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具稀疏風險因子之套利定價模型與傳統定價模型之比較 = Compare A...
~
呂思萱
具稀疏風險因子之套利定價模型與傳統定價模型之比較 = Compare Arbitrage Pricing Model with Sparse Risk Factors to Traditional Pricing Models
紀錄類型:
書目-語言資料,印刷品 : 單行本
並列題名:
Compare Arbitrage Pricing Model with Sparse Risk Factors to Traditional Pricing Models
作者:
呂思萱,
其他團體作者:
國立高雄大學
出版地:
高雄市
出版者:
國立高雄大學;
出版年:
2013[民102]
面頁冊數:
52葉部分彩圖,表格 : 30公分;
標題:
資本資產定價模型
標題:
CAPM
電子資源:
https://hdl.handle.net/11296/sg6czn
附註:
107年11月1日公開
附註:
參考書目:葉35-38
附註:
附錄:美股上市公司名稱;各表格結果
摘要註:
「承擔風險得到報酬」為投資學上的普世價值,因此,如何透過尋找有解釋能力的風險因子來預測報酬,一直是學界和業界長期研究的重要課題之一。傳統文獻中的資本資產定價模型(Capital Asset Pricing Model, CAPM)以及套利定價模型(Arbitrage Pricing Model, APM)等,均是探討風險與報酬關係的重要模型。Carvalho、Lopes和Aguilar (2010)除了傳統CAPM及APM模型外,亦於模型中考慮財務資料之前後期相關的特性,因此在模型中加入(係數的)動態模型。此外,由於不同的公司可能受到不同風險因子的影響,因此,在給定三個風險因子下,對某些公司而言,若某些風險因子對該公司不具解釋能力時,該公司對該風險因子之因素負荷(factor loading)亦應為零,因此,作者亦於模型中加入稀疏性(sparsity)之考量,並以各種方式證明其結果能增加風險因子對於股票報酬的解釋能力。本文參考Carvalho、Lopes和Aguilar (2010)的模型,並提出新的動態模型估計方法;另外對於如何設定稀疏風險因子,給予新的建議,並將我們的方法與文獻上的方法進行比較及討論。實證研究部分,資料選自Yahoo Finance西元1996年9月至西元2012年6月紐約證券交易所(NYSE)162家製造業上市公司的股價、淨值市價、總市價等公司資訊,進行單因子模型、Fama-French三因子模型、BARRA模型,以及各模型之動態修正模型及稀疏風險因子考量,進行實證分析與比較。 “Incurring the risk for the reward in return” is the universal value in investment. Therefore, one of the important issues for the academic and industrial researches for a long time, is to find risk factors that are able to explain and through which to predict the reward. Capital Asset Pricing Model (CAPM) and Arbitrage Pricing Model (APM) in traditional documents are the important models that investigate the relationship between risks and rewards. In addition to traditional CAPM and APM models, Carvalho、Lopes and Aguilar(2010) added (coefficient) dynamics into model due to the characteristic of the correlation between the former and later financial data. Besides, different companies are affected by different risk factors, so provided there are three risk factors, if certain risk factors are invalid to some companies, the factor loading for these companies to the risk factors in question should be zero. Taking that into consideration, the author also added sparsity in the model and by many methods proved that the result can increase the ability of explanation of risk factors to stock rewards. This article takes the models of Carvalho, Lopes and Aguilar (2010) as references, and proposes a new method of dynamic model estimation. In addition, it also gives new suggestions on how to set up the sparsity risk factors, and it discusses and compares the method with those in the past. In the part of empirical research, data came from the corporate information of NYSE's 162 publicly traded manufacturers on Yahoo Finance, from September 1996 to June 2012, including stock price, net market value, total market value and so on. CAPM、Fama-French 3 factor model、BARRA model are used for empirical analysis and comparison, along with the dynamic correction of each model and taking sparsity risky factor into account.
具稀疏風險因子之套利定價模型與傳統定價模型之比較 = Compare Arbitrage Pricing Model with Sparse Risk Factors to Traditional Pricing Models
呂, 思萱
具稀疏風險因子之套利定價模型與傳統定價模型之比較
= Compare Arbitrage Pricing Model with Sparse Risk Factors to Traditional Pricing Models / 呂思萱撰 - 高雄市 : 國立高雄大學, 2013[民102]. - 52葉 ; 部分彩圖,表格 ; 30公分.
107年11月1日公開參考書目:葉35-38附錄:美股上市公司名稱;各表格結果.
資本資產定價模型CAPM
具稀疏風險因子之套利定價模型與傳統定價模型之比較 = Compare Arbitrage Pricing Model with Sparse Risk Factors to Traditional Pricing Models
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「承擔風險得到報酬」為投資學上的普世價值,因此,如何透過尋找有解釋能力的風險因子來預測報酬,一直是學界和業界長期研究的重要課題之一。傳統文獻中的資本資產定價模型(Capital Asset Pricing Model, CAPM)以及套利定價模型(Arbitrage Pricing Model, APM)等,均是探討風險與報酬關係的重要模型。Carvalho、Lopes和Aguilar (2010)除了傳統CAPM及APM模型外,亦於模型中考慮財務資料之前後期相關的特性,因此在模型中加入(係數的)動態模型。此外,由於不同的公司可能受到不同風險因子的影響,因此,在給定三個風險因子下,對某些公司而言,若某些風險因子對該公司不具解釋能力時,該公司對該風險因子之因素負荷(factor loading)亦應為零,因此,作者亦於模型中加入稀疏性(sparsity)之考量,並以各種方式證明其結果能增加風險因子對於股票報酬的解釋能力。本文參考Carvalho、Lopes和Aguilar (2010)的模型,並提出新的動態模型估計方法;另外對於如何設定稀疏風險因子,給予新的建議,並將我們的方法與文獻上的方法進行比較及討論。實證研究部分,資料選自Yahoo Finance西元1996年9月至西元2012年6月紐約證券交易所(NYSE)162家製造業上市公司的股價、淨值市價、總市價等公司資訊,進行單因子模型、Fama-French三因子模型、BARRA模型,以及各模型之動態修正模型及稀疏風險因子考量,進行實證分析與比較。 “Incurring the risk for the reward in return” is the universal value in investment. Therefore, one of the important issues for the academic and industrial researches for a long time, is to find risk factors that are able to explain and through which to predict the reward. Capital Asset Pricing Model (CAPM) and Arbitrage Pricing Model (APM) in traditional documents are the important models that investigate the relationship between risks and rewards. In addition to traditional CAPM and APM models, Carvalho、Lopes and Aguilar(2010) added (coefficient) dynamics into model due to the characteristic of the correlation between the former and later financial data. Besides, different companies are affected by different risk factors, so provided there are three risk factors, if certain risk factors are invalid to some companies, the factor loading for these companies to the risk factors in question should be zero. Taking that into consideration, the author also added sparsity in the model and by many methods proved that the result can increase the ability of explanation of risk factors to stock rewards. This article takes the models of Carvalho, Lopes and Aguilar (2010) as references, and proposes a new method of dynamic model estimation. In addition, it also gives new suggestions on how to set up the sparsity risk factors, and it discusses and compares the method with those in the past. In the part of empirical research, data came from the corporate information of NYSE's 162 publicly traded manufacturers on Yahoo Finance, from September 1996 to June 2012, including stock price, net market value, total market value and so on. CAPM、Fama-French 3 factor model、BARRA model are used for empirical analysis and comparison, along with the dynamic correction of each model and taking sparsity risky factor into account.
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