臺灣博碩士論文加值系統

本論文旨在測量與分析台灣股票市場內，九大類股自身之報酬率連結(return connectedness)與波動率連結(volatility connectedness)。論文結果顯示不同產業別之間的連結程度有很大的差異，其中金融業連結程度最高，而貿易百貨業、網路通訊業連結程度最低。另外本論文也針對各產業之報酬率連結與波動率連結做長達17年的動態追蹤，除了發現在所有產業中，此兩種連結的動態有顯著分歧，同時我們也可以觀察到，不同產業別自身連結程度之動態變化。最後，本論文對於影響連結程度的因子做了一些探討，並且發現即使是排除了2008金融海嘯的影響，經濟不景氣仍會顯著的使各產業之報酬率連結與波動率連結上升，另外，各產業之結構也會對其連結程度造成影響。

The empirical objective of this study is to measure the connectedness of stock prices in nine different market segments in Taiwan. For both the return and the volatility of stock prices, this research demonstrate that the connectedness level in different market segments significantly differs from one another. Moreover, the results suggest that the time-varying natures between the return and the volatility connectedness of stock prices are drastically different from each other. In addition, this paper aims to identify the key factors that strengthen or weaken the return and the volatility connectedness of stock prices. The findings suggest that both of them are profoundly influenced by economic downturns and the market structure of the industry.

口試委員會審定書…………………………………………………..….…………. i
謝詞………………………………………………………….………….…………. ii
中文摘要……………………………………………………………….…………. iv
英文摘要…………………………………………………………………..………. v
目錄……………………………………………………………………..………. vi
圖目錄…………………………………………………………………..………. vii
表目錄…………………………………………………………….……..………. viii
CH1. Introduction…………………………………………………….…………… 1
CH2. Measures of Connectedness……………………………………..………….. 3
CH3. Data…………………………….………………………………….……….. 12
CH4. Empirical Results………….……………………………………………….. 16
4.1 Full-Sample Return and Volatility Total Connectedness……...….…..… 17
4.2 Dynamics of Return and Volatility Total Connectedness……….………. 18
4.3 Regression Results………………………….……………..…….………. 26
4.4 Robustness Check………………………….……………..……..………. 33
CH5. Concluding Remarks……………………………………………...……….. 34
Reference.……………………………….………………………………………. 38
Appendix…………………………………………………………………….…… 41

Acharya, Viral V., Lasse H. Pedersen, Thomas Philippon, Matthew Richardson (2017). Measuring Systemic Risk. The Review of Financial Studies, 30, 2-47.

Adrian, Tobias and Markus K. Brunnermeier (2011). CoVaR. NBER Working Paper, No.w17454.

Alizadeh, Sassan. Michael W. Brandt and Francis X. Diebold (2002). Range-Based Estimation of Stochastic Volatility Models. The Journal of Finance, 57, 1047-1091.

Allen, L., T.G. Bali, and Y. Tang (2012). Does Systemic Risk in the Financial Sector Predict Future Economic Downturns? Review of Financial Studies, 25, 3000-3036.

Billio, Monica., Mila Getmansky, Andrew W. Lo, Loriana Pelizzon (2012). Econometric Measures of Connectedness and Systemic Risk in the Finance and Insurance Sector. Journal of Financial Economics, 104, 535-559.

Dees, Stephane., Filippo di Mauro, M. Hashem Pesaran and L. Vanessa Smith. (2007). Exploring the International Linkages of the Euro Area: A Global VAR Analysis. Journal of Applied Econometrics, 22, 1-38.

Demirer, M., F.X. Diebold, L. Liu, K. Ylmaz (2017). Estimating Global Bank Network Connectedness. NBER Working Paper, No. 23140.

Diebold, F.X. and Yilmaz, K. (2009). Measuring Financial Asset Return and Volatility Spillovers, With Application to Global Equity Markets. Economic Journal, 119, 158-171.

Diebold, F.X. and Yilmaz, K. (2012). Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers. International Journal of Forecasting, 28, 57-66.

Diebold, F.X. and Yilmaz, K. (2013). Measuring the Dynamics of Global Business Cycle Connectedness. PIER Working Paper, No.13-070.

Diebold, F.X. and Yilmaz, K. (2014). On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms. Journal of Econometrics, 182, 119-134.

Diebold, F.X. and Yilmaz, K. (2015). Financial and Macroeconomics Connectedness : A Network Approach to Measurement and Monitoring, Oxford.

Engel, R., and B. Kelly. (2012). Dynamic Equicorrelation. Journal of Business and Economic Statistics, 30, 212-228.

Fleming, J., C. Kirby, and B. Ostiek (2001), The Economic Value of Volatility Timing. Journal of Finance, 56, 329-352.

Forbes, K.J. and R.Rigobon (2002). No Contagion, Only Interdependence: Measuring Stock Market Comovements. Journal of Finance, 57, 2223-2261.

Garman, Mark B. and Michael J. Klass. (1980). On the Estimation of Security Price Volatilities from Historical Data. The Journal of Business, 53, 67-78.

Hamilton, James D. (1994). Time Series Analysis, Princeton: Princeton University Press.

Jorda, Oscar. (2005). Estimation and Inference of Impulse Responses by Local Projections. American Economic Review, 95, 161-182.

King, M., E.Sentana, and S.Wadhwani (1994). Volatility and Links Between National Stock Markets. Econometrica, 62, 901-933.

Kirby, C. and B. Ostdiek (2012). It''s All in the Timing: Simple Active Profolio Strategies that Outperform Naive Diversi cation. Journal of Financial and Quantitative Analysis, 47, 437-467.

Koop, G., M.H. Pesaran, S.M. Potter (1996). Impulse Response Analysis in Nonlinear Multivariate Models. Journal of Econometrics, 74, 119-147.

Kuan, C.M., U.L. Huang (2013). Vector Autoregressive Models: Economic Methods with R , Yeh Yeh book gallery.

Newey, Whitney K. and Kenneth D. West (1987). A Simple, Positive Semi-De nite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix. Econometrica, 55, 703-708.

Newey, Whitney K. and Kenneth D. West (1994). Automatic Lag Selection in Covariance Matrix Estimation. Review of Economic Studies, 61, 631-653.

Pesaran, Hashem M., Til Schuermann and Scott M Weiner. (2004). Modeling Regional Interdependencies Using a Global Error-Correcting Macroeconometric Model. Journal of Business and Economic Statistics, 22, 129-162.

Pesaran, H. Hashem and Yongcheol Shin. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58, 17-29.

Sims, Christopher A. (1980). Macroeconomics and Reality. Econometrica, 48, 1-48.