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研究生:李欣怡
研究生(外文):Sin-Yi Lee
論文名稱:景氣循環下之風險溢酬:新興市場之實證研究
論文名稱(外文):Risk Premium over the Business Cycles:Empirical Evidence from Emerging Markets
指導教授:張淑華張淑華引用關係黃亮洲黃亮洲引用關係
指導教授(外文):Shu-Hwa ChangLiang-Chou Huang
學位類別:碩士
校院名稱:真理大學
系所名稱:財經研究所
學門:商業及管理學門
學類:一般商業學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:47
中文關鍵詞:股市報酬風險溢酬景氣循環
外文關鍵詞:stock returnsrisk premiumbusiness cycle
相關次數:
  • 被引用被引用:1
  • 點閱點閱:157
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本文首先使用結構性時間序列模型研究新興市場-台灣和韓國景氣循環的時間。在實證結果上,發現結構性時間序列模型所捕捉的景氣循環的時間與台灣官方(Council for Economic Planning and Development)和韓國官方(National Statistics Office)所公佈的景氣循環時間點相似,所以結構性時間序列模型是可以掌握景氣循環時間的模型。本文接著使用AR(k)-GJR-GARCH-M模型研究在景氣循環下是否會對台灣和韓國的股市報酬以及波動產生影響。本文研究結果顯示,在景氣收縮的時候: (1)股市報酬的條件均數為負的;(2)風險溢酬為多為正且顯著的。在景氣擴張的時候,實證結果並不顯著。由實證可得知,在景氣循環下台灣和韓國的股市確實會有不同的行為表現。
In this paper we employ the structural time series models to investigate the business cycles in Taiwan and Korea. The empirical evidence of the structural time series models that it well reflect the official business cycles dating of Taiwan set by the Council for Economic Planning and Development (CEPD) and of Korea set by the National Statistics Office (NSO). And then, we employ the AR(k)-GJR-GARCH in mean models allowing for business cycle effects to examine a time varying relation between stock returns and volatility using Taiwan and Korea market index return. We find that both Taiwan and Korea in contraction periods: (1) the conditional means of stock returns are negative; (2) there exists positive and significant risk premium. As to expansion periods, the empirical evidences are insignificant. The empirical evidence suggests that the behavior of stock market seems to be different over the business cycles.
Chapter 1 Introduction.............................................................................................1
Chapter 2 Literature Review....................................................................................6
2.1 Stock Volatility and Business Cycle....................................................6
2.2 Stock Volatility and Stock Return........................................................7
Chapter 3 Methodology .........................................................................................10
3.1 Structural Time Series Model.............................................................10
3.2 The AR(1)-GARCH-M Model………...............................................13
Chapter4 Empirical Results ..................................................................................17
4.1 Data Description.....................................................................................17
4.2 Identifying of Business Cycles...............................................................17
4.2.1 Maximum Likelihood of the Parameter........................................17
4.2.2 Dating Business Cycle and
Classifying Periods of Business Cycle …....................................18
4.3 Empirical Results…………………………..………………………….20
Chapter5 Conclusion……………………..…………..…………………………...23
References… …………...…………………………………………………………...46
Aggarwal, R., C. Inclan and R. Leal (1999), “Volatility in Emerging Stock Markets”, Journal of Financial and Quantitative Analysis, 34(1), 33-55.
Ata, A. (2006), “The Stochastic Volatility in Mean Model and Automation: Evidence from TSE, Quarterly Review of Economics and Finance, 46, 241-253.
Bekaert, G. and C.R. Harvey (1997), “Emerging Equity Market Volatility”, Journal of Financial Economics, 43, 29-77.
Bekaert, G., C.R. Harvey, and C.T. Lundblad (2001), “Emerging Equity Markets and Economic Development”, Journal of Development Economics, 66, 465-504.
Bekaert, G. and G. Wu (2000), “Asymmetric Volatility and Risk in Equity
Markets,” Review of Financial Studies, 13, 1-42.
