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  本文關(guān)鍵詞：基于混頻數(shù)據(jù)支持向量回歸的股指期貨套利策略　出處：《浙江工商大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 股指期貨套利 支持向量回歸 混頻數(shù)據(jù) 交易策略

【摘要】：金融二級(jí)市場上股指期貨不僅是單邊投機(jī)的重要標(biāo)的,更是實(shí)現(xiàn)套期保值和雙邊套利的重要手段。股指期貨可以對(duì)A股市場起到價(jià)格發(fā)現(xiàn)以及平穩(wěn)趨勢的作用。但是在近期過往時(shí)間段內(nèi)(尤其2015年中),中國股票二級(jí)市場經(jīng)歷了巨大的波動(dòng),這波動(dòng)不僅表現(xiàn)在股票自身價(jià)格的大幅起落,也表現(xiàn)在股票現(xiàn)貨與期貨之間的價(jià)差的激烈起伏。按照套利理論,由于股票現(xiàn)貨和期貨之間由于強(qiáng)相關(guān)性和價(jià)格收斂性質(zhì)的存在,二者之間價(jià)差應(yīng)當(dāng)穩(wěn)定在一定區(qū)間范圍內(nèi)。市場上期現(xiàn)基差的劇烈波動(dòng),對(duì)套利交易者來說,既意味著隱藏風(fēng)險(xiǎn)也蘊(yùn)含著獲利機(jī)會(huì)。對(duì)基差范圍的準(zhǔn)確預(yù)測是套利交易有效進(jìn)行的重要保證。近年由于大數(shù)據(jù)概念、統(tǒng)計(jì)學(xué)習(xí)及深度學(xué)習(xí)等方法的蓬勃發(fā)展,越來越多的預(yù)測模型被運(yùn)用到量化投資領(lǐng)域當(dāng)中。支持向量回歸這一機(jī)器學(xué)習(xí)方法由于基于結(jié)構(gòu)風(fēng)險(xiǎn)最小化的理念和核函數(shù)的高維映射能力,使其成為外推能力優(yōu)異的預(yù)測方法。本文選取不同頻率的行情數(shù)據(jù)作為輸入特征,通過支持向量回歸方法,既預(yù)目標(biāo)測交易日中的基差值又預(yù)測基差波動(dòng)幅度。通過疊加基差值和基差波動(dòng)幅度,就可以得到基差的波動(dòng)范圍,即無套利區(qū)間。之后本文在此無套利區(qū)間上形成套利交易策略。利用1分鐘實(shí)時(shí)行情數(shù)據(jù)的突破和回復(fù),作為交易系統(tǒng)的開平倉條件。最后通過實(shí)證數(shù)據(jù)來驗(yàn)證交易策略,檢測其總體收益情況以及在不同行情階段的表現(xiàn)。
[Abstract]:Stock index futures in the secondary financial market are not only the important targets of unilateral speculation. Stock index futures can play a role in price discovery and stable trend of A share market. But in the recent past time period (especially middle of 2015). China's secondary stock market has experienced tremendous fluctuations, which is not only reflected in the volatility of the stock price itself, but also in the sharp fluctuation of the price difference between stock spot and futures. According to arbitrage theory. Due to the existence of strong correlation and price convergence between stock spot and futures, the price difference between them should be stabilized in a certain range. The sharp fluctuation of the current basis in the market is for arbitrage traders. Accurate prediction of the basis range is an important guarantee for the effective carrying out of arbitrage trading. In recent years, due to the rapid development of big data concept, statistical learning and in-depth learning, etc. More and more prediction models are applied to the field of quantitative investment. Support vector regression (SVM) is a machine learning method based on the concept of structural risk minimization and the ability of high-dimensional mapping of kernel functions. This paper selects the different frequency quotation data as the input feature, and adopts the support vector regression method. The base difference and the base fluctuation range can be obtained by adding the base value and the base fluctuation range. That is, no arbitrage interval. Then this paper forms a arbitrage trading strategy in this no-arbitrage range. Using the one-minute real-time market data breakthrough and recovery. Finally, through empirical data to verify the trading strategy, to test its overall earnings and performance in different stages of the market.
【學(xué)位授予單位】：浙江工商大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F832.51

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本文編號(hào)：1368114