請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/59386完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 葉小蓁 | |
| dc.contributor.author | TA-WEI HUANG | en |
| dc.contributor.author | 黃大維 | zh_TW |
| dc.date.accessioned | 2021-06-16T09:22:11Z | - |
| dc.date.available | 2020-07-07 | |
| dc.date.copyright | 2017-07-07 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-06-26 | |
| dc.identifier.citation | [1] Attanasio, O. P. (1991): “Risk, time-varying second moments and market efficiency,” The Review of Economic Studies, 58(3), 479-494.
[2] Bauwens, L., & Storti, G. (2007): “A component GARCH model with time varying weights,” CORE. [3] Bauwens, L., Laurent, S., & Rombouts, J. V. (2006): “Multivariate GARCH models: a survey,” Journal of applied econometrics, 21(1), 79-109. [4] Bertsimas, D., Lauprete, G. J., & Samarov, A. (2004): “Shortfall as a risk measure: properties, optimization and applications,” Journal of Economic Dynamics and control, 28(7), 1353-1381. [5] Bollerslev, T., Engle, R. F., & Nelson, D. B. (1994): “ARCH models,” Handbook of econometrics, 4, 2959-3038. [6] Bollerslev, T., Engle, R. F., & Wooldridge, J. M. (1988): “A capital asset pricing model with time-varying covariances,” Journal of political Economy, 96(1), 116-131. [7] Broda, S. A., & Paolella, M. S. (2009): “Chicago: A fast and accurate method for portfolio risk calculation,” Journal of Financial Econometrics, nbp011. [8] Campbell, R., Huisman, R., & Koedijk, K. (2001): “Optimal portfolio selection in a value-at-risk framework,” Journal of Banking & Finance, 25(9), 1789-1804. [9] Chan, L. K., Karceski, J., & Lakonishok, J. (1999): “On portfolio optimization: Forecasting covariances and choosing the risk model,” Review of Financial Studies, 12(5), 937-974. [10] Chang, C., & Tsay, R. S. (2010): “Estimation of covariance matrix via the sparse Cholesky factor with lasso,” Journal of Statistical Planning and Inference, 140(12), 3858-3873. [11] Chen, Y., Hrdle, W., & Spokoiny, V. (2007): “Portfolio value at risk based on independent component analysis,” Journal of Computational and Applied Mathematics, 205(1), 594-607. [12] DeMiguel, V., Garlappi, L., & Uppal, R. (2009): “Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy?” Review of Financial Studies, 22(5), 1915-1953. [13] Duchesne, P., & Lalancette, S. (2003): “On testing for multivariate ARCH effects in vector time series models,” Canadian Journal of Statistics, 31(3), 275-292. [14] Dufour, J. M., & Roy, R. (1985): “Some robust exact results on sample autocorrelations and tests of randomness,” Journal of Econometrics, 29(3), 257-273. [15] Dufour, J. M., & Roy, R. (1986): “Generalized portmanteau statistics and tests of randomness,” Communications in Statistics-Theory and Methods, 15(10), 2953-2972. [16] Elton, E. J., Gruber, M. J., & Padberg, M. W. (1976): “Simple criteria for optimal portfolio selection,” The Journal of Finance, 31(5), 1341-1357. [17] Engle, R. (2001): “GARCH 101: The use of ARCH/GARCH models in applied econometrics,” The Journal of Economic Perspectives, 15(4), 157-168. [18] Engle, R. (2002): “Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models,” Journal of Business & Economic Statistics, 20(3), 339-350. [19] Engle, R. F., & Bollerslev, T. (1986): “Modelling the persistence of conditional variances,” Econometric reviews, 5(1), 1-50. [20] Engle, R. F., & Kroner, K. F. (1995): “Multivariate simultaneous generalized ARCH,” Econometric theory, 11(01), 122-150. [21] Engle, R. F., & Sheppard, K. (2001): “Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH(No. w8554): “National