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題名 退休與保險基金之策略性資產配置---預測學習效果下之動態避險(II)
其他題名 Strategic Asset Allocation for Pension and Insurance Fund--- Dynamic Hedging through Learning Predictability
作者 張士傑
貢獻者 國立政治大學風險管理與保險學系
行政院國家科學委員會
關鍵詞 退休;保險基金;資產配置
日期 2008
上傳時間 22-Oct-2012 15:44:35 (UTC+8)
摘要 本文於確定提撥退休金制度下,探討基金經理人如何決定最適資產策略規避薪資所得及通貨膨脹之不確定風險,同時考量績效制度(績效獎金與跌價懲罰機制)下,求得期末財富效用期望值極大化。本研究首先擴展Battocchio與Menoncin (2004)所建構之資產模型,其中不僅探討來自市場之風險,同時亦考量薪資所得、通貨膨脹與費用率之不確定性,研究其對最適資產配置行為的影響,建構隨機控制模型,以動態規劃方法求解Hamiltonian方程式,研究結果顯示,可利用共同基金分離定理來描述投資人之最適投資決策。依據數值結果顯示,本研究結果與Raghu et al. (2003)相符。
In this study, we investigate the portfolio selection problem incorporating incentive mechanism (i.e., bonus fees and downside penalty) into the defined contribution (DC) pension schemes. The framework in Battocchio and Menoncin (2004) is modified to incorporate the risks from the financial market and the background risks in describing the inflation rate and the labor income uncertainties through the stochastic processes. In order to scrutinize the general pattern of the fund dynamics under performance-oriented arrangement, a stochastic control problem is formulated. Then, the optimization scheme through dynamic programming is employed to solve the asset allocation problem. Finally, the numerical illustrations are shown and the results are summarized as following. When incentive mechanisms are incorporated, the settlement of delegated management contract is vital since the setup could affect the fund dynamics significantly. The result is consistent with the conclusion in Raghu et al. (2003). Based on our numerical results, it shows that the performance-oriented arrangements dominate the investment discretion in fund management. Hence an incentive program is required to be carefully implemented in order to balance the risk and reward in DC pension fund management.
關聯 應用研究
學術補助
研究期間:9708~ 9807
研究經費:980仟元
資料類型 report
dc.contributor 國立政治大學風險管理與保險學系en_US
dc.contributor 行政院國家科學委員會en_US
dc.creator (作者) 張士傑zh_TW
dc.date (日期) 2008en_US
dc.date.accessioned 22-Oct-2012 15:44:35 (UTC+8)-
dc.date.available 22-Oct-2012 15:44:35 (UTC+8)-
dc.date.issued (上傳時間) 22-Oct-2012 15:44:35 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/53891-
dc.description.abstract (摘要) 本文於確定提撥退休金制度下,探討基金經理人如何決定最適資產策略規避薪資所得及通貨膨脹之不確定風險,同時考量績效制度(績效獎金與跌價懲罰機制)下,求得期末財富效用期望值極大化。本研究首先擴展Battocchio與Menoncin (2004)所建構之資產模型,其中不僅探討來自市場之風險,同時亦考量薪資所得、通貨膨脹與費用率之不確定性,研究其對最適資產配置行為的影響,建構隨機控制模型,以動態規劃方法求解Hamiltonian方程式,研究結果顯示,可利用共同基金分離定理來描述投資人之最適投資決策。依據數值結果顯示,本研究結果與Raghu et al. (2003)相符。-
dc.description.abstract (摘要) In this study, we investigate the portfolio selection problem incorporating incentive mechanism (i.e., bonus fees and downside penalty) into the defined contribution (DC) pension schemes. The framework in Battocchio and Menoncin (2004) is modified to incorporate the risks from the financial market and the background risks in describing the inflation rate and the labor income uncertainties through the stochastic processes. In order to scrutinize the general pattern of the fund dynamics under performance-oriented arrangement, a stochastic control problem is formulated. Then, the optimization scheme through dynamic programming is employed to solve the asset allocation problem. Finally, the numerical illustrations are shown and the results are summarized as following. When incentive mechanisms are incorporated, the settlement of delegated management contract is vital since the setup could affect the fund dynamics significantly. The result is consistent with the conclusion in Raghu et al. (2003). Based on our numerical results, it shows that the performance-oriented arrangements dominate the investment discretion in fund management. Hence an incentive program is required to be carefully implemented in order to balance the risk and reward in DC pension fund management.-
dc.language.iso en_US-
dc.relation (關聯) 應用研究en_US
dc.relation (關聯) 學術補助en_US
dc.relation (關聯) 研究期間:9708~ 9807en_US
dc.relation (關聯) 研究經費:980仟元en_US
dc.subject (關鍵詞) 退休;保險基金;資產配置en_US
dc.title (題名) 退休與保險基金之策略性資產配置---預測學習效果下之動態避險(II)zh_TW
dc.title.alternative (其他題名) Strategic Asset Allocation for Pension and Insurance Fund--- Dynamic Hedging through Learning Predictabilityen_US
dc.type (資料類型) reporten