本文選題：金融時間序列 + 多尺度　； 參考：《中國科學(xué)技術(shù)大學(xué)》2016年碩士論文

【摘要】：近年來,隨著我國金融市場的發(fā)展以及投資品種的日益豐富,量化投資逐漸步入人們的視野,同時也將金融計量分析推至前所未有的高度。時間序列作為金融市場中最常見的觀測數(shù)據(jù),是時市場行為的真實刻畫,通過對其進行量化分析,能夠挖掘市場中潛在的信息,進而為投資決策提供理論和技術(shù)支撐,是策略制定、風(fēng)險管理、資產(chǎn)定價和產(chǎn)品設(shè)計等工作的前提�；诮鹑跁r間序列的多尺度、非線性和非平穩(wěn)和的多重特性,本文將集成經(jīng)驗?zāi)B(tài)分解(EEMD)應(yīng)用到金融時間序列分析中。首先,利用EEMD建立多尺度集成預(yù)測模型。先用EEMD將原始序列分解并重構(gòu)成高頻、趨勢和低頻三個子序列；再結(jié)合Elman神經(jīng)網(wǎng)絡(luò)、支持向量機(SVM)和GM(1,1)對各部分進行擬合；集成模型最后的預(yù)測值為各部分預(yù)測值之和。實證結(jié)果表明：該多尺度集成模型的預(yù)測精度要顯著高于傳統(tǒng)的單一模型和集成模型。其次,利用EEMD探究股市與匯市的波動溢出效應(yīng)。利用EEMD將股價和匯率序列分解并重構(gòu)成高頻(短期波動項)、。低頻(中期波動項)和長期趨勢三個波動成分,從時域和頻域的雙重視角來探究股市與匯市的波動溢出效應(yīng)。在不同的波動層次上,分別結(jié)合Granger因果檢驗和時變Copula探究波動溢出的方向和強度。研究表明：短期波動項對原始序列波動的貢獻最大；不同時間尺度上的波動溢出方向和強度是不同的。最后,利用EEMD去噪建立跨期套利策略。將價差信號看作趨勢和噪聲的疊加,用EEMD方法對價差序列進行消噪,參考均值回復(fù)策略的原理,結(jié)合價差在趨勢周圍的波動狀況尋找套利機會,相比于傳統(tǒng)的小波去噪方法,避免了小波基函數(shù)參數(shù)選擇的這一難題。
[Abstract]:In recent years, with the development of our financial market and the increasing variety of investment, the quantitative investment has gradually stepped into the people's vision, at the same time, the financial econometric analysis has been pushed to an unprecedented height. As the most common observation data in the financial market, time series is the real depiction of the market behavior. Through the quantitative analysis of the time series, the potential information in the market can be excavated, thus providing theoretical and technical support for the investment decision. It is a prerequisite for strategy making, risk management, asset pricing and product design. Based on the multi-scale, nonlinear and non-stationary characteristics of financial time series, this paper applies the integrated empirical mode decomposition (EMD) to the analysis of financial time series. Firstly, the multi-scale integrated prediction model is established by using EEMD. First, the original sequence is decomposed by EEMD to form three sub-sequences of high frequency, trend and low frequency; then, combined with Elman neural network, support vector machine (SVM) and GM-1) are fitted to each part, and the final prediction value of the integrated model is the sum of the predicted values of each part. The empirical results show that the prediction accuracy of the multi-scale integrated model is significantly higher than that of the traditional single model and integrated model. Secondly, using EEMD to explore the volatility spillover effect of stock market and foreign exchange market. Using EEMD to decompose stock price and exchange rate sequence to form high frequency (short term fluctuation term). From the perspective of time domain and frequency domain, the volatility spillover effects of stock market and foreign exchange market are studied from the perspective of low frequency (medium term fluctuation term) and long term trend. At different volatility levels, the direction and intensity of volatility spillover are explored with Granger causality test and time-varying Copula respectively. The results show that the short-term fluctuation term has the greatest contribution to the fluctuation of the original series, and the direction and intensity of the volatility spillover are different in different time scales. Finally, the cross-period arbitrage strategy is established by using EEMD denoising. The spread signal is regarded as the superposition of trend and noise, and the spread sequence is de-noised by EEMD method. Referring to the principle of mean recovery strategy and combining the fluctuation of price difference around the trend, the arbitrage opportunity is found, compared with the traditional wavelet de-noising method. The problem of parameter selection of wavelet basis function is avoided.
【學(xué)位授予單位】：中國科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：F224;F832.51

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