Bureau of Economic Research. [22] Fan, J., Wang, M., & Yao, Q. (2008): “Modelling multivariate volatilities via conditionally uncorrelated components,” Journal of the Royal Statistical Society: series B (statistical methodology), 70(4), 679-702. [23] Farinelli, S., Ferreira, M., Rossello, D., Thoeny, M., & Tibiletti, L. (2008): “Beyond Sharpe ratio: Optimal asset allocation using different performance ratios,” Journal of Banking & Finance, 32(10), 2057-2063. [24] Frost, P. A., & Savarino, J. E. (1986): “An empirical Bayes approach to efficient portfolio selection,” Journal of Financial and Quantitative Analysis, 21(03), 293-305. [25] Hafner, C. M. (2003): “Fourth moment structure of multivariate GARCH models,” Journal of Financial Econometrics, 1(1), 26-54. [26] Hendricks, D. (1996): “Evaluation of value-at-risk models using historical data (digest summary): “Economic Policy Review Federal Reserve Bank of New York, 2(1), 39-67. [27] Hu, Y. P., & Tsay, R. S. (2014): “Principal volatility component analysis,” Journal of Business & Economic Statistics, 32(2), 153-164. [28] Hyndman, R. J., & Khandakar, Y. (2007): “Automatic time series for forecasting: the forecast package for R(No. 6/07): “Monash University, Department of Econometrics and Business Statistics. [29] Hyv, A. (1999): “Fast and robust fixed-point algorithms for independent component analysis,” IEEE Transactions on Neural Networks, 10(3), 626-634. [30] Konno, H., & Yamazaki, H. (1991): “Mean-absolute deviation portfolio optimization model and its applications to Tokyo stock market,” Management science, 37(5), 519-531. [31] Krokhmal, P., Palmquist, J., & Uryasev, S. (2002): “Portfolio optimization with conditional value-at-risk objective and constraints,” Journal of risk, 4, 43-68. [32] Ledoit, O., & Wolf, M. (2003): “Improved estimation of the covariance matrix of stock returns with an application to portfolio selection,” Journal of empirical finance, 10(5), 603-621. [33] Ledoit, O., & Wolf, M. (2008): “Robust performance hypothesis testing with the Sharpe ratio,” Journal of Empirical Finance, 15(5), 850-859. [34] Li, D., & Ng, W. L. (2000): “Optimal dynamic portfolio selection: Multiperiod mean variance formulation,” Mathematical Finance, 10(3), 387-406. [35] Ling, S., & McAleer, M. (2003): “Asymptotic theory for a vector ARMAGARCH model,” Econometric theory, 19(02), 280-310. [36] Markowitz, H. (1952): “Portfolio selection,” The journal of finance, 7(1), 77-91. [37] Markowitz, H. M. (1991): “Foundations of portfolio theory,” The journal of finance, 46(2), 469-477. [38] Matteson, D. S., & Tsay, R. S. (2011): “Dynamic orthogonal components for multivariate time series,” Journal of the American Statistical Association, 106(496), 1450-1463. [39] Matteson, D. S., & Tsay, R. S. (2017): “Independent component analysis via distance covariance,” Journal of the American Statistical Association, Advance online publication, 1-38. [40] Rockafellar, R. T., & Uryasev, S. (2000): “Optimization of conditional value-at-risk.Journal of risk,2, 21-42. [41] Sharpe, W. F. (1994): “The sharpe ratio,” The journal of portfolio management, 21(1), 49-58. [42] Shen, W., Wang, J., Jiang, Y. G., & Zha, H. (2015, June): “Portfolio Choices with Orthogonal Bandit Learning. InIJCAI (p. 974): “ [43] Silvennoinen, A., & Tersvirta, T. (2009): “Multivariate GARCH models,” Handbook of financial time series, 201-229. [44] Tsay, R. S. (2005): “Analysis of financial time series(Vol. 543): “John Wiley & Sons. [45] Tsay, R. S. (2013): “Multivariate