政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/151994

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/151994

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政大典藏 > College of Social Science > Department of Economics > Theses > Item 140.119/151994

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/151994

Title:	利用稀疏因子模型拆解台灣各類股報酬率的波動態勢 Using a Sparse Factor Model to Decompose the Volatility Trends of Various Stock Returns in Taiwan
Authors:	曲慧茹 Qu, Hui-Ru
Contributors:	徐士勛曲慧茹 Qu, Hui-Ru
Keywords:	稀疏因子模型共同因子貝氏估計股價報酬率波動性 Sparse Factor Model Common Factors Bayesian Estimation Stock Price return Volatility
Date:	2024
Issue Date:	2024-07-01 12:19:01 (UTC+8)
Abstract:	本研究分析台灣不同產業類股在不同期間的共同波動性，並觀察這些波動性受到潛在共同因子的影響的程度。我們使用稀疏因子模型將股價報酬率的波動性拆解為共同因子和特殊性成分的組合，以清楚識別市場上系統性風險和個別產業固有衝擊對其股價報酬率的影響。研究結果表明，在遭遇負面的總體或經濟事件時，個股股價報酬率大部分的波動性可以歸咎於共同因子，說明系統性風險在很大程度上影響台灣各產業的股價報酬率。相對地，在一般的經濟環境下，各產業間的股價報酬率具有高度稀疏性，亦即某些特定產業展現出特有的波動方向及程度，股價報酬率不被共同因子所解釋。這些研究結果為投資者在建立投資組合時提供了多樣化的參考依據，透過對數據資料的分析，幫助投資者更精準地評估在遇到類似經濟事件時，該如何選擇合適的個股來配置其資產。 This study analyzes the common volatility among different industry stocks in Taiwan across various periods, focusing on the influence of latent common factors. Using a sparse factor model, we decompose stock return volatility into common factors and idiosyncratic components to identify the impact of systematic risks and industry-specific shocks. The results show that during adverse macroeconomic events, most individual stock volatility is due to common factors, indicating significant systematic risk across industries. Conversely, under normal conditions, stock returns show high sparsity, with certain industries exhibiting unique volatility patterns not explained by common factors. These findings offer valuable insights for investors in building diversified portfolios, helping them to better select appropriate stocks during different economic conditions through data analysis.
Reference:	郭維裕, 李淯靖, 陳致綱, 林建秀 (2015)。台灣產業指數的外溢效果。經濟論文叢刊, 43(4), 407-442。國家發展委員會。台灣景氣指標月刊, 第48卷第2期。 Bauwens, L., Lubrano, M., and Richard, J. F. (1999). Bayesian Inference in Dynamic Econometric Models. Oxford, UK: Oxford University Press. Bhattacharya, A., and Dunson, D. (2011). Sparse Bayesian infinite factor models. Biometrika, 98, 291-306. Boivin, J., and Ng, S. (2006). Are more data always better for factor analysis? Journal of Economics, 132, 169-194. Carhart, M. M. (1997). On persistence in mutual fund performance. The Journal of Finance, 52(1), 57-82. Fama, E.F. and French, K.R. (1993) Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56. Fama, E.F. and French, K.R. (2015) A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1-22. Frhwirth-Schnatter, S., and Lopes, H. F. (2018). Parsimonious Bayesian factor analysis when the number of factors is unknown. Chicago, IL: University of Chicago Booth School of Business. Hanson, T., and McMillan, G. T. (2012). Schefftyle simultaneous credible bands for regression surfaces with application to Ache honey gathering. Journal of Data Science, 10, 175-193. Harvey, C. R., Liu, Y., and Zhu, H. (2015). and the cross-section of expected returns. Review of Financial Studies, 29(1), 5-68. Kaufmann,S. and Schumacher, C. (2017). Identifying relevant and irrelevant variables in sparse factor models. Journal of Applied Economics, 2017, 32, 1123-1144. Onatski, A. (2010). Determining the Number of Factors from Empirical Distribution of Eigenvalues. The Review of Economics and Statistics (2010), 92 (4), 1004-1016. West, M. (2003). Bayesian factor regression models in the “large p, small n” paradigm. Bayesian Statistics, 7, 723-732.
Description:	碩士國立政治大學經濟學系 111258009
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0111258009
Data Type:	thesis
Appears in Collections:	[Department of Economics] Theses

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