Black, F. (1976), “Studies of stock price volatility changes”, Proceedings of the American Statistical Association, Business and Economics Statistics Section, 27, 399- 418.
Black, A. and D. McMillan (2006), “Asymmetric Risk Premium in Value and Growth Stocks”, International Review of Financial Analysis, 15, 237-246.
Bollerslev, T. (1986), “Generalised Autoregressive Conditional Heteroskedasticity”, Journal of Econometrics, 31, pp. 307-327.
Campbell, Y. J. and L. Hentschel (1992), “No News is Good News”, Journal of Financial Economics, 31, 281-318.
Chan, F., D. Marinova, and M. McAleer (2005), “Modeling Threshold and Volatility in US Ecological Patents”, Environmental Modeling and Software, 20, 1369-1378.
De Santis G. and S. Imrohoroglu (1997), “Stock Returns and Volatility in Emerging Financial Markets”, Journal of International Money and Finance, 16(4), 561-579.
Diebold, F.X. and G.D. Rudebusch (1989), “Long Memory and Persistence in Aggregate Output”, Journal of Monetary Economics, 24, 189-209.
Engle, R. F. (1982), “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of UK Inflation”, Econometrica, 50, 987-1008.
Engle, R. F. and V. K. Ng (1993), “Measuring and Testing the Impact of News on Volatility”, Journal of Finance, 48, 1749-1777.
Fama, E. F. and K. French (1989), “Business Conditions and Expected Returns on Stocks and Bonds,” Journal of Financial Economics, 25(1), 23-49.
Fama, E. F. (1990), “Stock Returns, Expected Returns, and Real Activity”,
Journal of Finance, 45(4) 1089-1108.
French, K. R., G. William Schwert, and Robert F. Stambaugh (1987), “Expected Stock Returns and Volatility”, Journal of Financial Economics, 19, 3-29.
Glosten, L.R., R. Jagannathan, and D. Runkle (1993), “On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks”, Journal of Finance, 48(5), 1779-1801.
Hamilton, J. D. and G. Lin (1996), “Stock Market Volatility and the Business Cycle”, Journal of Applied Econometrics, 11, 573-593.
Harvey, A.C. (1989), “Forecasting Analysis of Time Series Models and the Kalman Filter”, Cambridge University Press, Cambridge.
Harvey, A. C. and N. Shephard (1993), “Structural time series models”, in Maddala, G.S., Rao, C. R. and Vinod, H. D. (eds), Handbook of Statistics, Amsterdam: Elsevier Science Publishers B V.
McAleer, M., F. Chan, and D. Marinova, (2007) “An Econometric Analysis of Asymmetric Volatility: Theory and Application to Patents”, Journal of Econometrics, 139, 259-284.
Kaiser, T. (1996), “One-Factor-GARCH Models for German Stocks – Estimation and Forecasting”, Universitet Tübingen, Working Paper.
McQueen, G. and V. V. Roley (1993), “Stock Prices, News, and Business Conditions”, Review of Financial Studies, 6(3), 683-707.
Nelson, D. B. (1991), “Conditional Heteroskedasticity in Asset Returns: A New Approach”, Econometrics, 45(2), 347-370.
Ng, H. and M. McAleer (2004), “Recursive Modelling of Symmetric and Asymmetric Volatility in the Presence of Extreme Observations”, International Journal of Forecasting, 20, 115– 129.
Schwert, G. W. (1989), “Why does Stock Market Volatility Change Over Time? ”, Journal of Finance, 44(5), 1115-1153.
Schwert, G. W. (1990), “Stock Returns and Real Activity: a Century of Evidence”, Journal of Finance 45(4), 1237-1257.
Shin, J. (2005), “Stock Returns and Volatility in Emerging Stock Markets”, International Journal of Business and Economics, 4(1), 31-43.
Theodossiou, P. and U. Lee, (1995), “Relationship between Volatility and Expected
Returns across International Stock Markets,” Journal of Business Finance and
Accounting, 22(2), 289-300.
Wu, G. (2001), “The Determinants of Asymmetric Volatility”, Review of
Financial Studies, 14, 837-859.
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