模擬股票選擇權避險策略最佳化系統

System Simulation with Optimization For Hedging Stock Options Policies

徐崇禮(Chung-Li Hsu)

指導教授 ： 邱昭彰

元智大學/資訊學院/資訊管理學系/碩士(2002年)

摘要

台灣投資人較偏好在複雜多變的股票現貨市場環境中獲取高報酬，相對地投資人在做資金投資選擇時，由於高報酬隨之高風險，為了降低風險，可將資金一部份以反向操作股票選擇權買賣，以進行避險(hedging) ，就股票選擇權來說，它是一種衍生性金融商品。顧名思義，股票選擇權的價值是由股票的價值衍生出來。換言之，股票選擇權的價值與其相對應的特定股票(Underlying Security)價格的波動直接相關。因此，如何快速有效的訂定適當的股票選擇權避險策略，即需買進多少契約單位股票選擇權，乃是急待尋求解決的重要議題。本研究試圖採用系統模擬的蒙地卡羅模擬方式為求解基礎，引入統計分配概念作為模擬模型中不確定因素，並將其轉換為模式之參數，再配合基因演算法的最佳化以求得最適避險的投資，以使這個模式愈加符合真實世界的風貌。

關鍵字

選擇權 ； 避險 ； 不確定因素 ； 蒙地卡羅模擬 ； 基因演算法

並列摘要

Taiwanese usually select stocks market to make their higher returns. However Stocks investors without effective planning could avoid inside risks, and thereby influencing the investment decision. The challenges for effective planning is due to the fast changing market behavior, especially the hedging polices. In attempt to tackle such a issue, we adopt a novel approach combining simulation with optimization to improve hedging with options. The proposed approach is able to construct a suitable model to determine optimal hedging polices by referencing real data of Taiwan corporations, the options striking prices, along with related uncertainty risks of stocks investing process being taken into account. The natural question is, of course, how many options do we need to buy to adequately hedge our risk? Our goal will be to choose a hedging policies that minimizes the standard deviation of our total costs. We are employing Monte Carlo simulation and GA-based optimization technique in determining the best fitness of stock hedging costs, and hence increasing more revenue of Taiwan investors.

並列關鍵字

Options ； Hedging ； Uncertainty ； Monte Carlo Simulation ； GA-Based Optimization

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