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| Title: | CBOE SKEW指數資訊內涵研究-應用馬可夫狀態轉換模型建構交易策略
The Information Content of CBOE SKEW Index - Trading Strategy Under Markov Regime Switching Model |
| Authors: | 簡育昰
Jian, Yu Shi |
| Contributors: | 陳威光
林靖庭
Chen, Wei Kuang
Lin, Ching Ting
簡育昰
Jian, Yu Shi |
| Keywords: | SKEW指數
VIX指數
馬可夫狀態轉換模型
交易策略
SKEW Index
VIX Index
Markov Switching Model
Trading Strategy |
| Date: | 2016 |
| Issue Date: | 2016-09-01 23:47:21 (UTC+8) |
| Abstract: | 被市場稱作黑天鵝指數的CBOE SKEW指數在2015年10月12日來到了歷史新高148.92，這比2006年房地產泡沫破滅前及1998年長期資本管理公司倒閉時觸及的水準還要高，亦同時加劇了市場恐慌的心理。實際觀察股市後續發展，並未發生崩跌的現象，這引起我們的好奇心究竟SKEW指數該如何解讀。
CBOE於2011年推出SKEW指數，本文針對SKEW指數探究其資訊內含並建構交易策略。首先透過一系列的時間序列分析對SKEW指數有基本的認識。透過時間序列分析加以驗證SKEW指數與VIX指數是兩個捕捉不同資訊內涵的指數。VIX指數捕捉的是報酬的標準差，而標準差僅描述平均數附近的報酬分布。但S&P500指數報酬並非常態分配，SKEW指數能額外捕捉VIX指數捕捉不到的尾端風險。SKEW指數還能用來預測未來大盤走勢，在不同資料頻率比較下以預測大盤週報酬的效果最好。
本文進一步採用SKEW指數建構交易策略。以採用固定轉換機率馬可夫轉換模型下VIX指數所偵測的狀態轉換為比較基準，比較增加SKEW指數作為訊息因子後所採用的時序變動型馬可夫轉換模型是否能提升模型偵測狀態轉換的能力。樣本期間為2002年4月15日至2013年3月29日，透過模型偵測到狀態轉換的時點，於隔日以開盤價在市場上建立相應部位。當再次偵測到狀態轉換時，隔日以開盤價做反向部位，如此反覆操作。實證結果發現以VIX指數作為應變數並搭配SKEW指數作為訊息因子下的時序變動型馬可夫轉換模型偵測狀態轉換的能力最佳，其中多頭部位表現又都較空頭部位表現好。以SKEW指數作為訊息因子的TVTP模型在不考慮稅、手續費及股利下年化報酬有13.61%，考慮稅、手續費及股利後年化報酬仍有12.47%。
This paper divided into two parts to investigate on the information content of CBOE SKEW Index. For the first part, we do time series analysis to observe the relationship between SKEW Index and other variables. First, we found that SKEW index is totally different from VIX index. VIX index is a proxy for the standard deviation of the returns. The standard deviation describes the average spread of the distribution of returns around its mean. This is not a sufficient measure of risk because the distribution of S&P 500 log returns is not normal. SKEW Index captures the tail risk of the distribution. Next, SKEW Index is good at predict future S&P500 ETF returns especially weekly speaking. Also, we found that the correlation between SKEW index & S&P500 index is too unstable to interpret. We argue that it’s not easy to interpret SKEW Index directly but we can combine SKEW Index with VIX Index.
Regarding the above reason, in second part, we combined SKEW Index with VIX Index to construct trading strategy under Markov Switching Model. By comparing with FTP Model, which included VIX index only, we found that TVTP model, which encompassed VIX Index and SKEW Index together, significantly outperform others. When the model detected regime switching, we buy/short SPY ETF in the market separately. We did the simulation test from 2002.4.15 to 2013.3.29. Without considering tax, fee and dividend, we earned yearly average rate of return 13.61%. After considering tax, fee and dividend, we earned yearly average rate of return 9.51%. |
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ii. 國內文獻
黃裕烈(1996) 「Markov Switching Model：台灣實質GNP的應用」，國立台灣大學經濟研究所碩士論文。
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董慧萍(2000) 「股市價量互動非線性模型之研究－應用TVTP Markov-Switching模型」，國立政治大學國際經營與貿易研究所碩士論文。
戴天君(2013) 「以VIX指數偵測危機狀態之效果探討─TVTP方法之應用」，國立政治大學金融研究所碩士論文。
陳威光(2015) 「期貨與選擇權原理」，新陸文化。 |
| Description: | 碩士
國立政治大學
金融研究所
103352005 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G1033520052 |
| Data Type: | thesis |
| Appears in Collections: | [金融學系] 學位論文
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