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題名 隱藏式馬可夫狀態轉換模型下的動態資產配置
Dynamic Asset Allocation under Hidden Markov Regime Switching Model
作者 盧建豪
Lu, Chien-Hou
貢獻者 江彌修
Chiang, Mi-Hsiu
盧建豪
Lu, Chien-Hou
關鍵詞 資產配置
隱藏式馬可夫模型
狀態轉換模型
分層式風險平價
Asset Allocation
Hidden Markov Model
Regime-switching Model
Hierarchical Risk Parity
日期 2022
上傳時間 1-Aug-2022 17:30:05 (UTC+8)
摘要 本文探討隱藏式馬可夫狀態轉換模型是否能夠使投資組合績效上升。基於隱
藏式馬可夫模型對台灣加權指數進行狀態分類後,得到兩種狀態:高波動低報酬
狀態與低波動高報酬狀態。本文建立逆變異數加權、分層式風險評價與二次規劃
最小化變異數三個投資組合,並採用 2007 年 1 月 4 日至 2021 年 12 月 31 日的台灣股市資料與美債報酬進行回測。實證發現,狀態轉換下的動態資產配置能使投資組合的夏普比率、索提諾比率與最大策略虧損報酬上升,其中又以加入狀態轉換模型的逆變異數加權投資組合表現最佳。
This paper explores whether a hidden Markov regime-switching model can improve portfolio performance. After we classify the Taiwan Weighted Index based on the Hidden Markov Model, two states are obtained: the state of high volatility and low return, and the state of low volatility and high return. This paper constructs three portfolios: inverse variance weighting, hierarchical risk parity and quadratic programming minimizing variance. We conduct backtests based on Taiwan stock market and U.S. bond data from January 4, 2007 to December 31, 2021. We find that
the dynamic asset allocation under the regime-switching model can increase the Sharpe ratio, Sortino ratio and maximum drawdown return of the portfolio. The inverse
variance weighted portfolio under the regime-switching model performs the best.
參考文獻 Ang, A., & Bekaert, G. (2002). International asset allocation with regime shifts. The
review of financial studies, 15(4), 1137-1187.
Bailey, D. H., & Lopez de Prado, M. (2012). The Sharpe ratio efficient frontier. Journal
of Risk, 15(2), 13.
Bulla, J., Mergner, S., Bulla, I., Sesboüé, A., & Chesneau, C. (2011). Markov-switching
asset allocation: Do profitable strategies exist?. Journal of Asset
Management, 12(5), 310-321.
Cajas, D. (2022). Riskfolio-lib (3.0.0). Retrieved from
https://github.com/dcajasn/Riskfolio-Lib
Costa, G., & Kwon, R. (2020). A Regime-Switching Factor Model for Mean-Variance
Optimization. Journal of Risk.
De Prado, M. L. (2016). Building diversified portfolios that outperform out of
sample. The Journal of Portfolio Management, 42(4), 59-69.
Konstantinov, G., Chorus, A., & Rebmann, J. (2020). A network and machine learning
approach to factor, asset, and blended allocation. The Journal of Portfolio
Management, 46(6), 54-71.
Kritzman, M., Page, S., & Turkington, D. (2012). Regime shifts: Implications for
dynamic strategies (corrected). Financial Analysts Journal, 68(3), 22-39.
Lo, A. W. (2002). The statistics of Sharpe ratios. Financial analysts journal, 58(4), 36-
52.
Meucci, A. (2009). Managing diversification. Risk, 74-79.
Nystrup, P. (2014). Regime-based asset allocation. Do profitable strategies
exist. Master`s thesis, Technical University of Denmark.
Nystrup, P., Madsen, H., & Lindström, E. (2017). Long memory of financial time series
39
and hidden Markov models with time‐varying parameters. Journal of
Forecasting, 36(8), 989-1002.
