn Teo. 2011. “The liquidity risk of liquid hedge funds”. Journal of Financial Economics. 100(1), 24-44.
Pesaran H., Shin Y., and Smith R. 2001. “Bound testing approaches to the analysis of level relationships”. Journal of Applied Econometrics. 16, 289–326.
Said S. and Dickey D.A. 1984. “Testing for unit roots in autoregressive-moving average models with unknown order”. Biometica. Vol. 71. 559-607.
Sims C. 1980. “Macroeconomic and Reality”. Econometrica. 48, 1-49.
Thai-Ha Le and Youngho Chang. 2011. “Oil and Gold: Correlation or Causation?”. Depocen. Working Paper Series No. 2011/22, 1-36.
Thomas Nitschka. 2013. “Momentum in stock market returns：implications for risk premia on foreign currencies”. Swiss National Bank. Financial Stability Unit. Working paper.
Wang, Zhiguang, and Robert T. Daigler. 2014. “VIX and SKEW Indices for SPX and VIX Options”. Journal of Economic Literature. Working paper.
Zhangxin Frank Liu and Robert Faff. 2014. “SIX：Proposing an Alternative Skewness Index”. Journal of Economic Literature. Working paper.
邱震宇 (2013) ， 恐懼與國際股票指數的關係，私立逢甲大學財務金融所碩士論文。
紀筌惟 (2013) ，Beveridge-Nelson分解趨勢方法對匯率預測模型績效之影響 -以新台幣兌美元匯率為例，國立政治大學金融學系碩士論文。
陳玉樹 (2011) ，原物料指數與股市、匯市關聯性的研究，國立政治大學金融學系碩士論文。
陳旭昇 (2013) ，時間序列分析：總體經濟與財務金融之應用，第二版。
陳虹均 (2010) ，能源價格衝擊與臺灣總體經濟，國立政治大學國際經營與貿易學系碩士論文。
楊奕農 (2009) ，時間序列分析：經濟與財務上之應用，第二版 。
鍾展弘 (2011) ， VIX、CDS與TED價差對股票市場的影響，國立世新大學財務金融所碩士論文。
謝濱宇 (2011) ，原物料指數與總經物價指數關聯性分析，國立政治大學金融學系碩士論文。 |
| Description: | 碩士
國立政治大學
金融學系
103352020 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0103352020 |
| Data Type: | thesis |
| Appears in Collections: | [金融學系] 學位論文
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