• Korean Management Science Review
• /
• v.31 no.1
• /
• pp.49-61
• /
• 2014

We investigate the hedging effectiveness of incorporating single-stock futures into the corresponding stocks. Investing in only stocks frequently causes too much risk when market volatility suddenly rises. We found that single-stock futures help reduce the variance and risk levels of the corresponding stocks invested. We use daily prices of Korean stocks and their corresponding futures for the time period from December 2009 to August 2013 to test the hedging effect. We also use system trading technique that uses automatic trading program which also has several simulation functions. Moving average strategy, Stochastic's strategy, Larry William's %R strategy have been considered for hedging strategy of the futures. Hedging effectiveness of each strategy was analyzed by percent reduction in the variance between the hedged and the unhedged variance. The results clearly showed that examined hedging strategies reduce price volatility risk compared to unhedged portfolio.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.13 no.3
• /
• pp.1091-1098
• /
• 2012

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%.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.2
• /
• pp.712-717
• /
• 2011

Option issuers generally utilize Dynamic Delta Hedging(DDH) technique to avoid the risk resulting from continuously changing option value. DDH duplicates payoff of option position by adjusting hedge position according to the delta value from Black-Scholes(BS) model in order to maintain risk neutral state. DDH, however, is not able to guarantee optimal hedging performance because of the weaknesses caused by impractical assumptions inherent in BS model. Therefore, this study presents a methodology for dynamic option hedge using artificial neural network(ANN) to enhance hedging performance and show the superiority of the proposed method using various computational experiments.

• International Journal of Aeronautical and Space Sciences
• /
• v.13 no.1
• /
• pp.74-89
• /
• 2012

An adaptive backstepping controller is designed for the automatic landing of a fixed-wing aircraft. The backstepping control scheme is adopted by using the nonlinear six degree-of-freedom dynamics of the aircraft during the landing phase. The adaptive law is integrated along with the backstepping controller in order to estimate the aircraft modeling errors as well as the external disturbance. The dynamic constraints of the states and the actuator inputs are taken into account in the parameter adaptation. This is done to prevent an aggressive adaptation and to provide reliable control commands. Numerical simulations were performed to verify the performance of the proposed control law for the landing of the aircraft with the presence of gust and actuator stuck.