【摘要】：隨著中國股票市場不斷發(fā)展和完善，時(shí)間序列分析用于股市的研究越來越多。傳遞函數(shù)模型在經(jīng)濟(jì)、工業(yè)以及工程領(lǐng)域方面應(yīng)用較廣泛，在股市應(yīng)用仍然少見。 本文將傳遞函數(shù)模型應(yīng)用于股市分析，主要利用價(jià)量的互相關(guān)函數(shù)檢驗(yàn)，分析在牛市、熊市和平衡市下，上證綜合指數(shù)價(jià)量關(guān)系。并與恒生指數(shù)、道·瓊斯工業(yè)股價(jià)平均指數(shù)和上海個(gè)股中國石化進(jìn)行比較。得出了上證綜合指數(shù)在不同趨勢下結(jié)果存在共性和差異，國內(nèi)外指數(shù)、指數(shù)和個(gè)股在同一個(gè)趨勢下結(jié)果存在共性和差異，，有價(jià)量存在同期關(guān)系，有也有價(jià)量互相反饋關(guān)系，還有價(jià)導(dǎo)致量或量導(dǎo)致價(jià)。再對(duì)價(jià)量建立傳遞函數(shù)模型，其優(yōu)點(diǎn)是模型的傳遞函數(shù)部分表達(dá)出價(jià)量的關(guān)系，噪聲部分反映了輸出序列自身的時(shí)序關(guān)系。對(duì)于輸出時(shí)間序列，若與輸入序列的影響越顯著、影響總期數(shù)越多時(shí)，建立的傳遞函數(shù)模型會(huì)優(yōu)于ARIMA模型。反之，影響不顯著，或者影響總期數(shù)較少，建立ARIMA模型反而更優(yōu)。 對(duì)多變量傳遞函數(shù)模型的實(shí)證研究，本文先利用多元統(tǒng)計(jì)學(xué)中的逐步回歸法，以及輸出序列與輸入序列的互相關(guān)函數(shù)，先剔除了不滿足條件的輸入序列，再建立模型。當(dāng)多個(gè)輸入序列之間存在高相關(guān)性的情況，本文嘗試兩種加權(quán)方法，構(gòu)造新輸入序列，其中根據(jù)輸出序列和輸入序列的互相關(guān)函數(shù)的比值得出權(quán)重而構(gòu)造的新輸入序列，與輸出序列建立的模型最優(yōu)。 針對(duì)干預(yù)事件的出現(xiàn)，本文在第三章理論基礎(chǔ)上提出了三種修改的傳遞函數(shù)模型，對(duì)其中輸入變量帶干預(yù)的傳遞函數(shù)模型進(jìn)行了實(shí)證分析，以股指期貨上市對(duì)股市的干預(yù)為例，實(shí)證結(jié)果表明在輸入序列受到干預(yù)時(shí)，采用提出的修改模型優(yōu)于原始的傳遞函數(shù)模型。
[Abstract]:With the development and perfection of Chinese stock market, time series analysis is more and more widely used in stock market research. Transfer function model is widely used in economy, industry and engineering, but it is still rare in stock market. In this paper, the transfer function model is applied to the stock market analysis, which mainly uses the cross-correlation function test of the price quantity to analyze the relationship between the price and quantity of the composite index in the bull market, the bear market and the equilibrium market. And compared with Hang Seng Index, Dow Jones Industrial average Index and Shanghai stock market Sinopec. It is concluded that there are commonness and differences in the results of the Shanghai Composite Index under different trends, and there are commonness and differences in the results of the domestic and foreign indexes, indices and individual stocks in the same trend, and there is a contemporaneous relationship between the price quantity and the mutual feedback relationship between the price and the quantity. And price leads to quantity or quantity to price. The advantage of the transfer function model is that the transfer function part of the model expresses the relation of the price and the noise part reflects the sequential relation of the output sequence itself. For the output time series, if the influence of the input sequence and the input sequence is more significant, the established transfer function model will be better than the ARIMA model. On the other hand, if the influence is not significant, or the total number of periods is less, it is better to establish ARIMA model. Based on the empirical study of multivariate transfer function model, this paper firstly uses the stepwise regression method in multivariate statistics and the cross-correlation function between output sequence and input sequence to eliminate the input sequence which does not satisfy the conditions, and then establishes the model. When there is a high correlation between multiple input sequences, this paper attempts two weighted methods to construct new input sequences, in which a new input sequence is constructed based on the ratio of the cross-correlation function of the output sequence and the input sequence. The model established with the output sequence is optimal. According to the emergence of intervention events, this paper puts forward three modified transfer function models on the basis of the third chapter theory, and makes an empirical analysis of the transfer function model with the intervention of input variables, taking the intervention of stock index futures listing on the stock market as an example. The empirical results show that the proposed modified model is superior to the original transfer function model when the input sequence is interfered.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：F224;F832.51

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