本文選題：量價關(guān)系 + 協(xié)整分析��； 參考：《華南理工大學(xué)》2012年碩士論文

【摘要】：證券技術(shù)分析是廣泛采用的一種證券投資分析方法。投資者根據(jù)技術(shù)指標(biāo)預(yù)測股市未來的行情走勢以期獲得更多的收益，此時，最關(guān)鍵的就是尋找股價反轉(zhuǎn)點(diǎn)。如果能夠較早的判斷股價的反轉(zhuǎn)點(diǎn)，做到低吸高拋，投資者將獲得豐厚的回報。交易量和股價（簡稱量價）間的關(guān)系在技術(shù)分析中占據(jù)著重要的地位�；诹績r，本文設(shè)計(jì)了一種識別反轉(zhuǎn)點(diǎn)的模型，對于投資者進(jìn)行更好的投資決策具有參考意義。 根據(jù)證券技術(shù)分析的分析理念，及收盤價和交易量間存在的協(xié)整關(guān)系，本文將收盤價和交易量同時進(jìn)行考慮，設(shè)計(jì)了雙指標(biāo)極小投資模型（簡稱雙極模型）。雙極模型中有兩個參數(shù)，分別是P和Q，其中，P代表當(dāng)日收盤價是連續(xù)P天的極小值，Q代表當(dāng)日交易量是連續(xù)Q天的極小值，不同的參數(shù)組合代表不同的投資模型。雙極模型的設(shè)計(jì)思路是給定P和Q，若當(dāng)日收盤價是連續(xù)P天的極小值并且當(dāng)日交易量是連續(xù)Q天的極小值，則雙極指標(biāo)值取為1，否則取為0，當(dāng)雙極指標(biāo)值由1變?yōu)?時，則買進(jìn)股票；買進(jìn)股票后，當(dāng)股價從一個新高位置下跌，從這個新高位置開始，若4天內(nèi)回落達(dá)到5%，則賣出股票。對于股票收盤價序列和交易量序列一階單整，且差分后序列一階自相關(guān)的情況，理論上證明了雙極模型設(shè)計(jì)的合理性。 假定投資期限為n年，利用雙極模型進(jìn)行投資模擬。首先，計(jì)算不同參數(shù)組合下每次買賣的投資收益，按復(fù)利計(jì)算其n年投資總收益，并計(jì)算其年均收益率。其次，根據(jù)EGARCH模型，算出每次投資的日VaR值，從而得到一組VaR序列，將其99%分位數(shù)作為投資期限內(nèi)的總風(fēng)險值。用年均收益率和總風(fēng)險值綜合評估每次投資的優(yōu)劣。再次，為了尋找較好的參數(shù)組合，分別作出不同參數(shù)組合下的收益率等高線和風(fēng)險等高線，并觀察收益率和風(fēng)險對參數(shù)的敏感程度。最后，對參數(shù)進(jìn)行優(yōu)化，得到最佳參數(shù)組合。本文用萬科A股票進(jìn)行實(shí)驗(yàn)，結(jié)果表明，收益率對P比較敏感，對Q不太敏感，而風(fēng)險對P和Q都比較敏感；若采用RAROC指標(biāo)，最佳參數(shù)組合為P=2、Q=5；對于不同類型的投資者，根據(jù)有效邊界和無差異曲線，得到其相應(yīng)的最佳參數(shù)組合，其中，對于保守型投資者來說，最優(yōu)的參數(shù)組合為P=5、Q=4；對于進(jìn)取型投資者來說，，最優(yōu)的參數(shù)組合為P=3、Q=4。
[Abstract]:Securities technical analysis is a widely used method of securities investment analysis. When investors use technical indicators to predict how stocks will fare in the future in the hope of earning more, the key is to find a price reversal. If you can judge the reverse point of the stock price earlier and sell it low, investors will get a good return. The relationship between trading volume and stock price plays an important role in technical analysis. Based on the quantity price, this paper designs a model to identify the inversion point, which has reference significance for investors to make better investment decision. According to the analysis idea of security technical analysis and the cointegration relationship between closing price and trading volume, this paper considers the closing price and trading volume at the same time, and designs a double-index minimal investment model (dipole model for short). There are two parameters in the bipolar model, one is P and the other is Q.With P representing the closing price of the day is the minimum value of continuous P day, Q represents the minimum value of continuous Q day trading volume, and different parameter combinations represent different investment models. The design idea of the bipolar model is to give P and Q, if the closing price of the day is the minimum value of the continuous P day and the trading volume is the minimum value of the continuous Q day, then the bipolar index value is taken as 1, or 0, when the bipolar index value changes from 1 to 0. You buy stocks; after you buy stocks, you go down from a high, start at that high, and sell if you fall back to 5 in four days. It is proved theoretically that the design of the bipolar model is reasonable for the stock closing price sequence and the trading volume sequence in the case of the first order autocorrelation of the first order of the stock closing price sequence and the trading volume sequence and the first order autocorrelation of the difference sequence. Assuming that the investment period is n years, the investment simulation is carried out by using the bipolar model. First of all, the investment income of each purchase and sale under different parameter combinations is calculated, the total investment income per year is calculated by compound interest, and the average annual return rate is calculated. Secondly, according to the EGARCH model, the daily VaR value of each investment is calculated, and a set of VaR sequence is obtained. The 99quartile is taken as the total risk value within the investment period. The average annual rate of return and the total risk value are used to evaluate the merits and demerits of each investment. Thirdly, in order to find a better combination of parameters, the yield contour and the risk contour under different parameter combinations are made, and the sensitivity of the yield and risk to the parameters is observed. Finally, the optimal combination of parameters is obtained by optimizing the parameters. In this paper, Vanke A stock is used to experiment. The results show that the return rate is sensitive to P, less sensitive to Q, and the risk is sensitive to both P and Q; if the RAROC index is used, the best parameter combination is Pf2Q5; for different types of investors, According to the efficient boundary and the non-difference curve, the corresponding optimal parameter combination is obtained, in which, for conservative investors, the optimal parameter combination is P5 / QF4, and for enterprising investors, the optimal parameter combination is Pf3 / QF4.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：F224;F830.91

