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研究生:潘勝昱
論文名稱:總體經濟變數對台灣股價波動性之長、短期影響 --以CARR模型分析
論文名稱(外文):The Long-Short Term Influence of Taiwan Stock Price Volatility by Macroeconomic Variables --Analyze With CARR Model
指導教授:冼芻蕘冼芻蕘引用關係
學位類別:碩士
校院名稱:國立清華大學
系所名稱:經濟學系
學門:社會及行為科學學門
學類:經濟學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:51
中文關鍵詞:風險波動性CARR總體經濟
外文關鍵詞:riskvolatilityCARRmacroeconomic
相關次數:
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本文研究台灣發行量加權股價指數,
分別以日資料與月資料探討不同的總體經濟變數是否會對其波動性有顯著的解釋能力。
在金融市場上,
波動性即是風險,
若能夠準確的捕捉到其他變數對波動性的影響,
那將可以對市場的風險加以控管,
以達到減少損失的目的。
在實證的模型上,
本文採取了Chou(2005)所提出的CARR模型
(Conditional Autoregressive Range model)
來作為主要的風險模型,
並且透過不同的總體經濟變數組合,
檢視這些組合對台灣股價波動性的影響程度。
實證結果發現,
短期股價波動受國際金融市場和國內金融市場影響較為顯著,
但匯率日報酬率較沒有顯著的效果。
而長期股價波動受到匯率月報酬率和美國政府公債的影響較為顯著。
因此,
金融市場短期較容易受到市場上的信息而產生風險,
但長期則受經濟環境的影響居多。
The report influence that Taiwan Weight Stock Index different with daily data and monthly data,
investigate macroeconomic variables if it volatility significant explanatory power.
Volatility is the risk in the financial market.
To make sure that capture well effect volatility by the other variables.
The goal will be control by market risk in order to reduce losses.

In the empirical model selection,
we adopt CARR model(Conditional Autoregressive Range model)
proposed by Chou(2005)
to be the major risk-model,
with kinds of macro combination,
shows the effect level of these combination to Taiwan stock price volatility.
Concluded, short term volatility is more significant between global financial market and internal financial market,
but daily return rate of foreign exchange is not very outstanding.
The long term volatility is more significant between monthly return rate of foreign exchange and U.S. government bond.
So, the risk will get from the short term financial market,
on the other side, long term affect from economics environment.
1 前言.............................6
2 研究方法...........................11
3 資料敘述...........................14
3.1 日資料...........................15
3.2 月資料...........................15
3.3 總體經濟變數的波動性之討論與計算..............16
3.3.1 日資料之經濟變數 ....................17
3.3.2 月資料之經濟變數 ....................23
4 實證結果............................32
4.1 日資料分析.........................32
4.1.1 模型選擇.........................32
4.2 月資料分析.........................39
4.2.1 模型選擇.........................39
5 結論..............................47
參考文獻.............................49

Abdalla, S. A. and V. Murinde (1997), Exchange rate and stock price interactions in emerging financial markets: evidence on India, Korea, Pakistan and the Philippines, Applied Financial Economics, 7, 25-35.

Ajayi, R. A. and M. Mougoue (1996), On the Dynamic Relation between Stock Prices and Exchange Rates, Journal of Financial Research, 19(2), 193-207.

Alizadeh, S., M. Brandt, and F. Diebold (2002), Range-based estimation of stochastic volatility models, Journal of Finance ,57, 1047-1091.

Bollersleve, T. (1986), Generalized Autoregressive Conditional Heteroskedasticity, Journal of Econometrics, 31, 307-327.

Black, F. (1976), Studies of Stock Price Volatility Changes, Proceeding of the 1976 Meetings of the Business and Economics Statistics Section, American Statistical Association, 177-181.

Christie, A. A. (1982), The Stochastic Behavior of Common Stock Variances: Value, Leverage, and Interest Rate Effects, Journal of Financial Economics, 10, 407-432.