Time Series Analysis: with R and financial applications,” John Wiley & Sons. [46] Tse, Y. K. (2000): “A test for constant correlations in a multivariate GARCH model,” Journal of econometrics, 98(1), 107-127. [47] Van der Weide, R. (2002): “GOGARCH: a multivariate generalized orthogonal GARCH model,” Journal of Applied Econometrics, 17(5), 549-564. [48] Wu, E. H., & Philip, L. H. (2005, July): “Volatility modelling of multivariate financial time series by using ICA-GARCH models,” In International Conference on Intelligent Data Engineering and Automated Learning(pp. 571- 579): Springer Berlin Heidelberg. [49] Yeh, H. C. (2006): “Time Series Analysis and Application,” National Taiwan University. [50] Zhou, X. Y., & Li, D. (2000): “Continuous-time mean-variance portfolio selection: A stochastic LQ framework,” Applied mathematics & optimization, 42(1), 19-33. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/59386 | - |
| dc.description.abstract | 本文基於傳統兩階段的 Markowitz 投資組合選取理論，將其拓展成較容易時做的三階段投資組合選取架構。透過新加入的「投資組合建構」階段，我們可以巧妙地避開高維度共變異數矩陣估計與預測的問題，並妥善運用較為成熟的單元波動率模型。
此外，於本文我們運用了三種多元波動率因子模型，四種投資組合選取策略，以及兩種經過風險調整後的報酬率指標，進行最佳化投資組合的選取，並將其運用在兩組實務資料中。我們的資料包含匯率資料以及半導體類股資料，實證結果相當優異。 根據提出的交易策略，我們進行了縝密的分析。結果顯示：(1) 投資組合預期報酬的預測準確度並不是最重要的原因 (2) 我們的策略完全打敗傳統的最小變異數投資組合與等權重投資組合。 | zh_TW |
| dc.description.abstract | In this thesis, we extend the traditional Markowitz's procedure to an easy-to-implement three-stage portfolio selection framework. By introducing the portfolio derivation strategy, we smartly avoid the problem of high-dimensional covariance matrix forecasting and leverage the maturity of univariate volatility models.
Specifically, we apply 3 portfolio derivation strategies by factor volatility models, 4 portfolio selection strategies, and 2 risk-adjusted return portfolio selection measures. We implement these algorithms on foreign exchange rate dataset and the semiconductor stock dataset, leading to outstanding performances. We also conduct detailed analyses about our proposed trading strategies. The result suggests that (1) the forecast accuracy of portfolio returns is not the most important thing and (2) our proposed strategies outperforms traditional minimum-variance and equally-weighted portfolios. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T09:22:11Z (GMT). No. of bitstreams: 1 ntu-106-R04h41005-1.pdf: 2244425 bytes, checksum: 3674f648ef6443b89006f0213ec30b4b (MD5) Previous issue date: 2017 | en |
| dc.description.tableofcontents | Acknowledgments i
Abstract iii List of Figures viii List of Tables xi Chapter 1 Introduction 1 1.1 Portfolio Selection Problem . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Mean and Volatility Forecasting . . . . . . . . . . . . . . . . . . . . . 4 1.3 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Chapter 2 Literature Review 8 2.1 Conditional Heteroscedasticity . . . . . . . . . . . . . . . . . . . . . . 9 2.1.1 Portmanteau Test . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1.2 Rank-based Test . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2 Multivariate ARCH Model . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2.1 VEC(1,1) Model . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2.2 Bekk(1,1) Model . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2.3 EWMA Model . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2.4 DCC Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3 Portfolio Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.3.1 Global Minimum Variance Portfolio . . . . . . . . . . . . . . . 