政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/141066
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文笔数/总笔数 : 113451/144438 (79%)
造访人次 : 51296886      在线人数 : 844
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻
    政大機構典藏 > 商學院 > 金融學系 > 學位論文 >  Item 140.119/141066


    请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/141066


    题名: 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
    贡献者: 江彌修
    Chiang, Mi-Hsiu
    盧建豪
    Lu, Chien-Hou
    关键词: 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
    上传时间: 2022-08-01 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
    数据类型: thesis
    DOI: 10.6814/NCCU202200938
    显示于类别:[金融學系] 學位論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    202701.pdf2777KbAdobe PDF20检视/开启


    在政大典藏中所有的数据项都受到原著作权保护.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