本文選題：滬銅期貨 + 價格預(yù)測��； 參考：《蘭州交通大學(xué)》2017年碩士論文

【摘要】：隨著中國市場經(jīng)濟(jì)地位和人們理財意識的提高,期貨交易成為金融交易以及金融衍生品交易中的一個重要交易品種,期貨市場的健康穩(wěn)定發(fā)展也成為管理者和投資者研究的重點。不管是利用期貨市場進(jìn)行投資還是投機(jī),做好風(fēng)險控制是尤為重要的。而做好風(fēng)險控制的前提就需要對期貨價格進(jìn)行預(yù)測,在預(yù)測分析之后制定相應(yīng)的交易原則,按照交易原則進(jìn)行風(fēng)控處理。目前,隨著現(xiàn)代科技的不斷進(jìn)步與發(fā)展,期貨的預(yù)測方法也越來越多,以統(tǒng)計學(xué)中的方法為例,有時間序列預(yù)測模型、灰色預(yù)測模型、神經(jīng)網(wǎng)絡(luò)預(yù)測模型等等。本文首先選取了其中的ARIMA模型、GARCH模型以及BP神經(jīng)網(wǎng)絡(luò)模型三種單一的預(yù)測模型對滬銅期貨價格進(jìn)行預(yù)測。選取滬銅主連合約從2015年1月5日到2015年9月25日共180個交易日的收盤價數(shù)據(jù)作為研究對象,其中2015年1月5日到2015年8月28日的滬銅主連合約收盤價數(shù)據(jù)用于擬合估計模型,剩余的數(shù)據(jù)用于預(yù)測結(jié)果的對比分析。實證的結(jié)果表明:BP神經(jīng)網(wǎng)絡(luò)模型的累計相對誤差值和MAPE值都比ARIMA模型和GARCH模型要小,說明BP神經(jīng)網(wǎng)絡(luò)模型是這三種模型中預(yù)測精度最高的。這主要源于BP神經(jīng)網(wǎng)絡(luò)模型有著強(qiáng)大的自學(xué)能力,它可以通過訓(xùn)練學(xué)習(xí)找到參數(shù)之間的規(guī)律和特點,掌握數(shù)據(jù)間的依存關(guān)系。其次,根據(jù)前面三個單一預(yù)測模型的預(yù)測效果,本文又在此基礎(chǔ)上優(yōu)化得到了兩個組合預(yù)測模型,即最優(yōu)權(quán)重線性組合預(yù)測模型和基于BP神經(jīng)網(wǎng)絡(luò)的組合預(yù)測模型,并用這兩個組合預(yù)測模型同樣做了實證分析,得到的結(jié)論是:兩種組合預(yù)測模型相比單一的預(yù)測模型,都在一定程度上提高了預(yù)測精度;在這兩種組合預(yù)測模型中,基于BP神經(jīng)網(wǎng)絡(luò)的組合預(yù)測模型具有更高的預(yù)測精度,預(yù)測效果更好。最后,考慮到不同模型對數(shù)據(jù)的適用性不同,本文又選取了2016年10月10日到2016年11月18日共30個交易日的滬銅主連收盤價進(jìn)行短期時間跨度預(yù)測研究,對比180個交易日的長期時間跨度的預(yù)測結(jié)果,來探究各模型在滬銅期貨價格預(yù)測中的適用條件和范圍。結(jié)果表明,基于BP神經(jīng)網(wǎng)絡(luò)的組合預(yù)測模型無論是在短期時間跨度還是長期時間跨度預(yù)測中都有著很好的預(yù)測效果。
[Abstract]:With the development of China's market economy status and people's awareness of financial management, futures trading has become an important trading variety in financial transactions and financial derivatives transactions. The healthy and stable development of futures market has also become the focus of managers and investors. Whether using futures market for investment or speculation, risk control is particularly important. The premise of risk control is to forecast the futures price. After forecasting and analyzing, the corresponding trading principles should be worked out, and the wind control should be carried out according to the transaction principles. At present, with the progress and development of modern science and technology, there are more and more forecasting methods of futures, such as time series forecasting model, grey forecasting model, neural network forecasting model and so on. This paper first selects Arima model GARCH model and BP neural network model to forecast Shanghai copper futures price. The closing price data of 180 trading days from January 5, 2015 to September 25, 2015 are selected as the research objects. The closing price data of Shanghai Copper main Company contract from January 5, 2015 to August 28, 2015 are used to fit the estimated model. The remaining data are used for comparative analysis of the predicted results. The empirical results show that the cumulative relative error and MAPE of the BP neural network model are smaller than those of Arima model and GARCH model, indicating that the BP neural network model is the most accurate of the three models. This is mainly due to the strong self-learning ability of BP neural network model, which can find the rules and characteristics of parameters through training and learning, and grasp the dependence of data. Secondly, according to the prediction effect of the first three single prediction models, this paper optimizes two combined forecasting models, that is, the optimal weight linear combination prediction model and the BP neural network based combination forecasting model. The two combined forecasting models are also used for empirical analysis. The conclusion is that the two combined forecasting models have improved the prediction accuracy to some extent compared with the single prediction model; in these two combined forecasting models, the two combined forecasting models have improved the prediction accuracy to a certain extent. The combined prediction model based on BP neural network has higher prediction accuracy and better prediction effect. Finally, considering the different applicability of different models to the data, this paper also selects 30 trading days from October 10, 2016 to November 18, 2016 to carry out short-term time span prediction research on the closing price of Shanghai Copper main Line. By comparing the forecasting results of 180 trading days with a long time span, this paper probes into the applicable conditions and scope of each model in Shanghai copper futures price forecasting. The results show that the combined prediction model based on BP neural network has good prediction effect in both short and long term time span prediction.
【學(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F224;F724.5;F764.2

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