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  本文關(guān)鍵詞：基于ARFIMA模型的波動(dòng)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(dòng)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(duì)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(jià)公式中，我們一般假設(shè)波動(dòng)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(yùn)用ARCH Model、GARCH Model、SV Model等對(duì)股票市場波動(dòng)率進(jìn)行刻畫，，然而，通過研究我們發(fā)現(xiàn)，對(duì)于新興市場國家，股票波動(dòng)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(diǎn)�；谏鲜鲈�，我們引入時(shí)間序列理論體系中一個(gè)新領(lǐng)域ARFIMA模型，以此刻畫波動(dòng)率序列的長記憶性，并對(duì)其進(jìn)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(dòng)率的長記憶性，并運(yùn)用ARFIMA模型對(duì)長記憶性序列進(jìn)行建模及預(yù)測，同時(shí)基于所預(yù)測波動(dòng)率序列對(duì)期權(quán)交易策略進(jìn)行研究。具體可以分為以下幾個(gè)方面： 首先，研究了股票市場波動(dòng)率的度量方法。給出了“已實(shí)現(xiàn)”波動(dòng)率可以作為波動(dòng)率的無偏有效估計(jì)的結(jié)論，并通過二次移動(dòng)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(shí)最佳。 其次，對(duì)時(shí)間序列ARFIMA模型進(jìn)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(jì)算出d值，確定模型的參數(shù)，并運(yùn)用三種方法對(duì)未來波動(dòng)率進(jìn)行預(yù)測。 最后，研究了基于波動(dòng)率的期權(quán)交易策略。以江銅CWB1為例，提出了波動(dòng)率異常的識(shí)別方法及綜合運(yùn)用，并分別詳細(xì)介紹了買入波動(dòng)率策略和賣出波動(dòng)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(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F832.51;F224

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本文編號(hào)：1382258