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http://dx.doi.org/10.5762/KAIS.2012.13.3.1091

A Methodology for Hedging Equity Linked Warrant Using Artificial Neural Network  

Ryu, Jae-Pil (Dept. of Management Engineering, Sangmyung University)
Shin, Hyun-Joon (Dept. of Management Engineering, Sangmyung University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.13, no.3, 2012 , pp. 1091-1098 More about this Journal
Abstract
From the perspective of risk management, financial organization that have issued ELW require an efficient hedging methodology due to recently increased trade volume of ELW. This study presents an ELW hedging methodology using artificial neural network(ANN) to minimize hedging costs. The performance of the presented methodology in this study is examined by analysis utilizing the prices and volatilities of underlying assets, risk free interest rates, and maturities and computational experiments show that the proposed method is superior to existing dynamic delta hedging(DDH) technique in terms of hedging costs ranged from 25% to 250%.
Keywords
Equity Linked Warrant; Hedging Methodology; Costs; Black-Scholes; ANN;
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