Choi, K. H., Z. H. Jiang, S. H. Kang and S. M. Yoon (2012), Relationship between Trading Volume and Asymmetric Volatility in the Korean Stock Market, Modern Economy, 3, 584-589.

Chou, Ray Y. (2005), Forecasting Financial Volatilities with Extreme Values: The Conditional Autoregressive Range (CARR) Model, Journal of Money, Credit, and Banking, 37(3), 561-582.

David, A. and P. Veronesi (2009), Inflation and Earnings Uncertainty and Volatility Forecasts: A Structural Form Approach, Working Paper. Chicago GSB Research Paper.

Driesprong, G., B. Jacobsen and B. Maat (2007), Striking Oil: Another Puzzle?, Working Paper. EFA 2005 Moscow Meetings Paper.

Engle, R. F. (1982), Autoregressive conditional heteroscedasticity with estimates of the variance of U.K. inflation, Econometrica, 50, 987-1008.

\laref Engle, R. F. (2002), New Frontiers for Arch Models, \textit{Journal of Applied Econometrics}, 17, 425-446.

Engle, R. F. and J. G. Rangel (2008), The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes, The Review of Financial Studies, 21(3), 1187-1222.

Engle, R. F., and J. R. Russell (1998), Autoregressive conditional duration: a new model for irregular spaced transaction data, Econometrica, 66, 1127-1162.

Fama, E. F. and G. W. Schwert (1977), Asset Returns and Inflation, Journal of Financial Economics, 5, 115-146.

Fama, E. F. (1981), Stock Returns, Real Activity, Inflation, and Money, American Economic Review, 71(4), 545-565.

Fortune, P. (1989), An Assessment of Financial Market Volatility: Bill, Bonds, and Stocks. New England Economic Review, 13-28.

French, K. H., G. W. Schwert and R. F. Stambaugh (1987), Expected stock returns and volatility, Journal of Financial Economics, 19, 3-29.

Friedman, M. and A. J. Schwartz (1982), The Effect of Term Structure of Interest Rates on the Demand for Money in the United States, Journal of Political Economy, 90(1), 201-212.

Friedman, M. (1988), Money and the Stock Market, Journal of Political Economy, 96(2), 221-245.

Jones, C. M. and G. Kaul (1996), Oil and the Stock Markets, Journal of Finance, 51(2), 463-491.

Kearney, C. and K. Daly (1998), The Causes of Stock Market Volatility in Australia, Applied Financial Economics, 8, 597-605.

Lamoureux, C. G. and W. D. Lastrapes (1990), Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects, Journal of Finance, 45(1), 221-229.

Mandelbrot, B. (1971), When can Price be Arbitraged Efficiently? A Limit to the Validity of the Random Walk and Martingale Models, Review of Economics and Statistics, 53, 225-236.

Morgan, I. G. (1976), Stock Price and Heteroskedasticity, Journal of Business, 49, 496-508.

Officer, R. R. (1973), The Variability of the Market Factor of the New York Stock Exchange, Journal of Business, 46, 434-453.

Parkinson, M. (1980), The extreme value method for estimating the variance of the rate of return, Journal of Business, 53, 61-65.

Pearce, D. K. and V. Raley (1985), Stock Prices and Economic News, Journal of Business, 58, 49-67.

Sadorsky, P. (1999), Oil price shocks and stock market activity, Energy Economics, 21, 449-469.

Shapiro, A. C. (2006), Multinational Financial Management, NJ: John Wiley \& Sons, Inc.

Schwert, G. W. (1988), Tests for Unit Roots: a Monte Carlo Investigation, NBER Technical Working Paper No. 73.

Schwert, G. W. (1989), Why Does Stock Market Volatility Change over Time?, Journal of Finance, 44(5), 1115-1153.

Schwert, G. W. and P. J. Seguin (1990), Heteroskedasticity in Stock Returns, Journal of Finance, 45(4), 1129-1155.
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