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 王獻(xiàn)東;;基于GARCH模型的波動率預(yù)測及應(yīng)用[J];常州工學(xué)院學(xué)報;2011年06期

2 崔連翔;張瑩;;外匯儲備對我國物價水平的影響——基于協(xié)整檢驗(yàn)與誤差修正模型的分析[J];經(jīng)濟(jì)問題;2012年03期

3 郭興義;杜本峰;何龍燦;;金融計(jì)量學(xué)研究最新進(jìn)展[J];經(jīng)濟(jì)學(xué)動態(tài);2003年01期

4 羅光成;周燕春;;經(jīng)濟(jì)增長貢獻(xiàn)率影響因素分析——基于誤差修正模型實(shí)證研究[J];商業(yè)時代;2012年02期

5 靳庭良;;ADF檢驗(yàn)與PP檢驗(yàn)的可靠性比較[J];統(tǒng)計(jì)與決策;2007年14期

6 周孝華;王小慶;;基于穩(wěn)定分布的風(fēng)險價值(VaR)研究與實(shí)證[J];統(tǒng)計(jì)與決策;2009年22期

7 張維,閆冀楠;關(guān)于上海股市量價因果關(guān)系的實(shí)證探測[J];系統(tǒng)工程理論與實(shí)踐;1998年06期

8 莫達(dá)隆;;利用ADF檢驗(yàn)對時間序列進(jìn)行建模[J];時代金融;2010年04期

9 劉金全,崔暢,邵欣煒;股票價格與實(shí)際利率之間長期協(xié)整與短期影響關(guān)系的實(shí)證檢驗(yàn)[J];預(yù)測;2002年05期

10 李瑋;張艷青;張榮霞;;山西省能源消費(fèi)和經(jīng)濟(jì)增長關(guān)系研究:基于Johansen協(xié)整檢驗(yàn)和Granger因果關(guān)系檢驗(yàn)[J];中國礦業(yè);2012年03期

相關(guān)碩士學(xué)位論文 前1條

1 余喜洋;多元GARCH模型及其在VaR計(jì)算中應(yīng)用[D];上海財(cái)經(jīng)大學(xué);2006年

本文編號：1865878