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題名 多資產投組估計： 動態Factor Copula模型
Estimation of Multi-Asset Portfolio：Dynamic Factor Copula Model作者 湯詠皓
Tang, Yung-Hao貢獻者 楊曉文
湯詠皓
Tang, Yung-Hao關鍵詞 動態 Factor Copula模型
關聯結構
蒙地卡羅情境模擬
附保證投資型商品
時間序列模型
Dynamic Factor Copula
Copula
Monte Carlo Simulation
GMXB
Time Series Model日期 2021 上傳時間 1-Jul-2021 17:57:25 (UTC+8) 摘要 全球市場的報酬走勢根據過往的文獻並不符合常態分佈，極端行情出現的可能性高於預期而且頻繁，其分配具有厚尾且高峰的現象，並且因為隨著全球化，世界發生的大事在短時間內，市場間互相影響，因此資產間的關聯結構越來越被重視。過往像是Markowiz (1952) 提出Mean–Variance投資組合理論，其背後之假設便是資產需符合常態，因此如何解決非常態相關性問題是許多學者在意的問題。Copula在Sklar (1959) 提出後，聯合分配函數可以透過邊際分配和Copula函數所組成，也有效解決常態性假設的必須性，不過在過往的Copula文獻中，多數都是使用兩資產的建構，直到Oh and Patton (2018)提出之動態Factor Copula模型，成功將高維度變數轉成單因子估計，並使用GAS架構對時間序列進行估計，本文將使用不同的動態Factor Copula對不同的投資組合進行配適，接下來與不同的模型進行比較，並用模擬出來的情境帶入附保證投資型商品中，觀察對於保險公司提列準備金的影響。
According to the existing literature, the trend of the global market return didn’t follow the normal distribution. The distribution of stock returns may appear a thick tail and high peakness. Besides, because of the globalization, the correlation struc-ture between assets is getting more and more attention. In the past paper, for exam-ple, Markowiz (1952) proposed the Mean-Variance portfolio theory. The assumption behind it was that assets must follow normal distribution. Therefore, how to solve the problem of abnormal correlation is a problem that many scholars attempt to work with. After Copula proposed by Sklar (1959), the joint distribution function can be composed of marginal distribution and the Copula function, which solves the ne-cessity of the normality assumption. However, in the existing literature regarding Copula , most of them only constructed by two assets. Until the Dynamic Factor Copula proposed by Oh and Patton (2018) successfully converted high-dimensional variables into single factor estimation, and used the GAS framework to estimate the time series. This article will use different Dynamic Factor Copula to adapt to differ-ent portfolios. Compare with different models, we use the simulated scenarios with the application on GMXB proucts to observe the impact on insurance companies` reserve requirements.參考文獻 [1]Ang, A., & Chen, J. (2002). Asymmetric correlations of equity portfoli-os. Journal of financial Economics, 63(3), 443-494.[2]Andersson, M., Krylova, E., & Vähämaa, S. (2008). Why does the correlation between stock and bond returns vary over time?. Applied Financial Econom-ics, 18(2), 139-151.[3]Alonso-García, J., Wood, O., & Ziveyi, J. (2018). Pricing and hedging guaran-teed minimum withdrawal benefits under a general Lévy framework using the COS method. Quantitative Finance, 18(6), 1049-1075.[4]Brennan, M. J., & Schwartz, E. S. (1976). The pricing of equity-linked life in-surance policies with an asset value guarantee. Journal of Financial Economics, 3(3), 195-213.[5]Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedastici-ty. Journal of econometrics, 31(3), 307-327.[6]Connolly, R., Stivers, C., & Sun, L. (2005). Stock market uncertainty and the stock-bond return relation. Journal of Financial and Quantitative Analysis, 161-194.[7]Creal, D., Koopman, S. J., & Lucas, A. (2013). Generalized autoregressive score models with applications. Journal of Applied Econometrics, 28(5), 777-795.[8]Dai, M., Kuen Kwok, Y., & Zong, J. (2008). Guaranteed minimum withdrawal benefit in variable annuities. Mathematical Finance: An International Journal of Mathematics, Statistics and Financial Economics, 18(4), 595-611.[9]Erb, C. B., Harvey, C. R., & Viskanta, T. E. (1994). Forecasting international equity correlations. Financial analysts journal, 50(6), 32-45.[10]Engle, R. (2002). Dynamic conditional correlation: A simple class of multivari-ate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20(3), 339-350.[11]Jondeau, E., & Rockinger, M. (2006). The copula-garch model of conditional dependencies: An international stock market application. Journal of interna-tional money and finance, 25(5), 827-853..