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  本文選題：高頻金融數(shù)據(jù)　切入點：波動率　出處：《重慶理工大學》2017年碩士論文　論文類型：學位論文

【摘要】：近年來,隨著電子化交易在金融市場的廣泛應用以及信息技術的迅猛發(fā)展,金融市場的波動性也日趨激烈。如何更精確地預測資產(chǎn)的收益風險引起了人們的高度重視。估計資產(chǎn)收益的波動率是預測收益風險的關鍵問題之一,而波動率的估計精度又與模型的假設及數(shù)據(jù)的采集頻率密切相關。一般而言,模型的波動率估計值越精確以及所使用的數(shù)據(jù)頻率越高,波動率的估計精度就越好。因此,以高頻金融數(shù)據(jù)為研究對象,如何建立一個具有統(tǒng)計優(yōu)良性的波動率模型是本文的主要研究目標。本文的主要研究內(nèi)容及創(chuàng)新點如下:1.基于NGARCH模型刻畫了波動率的杠桿效應特征,本文在已實現(xiàn)GARCH模型的波動率方程中引入?yún)?shù)的擾動,提出了已實現(xiàn)NGARCH模型。在新模型中,引入的參數(shù)與誤差項序列成負相關關系,使得新息既在大小上對當前收益作出擾動,又在方向上對當前收益作出擾動。2.鑒于模型的參數(shù)估計精度直接影響風險預測的準確性,本文采用蒙特卡羅方法對提出的已實現(xiàn)NGARCH模型的參數(shù)估計的穩(wěn)健性進行檢驗。隨機模擬結果顯示,在%5的顯著性水平下,所有參數(shù)估計值的均方誤差均顯著。同時,當設定模擬次數(shù)為500次時,隨著樣本量的增大,所有參數(shù)的估計值依然顯著。模擬結果表明,本文提出的已實現(xiàn)NGARCH模型的波動率估計方法有較好的穩(wěn)健性。3.基于文中提出的已實現(xiàn)NGARCH模型對上證50指數(shù)和上證380指數(shù)5min頻率的高頻數(shù)據(jù)進行了實證分析,并對其風險預測結果進行了比率檢驗。其次,對已實現(xiàn)NGARCH模型和已實現(xiàn)GARCH模型的風險預測結果進行了比較,結果表明,已實現(xiàn)GARCH模型比已實現(xiàn)NGARCH模型高估了市場風險。本文提出的已實現(xiàn)NGARCH模型,為金融風險管理提供了新的方法,在一定程度上豐富了金融風險管理的理論。
[Abstract]:In recent years, with the wide application of electronic transactions in financial markets and the rapid development of information technology, The volatility of financial markets is becoming more and more intense. How to predict the return risk of assets more accurately has attracted great attention. Estimating the volatility of asset returns is one of the key problems in predicting income risk. The accuracy of volatility estimation is closely related to the assumptions of the model and the frequency of data collection. In general, the more accurate the volatility estimate is and the higher the frequency of the data used, the better the accuracy of volatility estimation. Taking high-frequency financial data as the research object, How to establish a volatility model with statistical excellence is the main research objective of this paper. The main contents and innovations of this paper are as follows: 1. Based on the NGARCH model, the characteristics of volatility leverage are described. In this paper, the parameter perturbation is introduced into the volatility equation of the realized GARCH model, and the realized NGARCH model is proposed. In the new model, the introduced parameters have a negative correlation with the series of error terms, which makes the innovation not only disturb the current income in the magnitude, but also the new model. In view of the fact that the accuracy of parameter estimation of the model directly affects the accuracy of risk prediction, In this paper, Monte-Carlo method is used to test the robustness of the proposed parameter estimation of NGARCH model. The results of random simulation show that the mean square error of all parameter estimates is significant at the significant level of 5. At the same time, When the number of simulations is set to 500 times, with the increase of sample size, the estimated values of all parameters are still significant. The simulation results show that, The volatility estimation method of realized NGARCH model proposed in this paper has good robustness. 3. Based on the realized NGARCH model proposed in this paper, the high frequency data of Shanghai 50 index and Shanghai 380 index 5min frequency are empirically analyzed. The results of risk prediction are compared between the realized NGARCH model and the realized GARCH model, and the results show that, The realized GARCH model overestimates the market risk compared with the realized NGARCH model. The realized NGARCH model proposed in this paper provides a new method for financial risk management and enriches the theory of financial risk management to a certain extent.
【學位授予單位】：重慶理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F224;F832.51

【參考文獻】

相關期刊論文 前10條

1 黃友珀;唐振鵬;周熙雯;;基于偏t分布realized GARCH模型的尾部風險估計[J];系統(tǒng)工程理論與實踐;2015年09期

2 王玲;;Realized GARCH模型在黃金期貨市場的應用[J];財務與金融;2015年03期

3 黃金山;陳敏;;基于高頻數(shù)據(jù)的GARCH模型的偽極大指數(shù)似然估計[J];應用數(shù)學學報;2014年06期

4 劉威儀;;市場微觀結構噪聲的形成機制研究[J];金融理論與實踐;2014年07期

5 袁周波;;基于R-GARCH模型與HAR模型預測能力比較研究[J];時代金融;2012年30期

6 葉緒國;杜雪樵;;高頻金融數(shù)據(jù)中市場微觀結構噪音誤差估計[J];大學數(shù)學;2012年05期

7 唐勇;寇貴明;;股票市場微觀結構噪聲、跳躍、流動性關系分析[J];中國管理科學;2012年02期

8 李勝歌;張世英;;金融高頻數(shù)據(jù)的最優(yōu)抽樣頻率研究[J];管理學報;2008年06期

9 張偉;李平;曾勇;;中國股票市場個股已實現(xiàn)波動率估計[J];管理學報;2008年02期

10 段琳琳;屠新曙;;已實現(xiàn)波動率在我國金融市場的應用前景[J];統(tǒng)計與決策;2005年24期

相關博士學位論文 前1條

1 黃金山;基于高頻數(shù)據(jù)的GARCH模型的參數(shù)估計[D];中國科學技術大學;2013年

，

本文編號：1597360