本文選題：狀態(tài)空間模型 + 預(yù)測(cè)　； 參考：《合肥工業(yè)大學(xué)》2012年碩士論文

【摘要】：本文主要研究了狀態(tài)空間模型預(yù)測(cè)時(shí)，，樣本序列的變點(diǎn)估計(jì)值對(duì)模型預(yù)測(cè)影響的問(wèn)題。 第一章敘述了變點(diǎn)和狀態(tài)空間模型的研究背景及國(guó)內(nèi)外研究現(xiàn)狀，并對(duì)檢測(cè)變點(diǎn)的幾種經(jīng)典方法作了簡(jiǎn)單介紹。第二章介紹了狀態(tài)空間模型的定義和Kalman濾波，并給出了ARIMA模型轉(zhuǎn)化為狀態(tài)空間模型的標(biāo)準(zhǔn)形式的方法。第三章介紹了Γ分布參數(shù)變點(diǎn)的檢測(cè)方法，并討論了分布參數(shù)變點(diǎn)在狀態(tài)空間模型預(yù)測(cè)中的應(yīng)用。 在應(yīng)用方面，首先將上證A股指數(shù)收盤(pán)價(jià)序列轉(zhuǎn)化，得到全漲收益率序列和全跌收益率序列，利用分布參數(shù)變點(diǎn)理論得到全漲收益率序列和全跌收益率序列的變點(diǎn)個(gè)數(shù)及其所處時(shí)間位置。然后根據(jù)變點(diǎn)位置的不同，分別對(duì)三個(gè)不同時(shí)段的上證A股指數(shù)收盤(pán)價(jià)序列建立狀態(tài)空間模型。通過(guò)比較預(yù)測(cè)結(jié)果，得出變點(diǎn)越少，狀態(tài)空間模型的預(yù)測(cè)精度越高的結(jié)論。最后在無(wú)變點(diǎn)的情況下比較了ARIMA、自回歸與狀態(tài)空間模型的預(yù)測(cè)結(jié)果，說(shuō)明了狀態(tài)空間模型具有更好的預(yù)測(cè)效果。
[Abstract]:In this paper, the influence of the change point estimation of the sample sequence on the prediction of the state space model is studied. In the first chapter, the research background of the change point and state space model and the current research situation at home and abroad are described, and several classical methods for detecting the change point are briefly introduced. In chapter 2, the definition of state space model and Kalman filter are introduced, and the method of transforming ARIMA model into state space model is given. In chapter 3, the detection method of parameter change point in 螕 distribution is introduced, and the application of variation point of distribution parameter in prediction of state space model is discussed. In terms of application, first of all, the closing price sequence of Shanghai A-share index is transformed to obtain the full-rise yield sequence and the full-fall yield sequence. By using the theory of change point of distribution parameter, the number of change points and the time position of all rise rate series and total fall return sequence are obtained. Then, according to the different position of the change points, the state-space model is established for the closing sequence of the A-share index in three different periods of time. By comparing the prediction results, it is concluded that the lower the change points, the higher the prediction accuracy of the state space model. Finally, the prediction results of Arima, autoregressive and state space models are compared in the case of no change points, which shows that the state space model has better prediction effect.
【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類(lèi)號(hào)】：F224;F832.51

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