[12]Longin, F., & Solnik, B. (2001). Extreme correlation of international equity markets. The journal of finance, 56(2), 649-676.[13]Meneguzzo, D., & Vecchiato, W. (2004). Copula sensitivity in collateralized debt obligations and basket default swaps. Journal of Futures Markets: Futures, Options, and Other Derivative Products, 24(1), 37-70.[14]Oh, D. H., & Patton, A. J. (2017). Modeling dependence in high dimensions with factor copulas. Journal of Business & Economic Statistics, 35(1), 139-154.[15]Oh, D. H., & Patton, A. J. (2018). Time-varying systemic risk: Evidence from a dynamic copula model of cds spreads. Journal of Business & Economic Statis-tics, 36(2), 181-195.[16]Patton, A. J. (2004). On the out-of-sample importance of skewness and asym-metric dependence for asset allocation. Journal of Financial Econometrics, 2(1), 130-168.[17]Patton, A. J. (2006). Modelling asymmetric exchange rate dependence. Inter-national economic review, 47(2), 527-556.[18]Riccetti, L. (2010). The use of copulas in asset allocation: when and how a cop-ula model can be useful. LAP LAMBERT Academic Publishing.[19]Schönbucher, P. J., & Schubert, D. (2001). Copula-dependent default risk in in-tensity models. In Working paper, Department of Statistics, Bonn University. 描述 碩士
國立政治大學
金融學系
108352010資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108352010 資料類型 thesis dc.contributor.advisor 楊曉文 zh_TW dc.contributor.author (Authors) 湯詠皓 zh_TW dc.contributor.author (Authors) Tang, Yung-Hao en_US dc.creator (作者) 湯詠皓 zh_TW dc.creator (作者) Tang, Yung-Hao en_US dc.date (日期) 2021 en_US dc.date.accessioned 1-Jul-2021 17:57:25 (UTC+8) - dc.date.available 1-Jul-2021 17:57:25 (UTC+8) - dc.date.issued (上傳時間) 1-Jul-2021 17:57:25 (UTC+8) - dc.identifier (Other Identifiers) G0108352010 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/135939 - 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) 108352010 zh_TW dc.description.abstract (摘要) 全球市場的報酬走勢根據過往的文獻並不符合常態分佈，極端行情出現的可能性高於預期而且頻繁，其分配具有厚尾且高峰的現象，並且因為隨著全球化，世界發生的大事在短時間內，市場間互相影響，因此資產間的關聯結構越來越被重視。過往像是Markowiz (1952) 提出Mean–Variance投資組合理論，其背後之假設便是資產需符合常態，因此如何解決非常態相關性問題是許多學者在意的問題。Copula在Sklar (1959) 提出後，聯合分配函數可以透過邊際分配和Copula函數所組成，也有效解決常態性假設的必須性，不過在過往的Copula文獻中，多數都是使用兩資產的建構，直到Oh and Patton (2018)提出之動態Factor Copula模型，成功將高維度變數轉成單因子估計，並使用GAS架構對時間序列進行估計，本文將使用不同的動態Factor Copula對不同的投資組合進行配適，接下來與不同的模型進行比較，並用模擬出來的情境帶入附保證投資型商品中，觀察對於保險公司提列準備金的影響。 zh_TW dc.description.abstract (摘要) According to the existing literature, the trend of the global market return didn’t follow the normal distribution. The distribution of stock returns may appear a thick tail and high peakness. Besides, because of the globalization, the correlation struc-ture between assets is getting more and more attention. In the past paper, for exam-ple, Markowiz (1952) proposed the Mean-Variance portfolio theory. The assumption behind it was that assets must follow normal distribution. Therefore, how to solve the problem of abnormal correlation is a problem that many scholars attempt to work with. After Copula proposed by Sklar (1959), the joint distribution function can be composed of marginal distribution and the Copula function, which solves the ne-cessity of the normality assumption. However, in the existing literature regarding Copula , most of them only constructed by two assets. Until the Dynamic Factor Copula proposed by Oh and Patton (2018) successfully converted high-dimensional variables into single factor estimation, and used the GAS framework to estimate the time series. This article will use different Dynamic Factor Copula to adapt to differ-ent portfolios. Compare with different models, we use the simulated scenarios with the application on GMXB proucts to observe the impact on insurance companies` reserve requirements. en_US dc.description.tableofcontents 第一章 緒論 1第一節 研究背景與動機 1第二節 研究目的 2第三節 研究流程 3第二章 文獻回顧 4第一節 金融資產 4第二節 關聯結構 5第三節 多變數和時序下的關聯結構 6第四節 附保證投資型商品 7第三章 研究方法 9第一節 關聯結構 9第二節 動態Factor Copula 模型 10第三節 GAS架構 11第四節 最大概似估計法(MLE) 12第五節 蒙地卡羅模擬 14第六節 附保證商品假設及準備金計算 