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研究生: 吳岱恩
Wu, Tai En
論文名稱: 考量信用風險下之海外可轉債評價
Pricing Euro-Convertible Bonds with Credit Risk
指導教授: 廖四郎
Liao, Szu Lang
學位類別: 碩士
Master
系所名稱: 商學院 - 金融學系
Department of Money and Banking
論文出版年: 2016
畢業學年度: 104
語文別: 英文
論文頁數: 63
中文關鍵詞: 海外可轉債跳躍過程信用風險二項樹最小平方蒙地卡羅法CIR利率模型
外文關鍵詞: Euro-convertible bond, jump-diffusion process, credit risk, binomial tree, least squares Monte Carlo simulation, CIR interest model
相關次數: 點閱:29下載:5
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  •   鑒於近年全球海外可轉換公司債發行檔數大增,然而以此商品為研究主題的文獻並不多,於是決定以此為研究目標。
      影響海外可轉換公司債的價格因素包括股票價格、匯率、國內利率、國外利率和發行公司的違約機率,因此可買回、可賣回海外可轉換公司債是一個複雜的商品,而評價也較為困難。本文採用三維度二項樹和最小平方蒙地卡羅法建立評價海外可轉債的數值模型。為了更貼近真實世界,本文考量各變數間相關性和動態信用風險;另外,為了使評價更為精準,於股價過程中加入跳躍過程。
      本文將模型運用至兩檔台灣公司所發行的海外可轉債,發現理論價格傾向於高估,但是理論價格與市價極為接近,尤其當以最小平方蒙地卡羅法評價時。另外本文也針對發行條件和模型中各個變數作敏感度分析,其中重要的是發現股票波動度、股票與匯率間相關係數在海外可轉債評價中扮演重要的角色。


    The number of Euro-convertible bonds issued has highly increased in the early 2010s. However, the related literature is barely found. This paper studies the pricing models of this investment product. Euro-convertible bonds are complex instruments affected by the credit risk of the issuers, the dynamic process of stock prices, the term structure of the interest rate and the movement of the exchange rate in the same time. Accordingly, building the ECB pricing model is a hard work.
    This paper presents a model considering the dynamic credit risk and jump in stock price process to make valuation more precise. Another advantage of models in this paper is use of stochastic interest rates for both local and foreign so as to make the model more staying with the real world. The other advantage is taking the correlation between each random variables into account. For pricing the Euro-convertible bonds, the numerical methodologies used in this paper are three-dimension binomial tree and least squares Monte Carlo approach.
    For purpose of assessing the performance of the model, two Euro-convertible bonds issued by Taiwan companies are chosen as samples and the difference between the theoretical price and market price during its issue period are provided. The results demonstrate that in spite of pretty slight overestimation, the least squares Monte Carlo simulation does a better job.
    In addition, this paper performs several kinds of sensitivity analysis to have in-depth understanding about the models. The consequence shows that the volatility of a stock return and the correlation between stock and exchange rate play a central role in ECB valuations.

    1 Introduction 1
    2 Literature 4
    2-1 Convertible Bond 4
    2-2 Euro-Convertible Bond 6
    3 Model 7
    3-1 Dynamic Process 7
    3-2 Three-Dimension Binomial Tree 10
    3-2-1 Main Idea 10
    3-2-2 Pricing Framework 11
    3-2-3 Implementation Process 15
    3-3 Least Squares Monte Carlo 17
    3-3-1 Main Idea 17
    3-3-2 Pricing Framework 17
    4 Euro-Convertible Bond 21
    4-1 Settlement Equivalent 21
    4-2 Conversion Provision 21
    4-3 Early Redemption Amount 22
    4-4 Call Provision 22
    4-5 Put Provision 22
    5 Numerical Implement 23
    5-1 Data 23
    5-2 Parameter Estimation 23
    5-3 Contracts 24
    5-4 Pricing Results 26
    6 Sensitivity Analysis 30
    6-1 Sensitivity for Call and Put Provisions 31
    6-2 Sensitivity for Fixed Exchange Rate 33
    6-3 Sensitivity for Conversion Price and Stock Volatility 35
    6-4 Sensitivity for Jump Process 36
    6-5 Sensitivity for Dynamics of Credit Risk 38
    6-6 Sensitivity for Correlation between Stock and FX (I) 40
    6-7 Sensitivity for Correlation between Stock and FX (II) 41
    7 Conclusion 44
    8 References 45
    9 Appendix 47
    9-1 Derivation for Tree Model 47
    9-2 Table about ECBs market 49
    9-3 Table about Numerical Implement Parameters 51
    9-4 Table about Sensitivity Analysis Results 52
    9-4-1 Sensitivity for Terms of Issue: Call Price and Put Price 52
    9-4-2 Sensitivity for Terms of Issue: fixed FX 55
    9-4-3 Sensitivity for Parameters: Conversion Price and Stock Volatility 56
    9-4-4 Sensitivity for Parameters: Jump Process 58
    9-4-5 Sensitivity for Parameters: Dynamics of Credit Risk 59
    9-4-6 Sensitivity for Parameters: Correlation between Stock and FX (I) 62
    9-4-7 Sensitivity for Parameters: Correlation between Stock and FX (II) 63

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