發(fā)布時(shí)間：2018-05-07 12:14

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

【摘要】：近年來(lái),隨著我國(guó)金融市場(chǎng)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(shí)也將金融計(jì)量分析推至前所未有的高度。時(shí)間序列作為金融市場(chǎng)中最常見(jiàn)的觀測(cè)數(shù)據(jù),是時(shí)市場(chǎng)行為的真實(shí)刻畫(huà),通過(guò)對(duì)其進(jìn)行量化分析,能夠挖掘市場(chǎng)中潛在的信息,進(jìn)而為投資決策提供理論和技術(shù)支撐,是策略制定、風(fēng)險(xiǎn)管理、資產(chǎn)定價(jià)和產(chǎn)品設(shè)計(jì)等工作的前提�；诮鹑跁r(shí)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(yàn)?zāi)B(tài)分解(EEMD)應(yīng)用到金融時(shí)間序列分析中。首先,利用EEMD建立多尺度集成預(yù)測(cè)模型。先用EEMD將原始序列分解并重構(gòu)成高頻、趨勢(shì)和低頻三個(gè)子序列；再結(jié)合Elman神經(jīng)網(wǎng)絡(luò)、支持向量機(jī)(SVM)和GM(1,1)對(duì)各部分進(jìn)行擬合；集成模型最后的預(yù)測(cè)值為各部分預(yù)測(cè)值之和。實(shí)證結(jié)果表明：該多尺度集成模型的預(yù)測(cè)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(dòng)溢出效應(yīng)。利用EEMD將股價(jià)和匯率序列分解并重構(gòu)成高頻(短期波動(dòng)項(xiàng))、。低頻(中期波動(dòng)項(xiàng))和長(zhǎng)期趨勢(shì)三個(gè)波動(dòng)成分,從時(shí)域和頻域的雙重視角來(lái)探究股市與匯市的波動(dòng)溢出效應(yīng)。在不同的波動(dòng)層次上,分別結(jié)合Granger因果檢驗(yàn)和時(shí)變Copula探究波動(dòng)溢出的方向和強(qiáng)度。研究表明：短期波動(dòng)項(xiàng)對(duì)原始序列波動(dòng)的貢獻(xiàn)最大；不同時(shí)間尺度上的波動(dòng)溢出方向和強(qiáng)度是不同的。最后,利用EEMD去噪建立跨期套利策略。將價(jià)差信號(hào)看作趨勢(shì)和噪聲的疊加,用EEMD方法對(duì)價(jià)差序列進(jìn)行消噪,參考均值回復(fù)策略的原理,結(jié)合價(jià)差在趨勢(shì)周?chē)牟▌?dòng)狀況尋找套利機(jī)會(huì),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(guó)科學(xué)技術(shù)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：F224;F832.51

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