本研究在考慮交易成本的情況下，利用羅吉斯模型、類神經模型以及其兩者的混合模型建立一分類器，用以識別台灣選擇權與期貨市場中違反買權賣權平價等式的套利訊號。由逐筆成交資料的實證結果顯示，無論在金融海嘯（2007）、景氣復甦（2008）或是平穩時期（2012~2014）時，就識別率來說三種模型相差不大，但就獲利性而言混合模型有略優於其他兩者的表現。 / Considering the transaction cost, we establish a binary classifier system by logistic regression, artificial neural networks and hybird model with aboves. The system is used for distinguishing valid arbitrage opportunities which violated put call parity in Taiwan option and future market. By tickdata, we find that, although three models has same accuracy on classification almostly, hybird model is grater then the others in profitability no matter in depression(2007), boom(2008) or business steady state(2012~2014).
| Identifer | oai:union.ndltd.org:CHENGCHI/G0102352021 |
| Creators | 宋鴻緯, Sung, Hong Wei |
| Publisher | 國立政治大學 |
| Source Sets | National Chengchi University Libraries |
| Language | 中文 |
| Detected Language | English |
| Type | text |
| Rights | Copyright © nccu library on behalf of the copyright holders |
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