14 2.3.2 Safety First Portfolio . . . . . . . . . . . . . . . . . . . . . . . 15 2.3.3 Value at Risk Based Optimization . . . . . . . . . . . . . . . . 16 Chapter 3 Methodology 18 3.1 Problem Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.2 Portfolio Derivation Strategy . . . . . . . . . . . . . . . . . . . . . . . 19 3.2.1 Principal Component Analysis . . . . . . . . . . . . . . . . . . 20 3.2.2 Independent Component Analysis . . . . . . . . . . . . . . . . 21 3.2.3 Principal Volatility Component Analysis . . . . . . . . . . . . 23 3.2.4 Univariate Volatility Modeling . . . . . . . . . . . . . . . . . . 26 3.3 Portfolio Selection Strategy . . . . . . . . . . . . . . . . . . . . . . . 28 3.3.1 Types of Portfolio Selection Strategies . . . . . . . . . . . . . 28 3.3.2 Confidence Bound Strategy . . . . . . . . . . . . . . . . . . . 30 3.3.3 Maximum Sharpe-ratio Strategy . . . . . . . . . . . . . . . . . 32 Chapter 4 Empirical Analysis 34 4.1 Empirical Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.1.1 Training and Testing Schema . . . . . . . . . . . . . . . . . . 35 4.1.2 Parameter Tuning . . . . . . . . . . . . . . . . . . . . . . . . . 36 4.1.3 Evaluation Criteria . . . . . . . . . . . . . . . . . . . . . . . . 36 4.1.4 Benchmark Portfolio . . . . . . . . . . . . . . . . . . . . . . . 38 4.2 Foreign Exchange Data . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.2.1 Data Description . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.2.2 Explanatory Analysis . . . . . . . . . . . . . . . . . . . . . . . 39 4.2.3 Confidence Bound Strategy . . . . . . . . . . . . . . . . . . . 42 4.2.4 Maximum Sharpe-ratio Strategy . . . . . . . . . . . . . . . . . 62 4.3 Semiconductor Stock Data . . . . . . . . . . . . . . . . . . . . . . . . 75 4.3.1 Data Description . . . . . . . . . . . . . . . . . . . . . . . . . 75 4.3.2 Explanatory Analysis . . . . . . . . . . . . . . . . . . . . . . . 76 4.3.3 Confidence Bound Strategy . . . . . . . . . . . . . . . . . . . 79 4.3.4 Maximum Sharpe-ratio Strategy . . . . . . . . . . . . . . . . . 97 Chapter 5 Conclusion 110 5.1 Analysis of the Proposed Strategies . . . . . . . . . . . . . . . . . . . 111 5.2 Practical Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 Bibliography 113 Appendix - R Functions 120 | |
| dc.language.iso | en | |
| dc.subject | 極大夏普指標策略 | zh_TW |
| dc.subject | 條件異質變異數 | zh_TW |
| dc.subject | 多元波動率模型 | zh_TW |
| dc.subject | 投資組合選取 | zh_TW |
| dc.subject | 信賴界線策略 | zh_TW |
| dc.subject | Conditional Heteroscadasticity | en |
| dc.subject | Maximum Sharpe-ratio Strategy | en |
| dc.subject | Confidence Bound Strategy | en |
| dc.subject | Portfolio Selection | en |
| dc.subject | Factor Volatility Model | en |
| dc.title | 多元波動率因子模型於最佳投資組合選取之研究 | zh_TW |
| dc.title | The Study of Optimal Portfolio Selection with Factor Multivariate Volatility Models | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 許耀文,蘇永成 | |
| dc.subject.keyword | 條件異質變異數,多元波動率模型,投資組合選取,信賴界線策略,極大夏普指標策略, | zh_TW |
| dc.subject.keyword | Conditional Heteroscadasticity,Factor Volatility Model,Portfolio Selection,Confidence Bound Strategy,Maximum Sharpe-ratio Strategy, | en |
| dc.relation.page | 128 | |
| dc.identifier.doi | 10.6342/NTU201700985 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2017-06-27 | |
| dc.contributor.author-college | 共同教育中心 | zh_TW |
| dc.contributor.author-dept | 統計碩士學位學程 | zh_TW |
| 顯示於系所單位： | 統計碩士學位學程 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-106-1.pdf 未授權公開取用 | 2.19 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。