Papenbrock, J. (2011). Asset Clusters and Asset Networks in Financial Risk
Management and Portfolio Optimization (Doctoral dissertation, Dissertation,
Karlsruhe, Karlsruher Institut für Technologie (KIT), 2011).
Pfitzinger, J., & Katzke, N. (2019). A constrained hierarchical risk parity algorithm with
cluster-based capital allocation. Stellenbosch University, Department of
Economics.
Prajogo, A. U. (2011). Analyzing patterns in the equity market: ETF investor sentiment
and corporate cash holding. Princeton University.
Visser, I., Raijmakers, M. E., & Molenaar, P. C. (2000). Confidence intervals for hidden
Markov model parameters. British journal of mathematical and statistical
psychology, 53(2), 317-327.
Wang, M., Lin, Y. H., & Mikhelson, I. (2020). Regime-switching factor investing with
hidden Markov models. Journal of Risk and Financial Management, 13(12), 311.
Yue, S., Wang, X., & Wei, M. (2008). Application of two-order difference to gap
statistic. Transactions of Tianjin University, 14(3), 217-221.
描述 碩士
國立政治大學
金融學系
109352027
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109352027
資料類型 thesis
dc.contributor.advisor 江彌修zh_TW
dc.contributor.advisor Chiang, Mi-Hsiuen_US
dc.contributor.author (Authors) 盧建豪zh_TW
dc.contributor.author (Authors) Lu, Chien-Houen_US
dc.creator (作者) 盧建豪zh_TW
dc.creator (作者) Lu, Chien-Houen_US
dc.date (日期) 2022en_US
dc.date.accessioned 1-Aug-2022 17:30:05 (UTC+8)-
dc.date.available 1-Aug-2022 17:30:05 (UTC+8)-
dc.date.issued (上傳時間) 1-Aug-2022 17:30:05 (UTC+8)-
dc.identifier (Other Identifiers) G0109352027en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/141066-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 金融學系zh_TW
dc.description (描述) 109352027zh_TW
dc.description.abstract (摘要) 本文探討隱藏式馬可夫狀態轉換模型是否能夠使投資組合績效上升。基於隱
藏式馬可夫模型對台灣加權指數進行狀態分類後,得到兩種狀態:高波動低報酬
狀態與低波動高報酬狀態。本文建立逆變異數加權、分層式風險評價與二次規劃
最小化變異數三個投資組合,並採用 2007 年 1 月 4 日至 2021 年 12 月 31 日的台灣股市資料與美債報酬進行回測。實證發現,狀態轉換下的動態資產配置能使投資組合的夏普比率、索提諾比率與最大策略虧損報酬上升,其中又以加入狀態轉換模型的逆變異數加權投資組合表現最佳。
zh_TW
dc.description.abstract (摘要) This paper explores whether a hidden Markov regime-switching model can improve portfolio performance. After we classify the Taiwan Weighted Index based on the Hidden Markov Model, two states are obtained: the state of high volatility and low return, and the state of low volatility and high return. This paper constructs three portfolios: inverse variance weighting, hierarchical risk parity and quadratic programming minimizing variance. We conduct backtests based on Taiwan stock market and U.S. bond data from January 4, 2007 to December 31, 2021. We find that
the dynamic asset allocation under the regime-switching model can increase the Sharpe ratio, Sortino ratio and maximum drawdown return of the portfolio. The inverse
variance weighted portfolio under the regime-switching model performs the best.
