政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/151993

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/151993

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政大機構典藏 > 社會科學學院 > 經濟學系 > 學位論文 > Item 140.119/151993

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题名:	以稀疏貝氏因子模型分析台灣及亞洲各國的匯率變動態勢 Analyzing the Exchange Rate Volatility of Taiwan and Asian Countries Using Sparse Bayesian Factor Models
作者:	李怡䋸 Li, Yi-Syun
贡献者:	徐士勛李怡䋸 Li, Yi-Syun
关键词:	匯率因子模型稀疏因子加載矩陣貝氏估計 Exchange Rate Factor Model Sparse Factor Loading Matrix Bayesian Estimation
日期:	2024
上传时间:	2024-07-01 12:18:50 (UTC+8)
摘要:	隨著亞洲產業鏈的規模日益擴大且趨於完整，區域內的經貿活動往來越來越緊密，各國間的貨幣相關性也因而日漸顯著。本研究採用 Kaufmann and Schuhmacher (2017, Journal of Applied Econometrics) 提出的稀疏貝氏因子模型，觀察各國貨幣之間的關聯性。本研究將樣本依照全球金融危機、歐債危機及新冠疫情，分成 4 個子樣本，再透過因子模型的估計結果，將各國貨幣區分為相關與不相關變數兩類。實證結果顯示，兩層先驗機制在大部分的時期都會比一層先驗機制可多識別出 1 至 2 個不相關變數，並且在所有子時期皆可識別出匯率走勢與其他貨幣很不一致的港幣 (採聯絡匯率制度)。因此，兩層先驗機制較一層先驗機制有更好的識別能力。此外，比較台幣、日圓及韓圜匯率相較於共同波動的變動態勢，殘差分析的結果顯示，不論在哪一個子時期，台幣殘差的波動幅度都遠小於日圓與韓圜殘差的波動幅度，代表台幣的匯率多隨共同因子變動，而非受自我因素影響。 As the scale and completeness of the industrial chain in Asia continue to expand, economic and trade activities within the region have become more closely intertwined, leading to more significant currency correlations among the countries. This study employs the sparse Bayesian factor model proposed by Kaufmann and Schuhmacher (2017, Journal of Applied Econometrics) to observe the relationships between the exchange rates of various countries. The study divides the sample into four sub-samples based on the Global Financial Crisis, the European Debt Crisis, and the COVID-19. Using the results estimated by the factor model, currencies are classified into relevant and irrelevant variables. The empirical results show that the two-layer prior mechanism identifies 1 to 2 more irrelevant variables than the one-layer mechanism prior in most periods. Furthermore, HKD (linked exchange rate system) is consistently identified as an irrelevant variable due to its exchange rate trends being very inconsistent with other currencies in all sub-periods. Therefore, the two-layer prior mechanism has better identification capability compared to the one-layer prior mechanism. Additionally, by comparing the fluctuation trends of TWD, JPY, and KRW relative to the common factors, residual analysis results indicate that the volatility of TWD residuals is significantly lower than that of JPY and KRW residuals in every sub-period. This suggests that the exchange rate of TWD is more influenced by common factors rather than idiosyncratic component.
參考文獻:	Bhattacharya, A. and Dunson, D. (2011). Sparse Bayesian infinite factor models. Biometrika, 98, 291–306. Carvalho, C. M., Chang, C., Lucas, J. E., Nevins, J. R.,Wang, Q., and West, M. (2008). High-dimensional sparse factor modeling: Applications in gene expression genomics. Journal of the American Statistical Association, 103, 1438–1456. Francis, N., Owyang, M. T. and Savascin, O. (2017). An endogenously clustered factor approach to international business cycles. Journal of Applied Econometrics, 32(7), 1261–1276. Frühwirth-Schnatter, S. and Lopes, H. F. (2010). Parsimonious Bayesian factor analysis when the number of factors is unknown. Research report, Booth School of Business, University of Chicago. Frühwirth-Schnatter, S. and Lopes, H. F. (2018). Sparse Bayesian factor analysis when the number of factors is unknown. arXiv, 1804.04231. Kaufmann, S. and Schumacher, C. (2017). Identifying relevant and irrelevant variables in sparse factor models. Journal of Applied Econometrics, 32(6), 1123–1144. Kim, Y. and Ying, Y. H. (2007). An empirical assessment of currency devaluation in East Asian countries. Journal of International Money and Finance, 26(2), 265-283. Lucas, J., Carvalho, C., Wang, Q., Bild, A., Nevins, J., and West, M. (2006). Sparse statistical modelling in gene expression genomics. In Do, KA, Müller, P, and Vannucci, M (Eds.), Bayesian inference for gene expression and proteomics (pp. 155–176). Cambridge, UK: Cambridge University Press. Mackowiak, B., Mönch, E. and Wiederholt, M. (2009). Sectoral price data and models of price setting. Journal of Monetary Economics, 56, S78–S99. Onatski, A. (2010). Determining the number of factors from empirical distribution of eigenvalues. The Review of Economics and Statistics, 92(4), 1004-1016. West, M. (2003). Bayesian factor regression models in the “large p, small n” paradigm. Bayesian Statistics, 7, 723–732.
描述:	碩士國立政治大學經濟學系 111258008
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0111258008
数据类型:	thesis
显示于类别:	[經濟學系] 學位論文

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