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論文名稱 Title |
二階段金融網絡模型 A two-stage financial network model |
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系所名稱 Department |
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畢業學年期 Year, semester |
語文別 Language |
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學位類別 Degree |
頁數 Number of pages |
55 |
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研究生 Author |
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指導教授 Advisor |
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召集委員 Convenor |
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口試委員 Advisory Committee |
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口試日期 Date of Exam |
2017-06-29 |
繳交日期 Date of Submission |
2017-07-20 |
關鍵字 Keywords |
適應Lasso、向量自我迴歸、Lasso、網絡 adaptive Lasso, Lasso, network, vector auto-regression |
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統計 Statistics |
本論文已被瀏覽 5689 次,被下載 22 次 The thesis/dissertation has been browsed 5689 times, has been downloaded 22 times. |
中文摘要 |
我們發展一個建立金融網絡的兩階段程序。在第一階段中,我們為金融資產報酬率建立向量自 我迴歸(VAR)模型。為了克服高維模型中的選擇變量問題,我們採用三種方法:去除偏誤 的Lasso; 適應的Lasso; 逐步迴歸法。我們使用VAR 模型的係數來構建網絡的鄰接矩陣,這決 定了網絡中節點之間的連通性和關係。在第二階段中,我們將每個節點的連接節點視為解釋 變量,構建網絡向量自我迴歸(NAR)模型。此NAR模型用以探討金融系統的網絡和動量效 應,並且進行干擾分析。 |
Abstract |
We develop a two-stage procedure for constructing financial network. In the first stage, we build vector autoregressive (VAR) models for financial asset returns. To overcome the variable selection problem in high-dimensional models, we adopt the three methods: debiased Lasso, adaptive Lasso and a stepwise regression method. We use the coefficients of the VAR models to construct the adjacency matrix of the network, which determines the connectedness and the relationships among the nodes within the network. In the second stage, we treat each node’s connected nodes as an explanatory variable and build a network vector autoregressive (NAR) model. This NAR model is then utilized to investigate the network and momentum effects of the financial system and perform intervention analysis. |
目次 Table of Contents |
誌謝 i 摘要 ii Abstract iii 1 Introduction 1 2 Granger causality network model 3 2.1 Debiased Lasso VAR estimation . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 Adaptive Lasso VAR estimation . . . . . . . . . . . . . . . . . . . . . . . . 7 2.3 OGA+HDIC+Trim VAR estimation . . . . . . . . . . . . . . . . . . . . . 7 3 Network vector autoregression 9 3.1 NAR model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2 Intervention analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 4 Further study on the two stage network 11 4.1 Estimation comparison for the VAR model . . . . . . . . . . . . . . . . . . 11 4.2 Relationship between VAR and NAR coefficients . . . . . . . . . . . . . . . 12 4.3 Intervention analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 5 Empirical study 14 6 Conclusion 19 7 Future work 20 7.1 Penalized quantile regression estimation with Lasso . . . . . . . . . . . . . 20 7.2 Quantile NAR model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 7.3 Simulation study for quantile regression . . . . . . . . . . . . . . . . . . . . 22 7.3.1 Estimate network via VAR and quantile regression models . . . . . 22 7.3.2 Fit quantile NAR model . . . . . . . . . . . . . . . . . . . . . . . . 24 7.3.3 Debiased Lasso VAR and Lasso VAR + LSE . . . . . . . . . . . . . 26 7.4 Empirical study for quantile regression . . . . . . . . . . . . . . . . . . . . 27 8 References 30 9 Appendix 32 9.1 Appendix A: Company Tables . . . . . . . . . . . . . . . . . . . . . . . . . 32 9.2 Appendix B: Choice of the quantile regression Lasso penalty parameter λi 34 9.3 Appendix C: Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 |
參考文獻 References |
[1] Basu, S., Das, S. and Michailidis, G. (2017). A system-wide approach to measure connectivity in the financial sector. Available at SSRN: https://ssrn.com/abstract=2816137, 12–20. [2] Basu, S., Shojaie, A., Michailidis, G. (2015). Network Granger causality with inherent grouping structure. Journal of Machine Learning Research, 16, 417–453. [3] Belloni, A. and Chernozhukov, V. (2011). l1-penalized quantile regression in highdimensional sparse models, The Annals of Statistics, 39, 82–130. [4] Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, 57, 289–300. [5] Billio, M., Getmansky, M., Lo, A.W., and Pelizzon, L. (2012). Econometric measures of connectedness and systemic risk in the finance and insurance sectors. Journal of Financial Economics, 104, 535–559. [6] Feng, X., He, X. and Hu, J. (2011). Wild bootstrap for quantile regression. Biometrika, 98, 995–999. [7] Granger, J. (1969). Investigating causal relations by econometric models and crossspectral methods. Econometrica: Journal of the Econometric Society, 37, 424–438. [8] Hannan, E.J. and Quinn, B.G. (1979). The determination of the order of an autoregression. Journal of the Royal Statistical Society, 41, 190–195. [9] Hautsch, N., Schaumburg, J. and Schienle, M. (2014). Financial network systemic risk contributions. Review of Financial Studies, 19, 685–738. [10] Ing, C.-K. and Lai, T.L. (2011). A stepwise regression method and consistent model selection for high-dimensional sparse linear models. Statistica Sinica, 21, 1473–1513. [11] Javanmard, A. and Montanari, A. (2014). Confidence intervals and hypothesis testing for highdimensional regression. The Journal of Machine Learning Research, 15, 2869– 2909. [12] Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461–-464. [13] Sun, T. and Zhang, C.-H. (2012). Scaled sparse linear regression. Biometrika, 99, 879–898. [14] Zhu, X., Pan, R., Li, G., Liu, Y. and Wang, H. (2017). Network vector autoregression. The Annals of Statistics, 45, 923–1374. [15] Zou, H. (2006). The adaptive Lasso and its oracle properties. Journal of the American Statistical Association 101, 1418–1429. |
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