本文選題：波動率　切入點：ARFIMA模型　出處：《湘潭大學》2014年碩士論文　論文類型：學位論文

【摘要】：股票市場形勢復雜,變化多端,對其波動率的研究已經(jīng)成為該領域的核心內(nèi)容之一。我國滬深股票市場為新興金融市場,自實施漲跌幅限制以來已趨于穩(wěn)定,但市場體制還需進一步地完善。波動性是衡量股市質(zhì)量和效率的重要指標,一定幅度的波動有利于活躍股市并使其持續(xù)發(fā)展,過于頻繁且劇烈的波動反而會適得其反。因此,波動率的研究對于我國股市的規(guī)范以及投資者的理性判斷具有重要意義。 本文以上海和深圳股市最具代表性的上證指數(shù)和深圳綜指為研究對象,采用最新歷史數(shù)據(jù)來分析我國近年股市波動情況。并選取了每只股票的日數(shù)據(jù)、5分鐘數(shù)據(jù)和1分鐘數(shù)據(jù),對其建立兩種均值模型和六種GARCH方差模型進行波動率的研究,得到兩個結(jié)論： 1、最適合研究近年我國滬深股市收益波動率的均值模型為ARFIMA模型,方差模型為EGARCH模型。 2、不同頻率的收益序列,其最優(yōu)波動率模型不同,且頻率越高,波動率模型的擬合效果和預測效果越好。本文1分鐘數(shù)據(jù)的波動率模型的研究結(jié)果最理想。 原因有三： 1、隨著我國股票市場體制的完善,逐漸趨于穩(wěn)定,股市收益時間序列已經(jīng)具備長記憶性。 2、ARFIMA-EGARCH模型綜合考慮了我國股市收益序列的自回歸異方差性、杠桿效應、長記憶性和風險對收益的影響。 3、數(shù)據(jù)頻率越高,對股市信息反映的更全面,更利于波動率模型的擬合和預測。
[Abstract]:The situation of stock market is complex and changeable. The research on volatility has become one of the core contents in this field. China's stock market in Shanghai and Shenzhen is an emerging financial market, which has become stable since the implementation of the limit of price and decline. But the market system needs to be further improved. Volatility is an important index to measure the quality and efficiency of the stock market. A certain range of fluctuations is conducive to the active and sustainable development of the stock market. The study of volatility is of great significance to the regulation of Chinese stock market and the rational judgment of investors. This paper takes the Shanghai and Shenzhen Composite Index, which is the most representative stock market in Shanghai and Shenzhen, as the research object. The latest historical data are used to analyze the stock market volatility in recent years in China. The daily data of 5 minutes and 1 minute of each stock are selected to study the volatility of two mean models and six GARCH variance models. Two conclusions are drawn:. 1. The ARFIMA model and the EGARCH model are the most suitable models to study the volatility of Shanghai and Shenzhen stock markets in recent years. 2. The optimal volatility model is different for different frequency return sequences, and the higher the frequency, the better the fitting effect and prediction effect of volatility model. There are three reasons:. 1. With the perfection of the stock market system, the stock market income time series has long memory. 2ARFIMA-EGARCH model synthetically considers the influence of autoregressive heteroscedasticity, leverage effect, long memory and risk on the return of stock market in China. 3. The higher the data frequency is, the more comprehensive the stock market information is, and the more favorable the volatility model is to fit and forecast.
【學位授予單位】：湘潭大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F830.91;F224

【參考文獻】

相關期刊論文 前10條

1 陳麗娟;;基于EGARCH-M模型和滬深300指數(shù)的股市風險分析[J];東北財經(jīng)大學學報;2010年02期

2 關華;;基于GARCH族模型的深證成指價格波動研究[J];湖南大學學報(社會科學版);2011年03期

3 劉金全;崔暢;;中國滬深股市收益率和波動性的實證分析[J];經(jīng)濟學(季刊);2002年03期

4 王蔣鳳;吳群英;;基于GARCH族模型對中國股市波動的分析與預測[J];經(jīng)濟研究導刊;2011年34期

5 陳艷;韓立磊;;滬深300指數(shù)收益波動性實證研究[J];金融經(jīng)濟;2009年14期

6 賀京同,霍焰;投資者行為、資產(chǎn)價格與股市波動[J];南開經(jīng)濟研究;2004年02期

7 史代敏;羅來東;龐皓;;股票市場收益率波動長記憶性的分解及實證研究[J];數(shù)量經(jīng)濟技術經(jīng)濟研究;2006年08期

8 魯萬波;;基于非參數(shù)GARCH模型的中國股市波動性預測[J];數(shù)理統(tǒng)計與管理;2006年04期

9 金秀;姚瑾;;用修正重標極差法對上證指數(shù)長期記憶性的研究[J];數(shù)理統(tǒng)計與管理;2006年05期

10 金秀;姚瑾;莊新田;;基于分數(shù)階差分的ARFIMA模型及預測效果研究[J];數(shù)理統(tǒng)計與管理;2007年05期

，

本文編號：1559372