en_US
dc.description.tableofcontents 第一章 緒論 1
第二章 文獻回顧 4
第三章 研究方法 7
第一節 隱藏式馬可夫模型 7
第二節 原始投資組合 8
第三節 限制股債比例投資組合 12
第四節 股債分別優化投資組合 12
第五節 投資組合比較 13
第四章 實證結果與分析 15
第一節 資料敘述 15
第二節 隱藏式馬可夫模型 17
第三節 無限制股債比例投組 22
第四節 狀態轉換機率投資組合 25
第五節 股債分別優化 28
第六節 投資組合比較 31
第五章 結論與後續建議 36
參考文獻 38
zh_TW
dc.format.extent 2843961 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109352027en_US
dc.subject (關鍵詞) 資產配置zh_TW
dc.subject (關鍵詞) 隱藏式馬可夫模型zh_TW
dc.subject (關鍵詞) 狀態轉換模型zh_TW
dc.subject (關鍵詞) 分層式風險平價zh_TW
dc.subject (關鍵詞) Asset Allocationen_US
dc.subject (關鍵詞) Hidden Markov Modelen_US
dc.subject (關鍵詞) Regime-switching Modelen_US
dc.subject (關鍵詞) Hierarchical Risk Parityen_US
dc.title (題名) 隱藏式馬可夫狀態轉換模型下的動態資產配置zh_TW
dc.title (題名) Dynamic Asset Allocation under Hidden Markov Regime Switching Modelen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Ang, A., & Bekaert, G. (2002). International asset allocation with regime shifts. The
review of financial studies, 15(4), 1137-1187.
Bailey, D. H., & Lopez de Prado, M. (2012). The Sharpe ratio efficient frontier. Journal
of Risk, 15(2), 13.
Bulla, J., Mergner, S., Bulla, I., Sesboüé, A., & Chesneau, C. (2011). Markov-switching
asset allocation: Do profitable strategies exist?. Journal of Asset
Management, 12(5), 310-321.
Cajas, D. (2022). Riskfolio-lib (3.0.0). Retrieved from
https://github.com/dcajasn/Riskfolio-Lib
Costa, G., & Kwon, R. (2020). A Regime-Switching Factor Model for Mean-Variance
Optimization. Journal of Risk.
De Prado, M. L. (2016). Building diversified portfolios that outperform out of
sample. The Journal of Portfolio Management, 42(4), 59-69.
Konstantinov, G., Chorus, A., & Rebmann, J. (2020). A network and machine learning
approach to factor, asset, and blended allocation. The Journal of Portfolio
Management, 46(6), 54-71.
Kritzman, M., Page, S., & Turkington, D. (2012). Regime shifts: Implications for
dynamic strategies (corrected). Financial Analysts Journal, 68(3), 22-39.
Lo, A. W. (2002). The statistics of Sharpe ratios. Financial analysts journal, 58(4), 36-
52.
Meucci, A. (2009). Managing diversification. Risk, 74-79.
Nystrup, P. (2014). Regime-based asset allocation. Do profitable strategies
exist. Master`s thesis, Technical University of Denmark.
Nystrup, P., Madsen, H., & Lindström, E. (2017). Long memory of financial time series
39
and hidden Markov models with time‐varying parameters. Journal of
Forecasting, 36(8), 989-1002.
Papenbrock, J. (2011). Asset Clusters and Asset Networks in Financial Risk
Management and Portfolio Optimization (Doctoral dissertation, Dissertation,
Karlsruhe, Karlsruher Institut für Technologie (KIT), 2011).
Pfitzinger, J., & Katzke, N. (2019). A constrained hierarchical risk parity algorithm with
cluster-based capital allocation. Stellenbosch University, Department of
Economics.
Prajogo, A. U. (2011). Analyzing patterns in the equity market: ETF investor sentiment
and corporate cash holding. Princeton University.
Visser, I., Raijmakers, M. E., & Molenaar, P. C. (2000). Confidence intervals for hidden
Markov model parameters. British journal of mathematical and statistical
psychology, 53(2), 317-327.
Wang, M., Lin, Y. H., & Mikhelson, I. (2020). Regime-switching factor investing with
hidden Markov models. Journal of Risk and Financial Management, 13(12), 311.
Yue, S., Wang, X., & Wei, M. (2008). Application of two-order difference to gap
statistic. Transactions of Tianjin University, 14(3), 217-221.
zh_TW
dc.identifier.doi (DOI) 10.6814/NCCU202200938en_US