重設型認購權證之不同於標準型認購權證者，乃在重設期間內，若標的股價下跌到事先設定的水準，則履約價可向下重設，因此，具有下檔風險的保護作用。重設型權證具有一些特殊而有趣的特性，其評價方法也較標準型權證困難。常見的樹網膜型評價方法都存在著分配誤差與非線性誤差，本研究引用Figlewski and Gao (1999)的AMM即針對易產生這兩種誤差的部分作細網化的處理，不但可增加評價的正確性，並提高模型的運算效率。在權證的價格特性方面，本文發現在某特定的重設價格及某特定的重設基準日下，均分別可使重設型認購權證價格最大；在權證的避險特性方面，當標的股價接近重設履約價時，Delta值有跳躍的情況發生，Gamma值則在重設價兩端出現兩個角錐，而Vega值出現駝蜂狀，這些現象均使重設型認購權證的發行券商，在避險上遭到相當大的困難。

Reset warrants differ from standard warrants in that the strike price of a warrant can be reset to a lower level if the underlying stock price falls in a specified period (or date). As a result, reset warrants provide a downside protection. Some special and important properties can be found for the reset warrants, and the valuation of reset warrants is more difficult than that of standard warrants. For most tree models, the problem is that distribution error and nonlinearity error are often come out in the valuation of barrier-related options. We apply the adaptive mesh model (AMM) introduced by Figlewski and Gao (1999) to deal with the two errors. By constructing fine meshes in the area of easily producing the two errors, the AMM will greatly increase the precision and the efficiency of valuation. In respect of pricing properties, there exist a reset strike price and a reset data, respectively, to maximize the price of a reset warrant. In respect of hedging properties, there exist a delta jump, two gamma cones, and a vega hump when the underlying stock price approaches to the reset strike price. These properties will increase the difficulty in hedging warrants for the issuers.