本文選題：黃金價(jià)格　切入點(diǎn)：小波去噪　出處：《蘭州大學(xué)》2013年碩士論文

【摘要】：隨著金融市場(chǎng)中金融產(chǎn)品的多元化,以及金融衍生品的多樣化,人們會(huì)越來(lái)越加大在金融產(chǎn)品上的投資,但鑒于金融產(chǎn)品的特點(diǎn),收益既伴隨著風(fēng)險(xiǎn),顯示出預(yù)測(cè)準(zhǔn)確的重要性。眾多學(xué)者開(kāi)始對(duì)黃金價(jià)格進(jìn)行預(yù)測(cè),黃金價(jià)格序列受許多因素的影響并且具有較強(qiáng)的隨機(jī)波動(dòng)性和一些尖峰,因此,用單一的時(shí)間序列模型例如R、MA、ARIMA進(jìn)行預(yù)測(cè)往往不太理想,所以本文選擇了小波神經(jīng)網(wǎng)絡(luò)來(lái)進(jìn)行預(yù)測(cè)黃金價(jià)格。一方面利用了小波去噪使數(shù)據(jù)平滑,這樣去掉干擾大的噪聲,另一方面神經(jīng)網(wǎng)絡(luò)具有很強(qiáng)的非線性擬合能力,可映射任意復(fù)雜的非線性關(guān)系,所以在神經(jīng)網(wǎng)絡(luò)的基礎(chǔ)上加上小波去噪便可以更好地預(yù)測(cè)黃金價(jià)格的波動(dòng)。從而為投資者提供參考。 主要內(nèi)容如下： 1.在參閱文獻(xiàn)和理論的思考上,分析可能影響黃金價(jià)格的元素,進(jìn)而對(duì)他們用PERSON相關(guān)系數(shù)進(jìn)行分析,找出了美元指數(shù),原油,銀,道瓊斯指數(shù),上征指數(shù)五大因素,并確定他們的相關(guān)系數(shù)。 2.對(duì)選取的倫敦金價(jià)格進(jìn)行去噪,然后再把對(duì)應(yīng)的美元指數(shù),原油,銀,道瓊斯指數(shù),上證指數(shù)的數(shù)據(jù)代入,建立去躁前和去噪后的神經(jīng)網(wǎng)絡(luò)模型,再分別確定預(yù)測(cè)值,和真實(shí)值作對(duì)比。 3.然后分別算出去噪前模型的和去噪后模型的RMSE、MAXAE、MAE、MAXAPE、 MAPE,充分用數(shù)據(jù)說(shuō)明小波神經(jīng)網(wǎng)絡(luò)對(duì)于黃金價(jià)格預(yù)測(cè)擬合度好,準(zhǔn)確度高。
[Abstract]:With the diversification of financial products in financial markets and the diversification of financial derivatives, people will increase their investment in financial products more and more, but in view of the characteristics of financial products, the returns are accompanied by risks. Many scholars began to predict the gold price. The gold price sequence is influenced by many factors and has strong random volatility and some spikes. It is not ideal to use a single time series model such as RMA Arima to predict gold price. In this paper, wavelet neural network is chosen to predict gold price. On the one hand, wavelet denoising is used to smooth the data so as to remove the noise with large interference. On the other hand, neural networks have strong nonlinear fitting ability, and can map any complex nonlinear relations. So adding wavelet denoising to the neural network can better predict the fluctuation of gold price and provide reference for investors. The main contents are as follows:. 1. On the basis of literature and theoretical considerations, we analyze the elements that may affect the price of gold, and then use the PERSON correlation coefficient to find out the five major factors: dollar index, crude oil, silver, Dow Jones index and index. And determine their correlation coefficient. 2. The selected London gold price is de-noised, then the corresponding data of the dollar index, crude oil, silver, Dow Jones index and Shanghai stock index are substituted, and the neural network models before and after de-noising are established, and the predicted values are determined respectively. Compare with the true value. 3. Then the RMSE / MAXAE / MAXAPE, MAPE of the pre-noise model and the de-noised model are calculated respectively, which shows that the wavelet neural network has good fitting and high accuracy for gold price prediction.
【學(xué)位授予單位】：蘭州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：F830.9;TP18

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