15第七節 實驗設計流程 16第四章 實證研究 18第一節 資料期間 18第二節 邊際GARCH模型 20第三節 動態Factor Copula模型 24第四節 模型估計結果 – 股票組合 26第五節 模型估計結果 – 商品組合 29第六節 模型比較 32第七節 附保證型投資型商品模擬 33第五章 結論與展望 35第一節 結論與探討 35第二節 未來研究方向建議 36參考文獻 38附錄 補充圖表 40 zh_TW dc.format.extent 2039014 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108352010 en_US dc.subject (關鍵詞) 動態 Factor Copula模型 zh_TW dc.subject (關鍵詞) 關聯結構 zh_TW dc.subject (關鍵詞) 蒙地卡羅情境模擬 zh_TW dc.subject (關鍵詞) 附保證投資型商品 zh_TW dc.subject (關鍵詞) 時間序列模型 zh_TW dc.subject (關鍵詞) Dynamic Factor Copula en_US dc.subject (關鍵詞) Copula en_US dc.subject (關鍵詞) Monte Carlo Simulation en_US dc.subject (關鍵詞) GMXB en_US dc.subject (關鍵詞) Time Series Model en_US dc.title (題名) 多資產投組估計： 動態Factor Copula模型 zh_TW dc.title (題名) Estimation of Multi-Asset Portfolio：Dynamic Factor Copula Model en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1]Ang, A., & Chen, J. (2002). Asymmetric correlations of equity portfoli-os. Journal of financial Economics, 63(3), 443-494.[2]Andersson, M., Krylova, E., & Vähämaa, S. (2008). Why does the correlation between stock and bond returns vary over time?. Applied Financial Econom-ics, 18(2), 139-151.[3]Alonso-García, J., Wood, O., & Ziveyi, J. (2018). Pricing and hedging guaran-teed minimum withdrawal benefits under a general Lévy framework using the COS method. Quantitative Finance, 18(6), 1049-1075.[4]Brennan, M. J., & Schwartz, E. S. (1976). The pricing of equity-linked life in-surance policies with an asset value guarantee. Journal of Financial Economics, 3(3), 195-213.[5]Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedastici-ty. Journal of econometrics, 31(3), 307-327.[6]Connolly, R., Stivers, C., & Sun, L. (2005). Stock market uncertainty and the stock-bond return relation. Journal of Financial and Quantitative Analysis, 161-194.[7]Creal, D., Koopman, S. J., & Lucas, A. (2013). Generalized autoregressive score models with applications. Journal of Applied Econometrics, 28(5), 777-795.[8]Dai, M., Kuen Kwok, Y., & Zong, J. (2008). Guaranteed minimum withdrawal benefit in variable annuities. Mathematical Finance: An International Journal of Mathematics, Statistics and Financial Economics, 18(4), 595-611.[9]Erb, C. B., Harvey, C. R., & Viskanta, T. E. (1994). Forecasting international equity correlations. Financial analysts journal, 50(6), 32-45.[10]Engle, R. (2002). Dynamic conditional correlation: A simple class of multivari-ate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20(3), 339-350.[11]Jondeau, E., & Rockinger, M. (2006). The copula-garch model of conditional dependencies: An international stock market application. Journal of interna-tional money and finance, 25(5), 827-853..[12]Longin, F., & Solnik, B. (2001). Extreme correlation of international equity markets. The journal of finance, 56(2), 649-676.[13]Meneguzzo, D., & Vecchiato, W. (2004). Copula sensitivity in collateralized debt obligations and basket default swaps. Journal of Futures Markets: Futures, Options, and Other Derivative Products, 24(1), 37-70.[14]Oh, D. H., & Patton, A. J. (2017). Modeling dependence in high dimensions with factor copulas. Journal of Business & Economic Statistics, 35(1), 139-154.[15]Oh, D. H., & Patton, A. J. (2018). Time-varying systemic risk: Evidence from a dynamic copula model of cds spreads. Journal of Business & Economic Statis-tics, 36(2), 181-195.[16]Patton, A. J. (2004). On the out-of-sample importance of skewness and asym-metric dependence for asset allocation. Journal of Financial Econometrics, 2(1), 130-168.[17]Patton, A. J. (2006). Modelling asymmetric exchange rate dependence. Inter-national economic review, 47(2), 527-556.[18]Riccetti, L. (2010). The use of copulas in asset allocation: when and how a cop-ula model can be useful. LAP LAMBERT Academic Publishing.[19]Schönbucher, P. J., & Schubert, D. (2001). Copula-dependent default risk in in-tensity models. In Working paper, Department of Statistics, Bonn University. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202100574 en_US
