應用自組織映射圖神經網路於選擇權隱含波動率之研究

Abstract

本研究旨在利用指數選擇權與其標的物之間存在的關係，對標的物之價格進行預測動作。故本研究擬利用隱含波動率所構成的圖形（由買權主要序列的隱含波動率或賣權主要序列的隱含波動率構成）來掌握標的物的價格漲跌。傳統上選擇權投資人預期標的物未來價格走勢上漲時，會買進買權，若此則會使買權的隱含波動率相對賣權的隱含波動率上升。若選擇權投資人預期標的物未來價格走勢下跌，則會買進賣權，使得賣權的隱含波動率相對買權的隱含波動率上升。

另外，本研究探討人工智慧領域中非監督式分群的自組織映射圖神經網路，由於自組織映射圖神經網路以自我群聚模式將大量未分類資料分群，使高維度複雜非線性資料轉化為低維度幾何關係，以萃取隱含於資料中的知識規則且能提供視覺化功能，相當適合做為金融資料分析預測之用途。

因此，本研究擬透過自組織映射圖神經網路對上述圖形進行分群，並賦予各群對應之買賣訊號，稍後再進行相關之模擬投資與操作實驗。實驗結果證實依本研究投資操作，其績效穩定地優於大盤買進持有策略及隨機買進策略。因此，選擇權主要序列隱含波動率之行為於研究期間確能對標的物之價格產生預測作用。

In this study we investigate the lead-lag relations between the index option market and the stock market at the aggregate level. We could forecast the fluctuation of Taiwan Stock Market Index if the relations did exist. We apply the diagram constructed by volatility, the combination of the implied volatility of call and put, to represent the option market’s view for future stock market movements and discover their relations. Investors would long call when they expect the future price of spot market to soar. Thus, the implied volatility of call would rising. If the implied volatility of call is higher than put, the investors will long call when they expect the future price of spot market to decline. Thus, the implied volatility of put would rising, and the implied volatility of put is higher than call. Therefore, we could use self-organizing map to classify diagram constructed by implied volatility, checking the trading signal for next day and producing real trading suggestion. Comparing our strategy with buy-and-hold strategy, our strategy is better .