本文關(guān)鍵詞：基于ARFIMA模型的波動率預(yù)測及交易策略研究　出處：《蘇州大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 波動率 長記憶性 ARFIMA 交易策略

【摘要】：隨著我國金融創(chuàng)新的不斷深入，市場上對于股票期權(quán)產(chǎn)品的需求呼聲越來越高。傳統(tǒng)的Black-Scholes定價公式中，我們一般假設(shè)波動率是已知并且固定不變的，但是這并不符合市場的客觀情況。因此，前人曾運用ARCH Model、GARCH Model、SV Model等對股票市場波動率進行刻畫，，然而，通過研究我們發(fā)現(xiàn)，對于新興市場國家，股票波動率是具有長記憶性的，而上述模型并不能很好刻畫這一特點。基于上述原因，我們引入時間序列理論體系中一個新領(lǐng)域ARFIMA模型，以此刻畫波動率序列的長記憶性，并對其進行預(yù)測和交易策略研究。 本文主要研究了基于我國股票市場的波動率的長記憶性，并運用ARFIMA模型對長記憶性序列進行建模及預(yù)測，同時基于所預(yù)測波動率序列對期權(quán)交易策略進行研究。具體可以分為以下幾個方面： 首先，研究了股票市場波動率的度量方法。給出了“已實現(xiàn)”波動率可以作為波動率的無偏有效估計的結(jié)論，并通過二次移動平均消除了“噪聲”，得出1天高頻數(shù)據(jù)取樣頻率為5分鐘時最佳。 其次，對時間序列ARFIMA模型進行了探討。比較了LW方法較R/S方法、修正R/S方法、GPH方法的優(yōu)勢，借助于Matlab軟件計算出d值，確定模型的參數(shù)，并運用三種方法對未來波動率進行預(yù)測。 最后，研究了基于波動率的期權(quán)交易策略。以江銅CWB1為例，提出了波動率異常的識別方法及綜合運用，并分別詳細介紹了買入波動率策略和賣出波動率策略。
[Abstract]:With the deepening of financial innovation in China, the demand for stock option products in the market is increasing. Traditional Black-Scholes pricing formula. We generally assume that volatility is known and fixed, but this does not conform to the objective situation of the market. Therefore, the previous use of the ARCH Model GARCH Model. SV Model and others depict the volatility of stock market. However, we find that the volatility of stock market has a long memory for emerging market countries. Because of the above reasons, we introduce a new domain ARFIMA model in the system of time series theory to describe the long memory of volatility series. And carries on the forecast and the trading strategy research to it. This paper mainly studies the long memory of volatility based on the stock market in China, and uses ARFIMA model to model and predict the long memory sequence. At the same time, based on the predicted volatility series to study the options trading strategy. The specific can be divided into the following aspects: Firstly, the paper studies the measurement method of volatility in stock market, and gives the conclusion that "realized" volatility can be used as an unbiased efficient estimate of volatility, and eliminates the "noise" by the quadratic moving average. The best sampling frequency of 1 day high frequency data is 5 minutes. Secondly, the time series ARFIMA model is discussed, and the advantage of LW method is compared with that of the R / S method and the modified R / S method. With the help of Matlab software, the d value is calculated, the parameters of the model are determined, and three methods are used to predict the volatility in the future. Finally, the option trading strategy based on volatility is studied. Taking Jiang Copper CWB1 as an example, the identification method of volatility anomaly and its comprehensive application are put forward. And the buy volatility strategy and the sell volatility strategy are introduced in detail.
【學(xué)位授予單位】：蘇州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：F832.51;F224

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相關(guān)期刊論文 前2條

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本文編號：1382258