NDLI: Innovating Risk Management and Hedging Strategy for Convertible Bonds Using Support Vector Machine

Content Provider	IEEE Xplore Digital Library
Author	Chuanhe Shen Xiangrong Wang Linna Guo
Copyright Year	2010
Abstract	Convertible bonds (CB) contain many kinds of embedded options and the complexity of their interaction makes hedging exposures of CBs challengeable. In order to tackle the issue, this paper introduced support vector machine (SVM) approach to overcome the shortcomings of traditional pricing methods and enhance hedging efficiency. By feature selection, kernel function determination and parameter optimization, SVM-based model proved to be more effective in that it can deal with complicated interaction among options of CBs and nonlinear and time-varying correlation among variant variables. On the basis of the proposed model, an innovated hedging strategy for CBs, based on delta-gamma-neutral hedging methodology, was explored in terms of the sensitivity of CB's value to the underlying stock price. Moreover, the model brought out great flexibility for risk management and hedge ratio determination by coping with neatly stochastic changes in volatility of the underlying stock, with remarkable advantages in hedging performance in empirical analysis over the traditional methods.
Starting Page	341
Ending Page	345
File Size	273511
Page Count	5
File Format	PDF
ISBN	9781424475759
e-ISBN	9781424475766
DOI	10.1109/BIFE.2010.86
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Correlation Economic indicators delta-gamma-neutral hedging convertible bonds (CB) least square support vector machines (LS-SVM) embedded options Pricing statistical learning theory Kernel Portfolios
Content Type	Text
Resource Type	Article

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