發(fā)布時(shí)間：2018-03-23 05:21

  本文選題：EMD分解　切入點(diǎn)：HHT變換　出處：《吉林大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著經(jīng)濟(jì)全球化的不斷加深，，中國(guó)在各國(guó)際經(jīng)濟(jì)動(dòng)蕩時(shí)期仍然屹立不倒，中國(guó)資本融入世界經(jīng)濟(jì)的比重越來越高，已經(jīng)成為世界經(jīng)濟(jì)體不可分割的重要部分，中國(guó)金融體系的現(xiàn)狀嚴(yán)重影響世界經(jīng)濟(jì)格局。中國(guó)期貨市場(chǎng)就是在這樣的背景下產(chǎn)生和發(fā)展。雖然目前的期貨市場(chǎng)從規(guī)模到品種都具有局限性，但是我們從活躍的交易市場(chǎng)中不難看出中國(guó)的期貨市場(chǎng)的發(fā)展前景是不可限量的，而且目前中國(guó)期貨已經(jīng)開始發(fā)揮其套期保值和價(jià)格發(fā)現(xiàn)的作用了。為了更全面，更好的發(fā)展中國(guó)經(jīng)濟(jì)，從金融市場(chǎng)的各個(gè)方面與世界發(fā)達(dá)國(guó)家接軌，我們有必要對(duì)中國(guó)金融期貨的發(fā)展趨勢(shì)和影響因素進(jìn)行深入探討。通過對(duì)已有的交易信息進(jìn)行分析，從中找出可用規(guī)律，為未來的金融期貨市場(chǎng)交易提供可用信息，使得中國(guó)的金融期貨交易系統(tǒng)更健全、更完善。 由于金融數(shù)據(jù)大多是高頻的、非平穩(wěn)的、非線性的，能夠準(zhǔn)確分析處理這類過程中數(shù)據(jù)的數(shù)據(jù)分析方法又是極為有限的,現(xiàn)在可以使用的數(shù)字處理的方法多是針對(duì)線性非平穩(wěn)過程或非線性平穩(wěn)過程，諸如小波分析、Wagner-Ville、傅里葉頻譜分析、多種相平面表示法和時(shí)間延遲嵌入法。為了詳細(xì)考察，由現(xiàn)實(shí)世界中非線性、非平穩(wěn)隨機(jī)過程中產(chǎn)生的數(shù)據(jù)，我們迫切地需要新方法。希爾伯特-黃變換HHT（Hilbert-Huang Transform）是美國(guó)工程學(xué)院院士黃鍔等人于1998年提出的一種新型的針對(duì)大多數(shù)是非線性、非平穩(wěn)過程的數(shù)字信號(hào)處理方法。 用希爾伯特-黃變換進(jìn)行股指期貨數(shù)據(jù)分析大致分為以下幾個(gè)步驟：首先將待分析數(shù)據(jù)進(jìn)行歸一化處理并剔除交易中不合理數(shù)據(jù)，然后進(jìn)行經(jīng)驗(yàn)?zāi)７纸馍啥鄠�(gè)本征模函數(shù)，最后基于本征模函數(shù)進(jìn)行希爾伯特變換生成Hilbert譜和Hilbert邊際譜。本文主要研究了EMD對(duì)中國(guó)高頻金融數(shù)據(jù)的有效性和完備性以及基于HHT對(duì)股指期貨的日內(nèi)特征分析，日內(nèi)特征分析是根據(jù)股指期貨的絕對(duì)收益率和成交量分別進(jìn)行得研究和分析。 論文對(duì)股指期貨收益率進(jìn)行EMD分解，由于現(xiàn)有EMD分解的MATLAB程序不完全適用于大量高頻金融類數(shù)據(jù)，論文中進(jìn)行了多次實(shí)驗(yàn)并設(shè)計(jì)了數(shù)據(jù)重構(gòu)程序；首先介紹了實(shí)驗(yàn)數(shù)據(jù)的生成原理和實(shí)驗(yàn)數(shù)據(jù)的合理性；然后分別進(jìn)行了希爾伯特-黃變換，針對(duì)逐分鐘的金融數(shù)據(jù)的特點(diǎn)對(duì)Hilbert變換的取樣參數(shù)FS進(jìn)行了多次實(shí)驗(yàn)；最后采用最貼近實(shí)際的參數(shù)值進(jìn)行Hilbert變換，此過程首先對(duì)EMD分解出的IMF結(jié)果進(jìn)行分析和總結(jié)，然后對(duì)生成Hilbert譜和Hilbert邊際譜進(jìn)行了分析，總結(jié)了股指期貨的日內(nèi)趨勢(shì)和主要周期性。
[Abstract]:With the deepening of economic globalization, China is still standing in various periods of international economic turmoil. The proportion of Chinese capital into the world economy is increasing, and it has become an inseparable and important part of the world economy. The current situation of China's financial system has seriously affected the world economic pattern. It is against this background that China's futures market has emerged and developed. Although the current futures market has its limitations from scale to variety, But it is not difficult to see from the active trading market that the prospects for the development of China's futures market are limitless, and at present Chinese futures have begun to play their role of hedging and price discovery. In order to be more comprehensive, To better develop China's economy and connect with the developed countries in all aspects of the financial market, it is necessary for us to deeply discuss the development trend and influencing factors of China's financial futures. Through the analysis of the existing trading information, To find out the available rules, to provide the available information for the future financial futures market, and to make the financial futures trading system in China more sound and perfect. Because financial data are mostly high-frequency, non-stationary and nonlinear, data analysis methods that can accurately analyze and process such data are extremely limited. Most of the digital processing methods that can be used now are linear non-stationary processes or nonlinear stationary processes, such as wavelet analysis Wagner-Ville, Fourier spectrum analysis, multi-phase plane representation and time-delay embedding. Data from nonlinear, nonstationary random processes in the real world, We urgently need new methods. Hilbert-Huang transform (HHT(Hilbert-Huang transform) is a new digital signal processing method proposed by American Academy of Engineering academician Huang E et al in 1998, which is mostly nonlinear and non-stationary. The analysis of stock index futures data by Hilbert-Huang transform is divided into the following steps: firstly, the data to be analyzed is normalized and the unreasonable data in the transaction is eliminated, and then empirical mode decomposition is used to generate multiple intrinsic mode functions. Finally, Hilbert transform based on intrinsic mode function is used to generate Hilbert spectrum and Hilbert marginal spectrum. This paper mainly studies the validity and completeness of EMD to Chinese high-frequency financial data and the intra-day characteristic analysis of index futures based on HHT. Intra- day characteristic analysis is based on the absolute return and turnover of stock index futures. In this paper, stock index futures yield is decomposed by EMD. Because the existing MATLAB program of EMD decomposition is not fully applicable to a large number of high-frequency financial data, the paper has carried out many experiments and designed a data reconstruction program. Firstly, the principle of experimental data generation and the rationality of experimental data are introduced, then Hilbert-Huang transform is carried out, and the sampling parameter FS of Hilbert transform is tested several times according to the characteristics of minute by minute financial data. In the end, the Hilbert transform is carried out by using the closest parameter value, and the results of IMF decomposed by EMD are analyzed and summarized firstly, and then the generated Hilbert spectrum and Hilbert marginal spectrum are analyzed. The paper summarizes the intraday trend and main periodicity of stock index futures.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F224;F830.91

【參考文獻(xiàn)】

相關(guān)期刊論文 前6條

1 劉慧婷,張e

本文編號(hào)：1652065