【摘要】：文章第二部分及第四部分分別運(yùn)用時(shí)間序列方法,選取高頻數(shù)據(jù)擬合了滬深300股指期貨的差價(jià)套利模型及香港恒生指數(shù)、滬深300股指期貨的比價(jià)套利模型,并運(yùn)用價(jià)格條件VaR方法對(duì)其套利風(fēng)險(xiǎn)進(jìn)行測(cè)算,并檢驗(yàn)其有效性,預(yù)測(cè)效果較好。 本文的重點(diǎn)及創(chuàng)新點(diǎn)在于套利點(diǎn)確定方法的研究。在差價(jià)套利部分,分別在正態(tài)分布和t分布的假設(shè)前提下,研究差價(jià)序列和對(duì)數(shù)差價(jià)序列的最優(yōu)套利點(diǎn)確定方法及步驟,在比價(jià)套利部分,分別在正態(tài)分布和t分布的假設(shè)前提下,研究差價(jià)序列的最優(yōu)套利點(diǎn)確定方法和步驟。本文運(yùn)用計(jì)算最大套利期望收益的方法得到最優(yōu)套利點(diǎn),運(yùn)用Lingo軟件求其數(shù)值解。 得到了以下結(jié)論：滬深300股指期貨不同到期日合約間差價(jià)及其差價(jià)對(duì)數(shù)序列部分平穩(wěn),且香港恒生指數(shù)的估計(jì)值與真實(shí)值的價(jià)差均屬于平穩(wěn)時(shí)間序列。對(duì)比價(jià)套利模型進(jìn)行轉(zhuǎn)換,得到△P=u(?)=PHS-βHS,因此,比價(jià)套利模型的套利點(diǎn)確定方法與差價(jià)套利的思想和方法一致。以此為依據(jù),研究得到了：在σ相同條件下,t分布的最大期望收益比正態(tài)分布高,伴隨t分布自由度增大,最優(yōu)套利點(diǎn)向正態(tài)分布的情景逼近�？紤]交易成本的最優(yōu)套利點(diǎn),比不考慮交易成本的最優(yōu)套利點(diǎn)離均值點(diǎn)更遠(yuǎn),最優(yōu)期望收益也更低。并且應(yīng)該將跨期套利的△P07-12和△P09-12作為主要關(guān)注對(duì)象,而跨市場(chǎng)跨品種套利應(yīng)該把△P10和△P12作為主要關(guān)注對(duì)象。
[Abstract]:The second part and the fourth part respectively use the time series method to select the high-frequency data to fit the price arbitrage model of the Shanghai and Shenzhen 300 stock index futures and the Hang Seng index of Hong Kong and the arbitrage model of the Shanghai and Shenzhen 300 stock index futures. The arbitrage risk is measured by price conditional VaR method, and its effectiveness is tested. The emphasis and innovation of this paper lies in the study of arbitrage point determination method. Under the assumption of normal distribution and t distribution, this paper studies the method and steps of determining the optimal arbitrage point of the difference sequence and the logarithmic difference sequence in the arbitrage part. Under the assumption of normal distribution and t distribution, the method and steps of determining the optimal arbitrage point of the difference sequence are studied. In this paper, the optimal arbitrage point is obtained by using the method of calculating the expected maximum arbitrage income, and the numerical solution is obtained by using Lingo software. The conclusions are as follows: the price difference and the logarithmic sequence of the price difference between the different maturity days of the Shanghai and Shenzhen 300 stock index futures are partially stable, and the difference between the estimated value and the real value of the Hang Seng Index in Hong Kong belongs to the stationary time series. In this paper, we convert the arbitrage model to get PHS- 尾 HS,. Therefore, the arbitrage point determination method of the arbitrage model is consistent with the idea and method of arbitrage at the price difference. Based on this, it is obtained that the maximum expected income of the t distribution is higher than that of the normal distribution under the same 蟽 conditions, the degree of freedom of the t distribution increases, and the optimal arbitrage point approximates to the normal distribution. The optimal arbitrage point considering the transaction cost is farther from the mean point and the optimal expected income is lower than the optimal arbitrage point without considering the transaction cost. And we should take P07-12 and P09-12 of intertemporal arbitrage as the main object of concern, while cross-market cross-variety arbitrage should take P10 and P12 as the main objects of concern.
【學(xué)位授予單位】：北方工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：F224;F832.51

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