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

【摘要】：近年來(lái),隨著電子化交易在金融市場(chǎng)的廣泛應(yīng)用以及信息技術(shù)的迅猛發(fā)展,金融市場(chǎng)的波動(dòng)性也日趨激烈。如何更精確地預(yù)測(cè)資產(chǎn)的收益風(fēng)險(xiǎn)引起了人們的高度重視。估計(jì)資產(chǎn)收益的波動(dòng)率是預(yù)測(cè)收益風(fēng)險(xiǎn)的關(guān)鍵問題之一,而波動(dòng)率的估計(jì)精度又與模型的假設(shè)及數(shù)據(jù)的采集頻率密切相關(guān)。一般而言,模型的波動(dòng)率估計(jì)值越精確以及所使用的數(shù)據(jù)頻率越高,波動(dòng)率的估計(jì)精度就越好。因此,以高頻金融數(shù)據(jù)為研究對(duì)象,如何建立一個(gè)具有統(tǒng)計(jì)優(yōu)良性的波動(dòng)率模型是本文的主要研究目標(biāo)。本文的主要研究?jī)?nèi)容及創(chuàng)新點(diǎn)如下:1.基于NGARCH模型刻畫了波動(dòng)率的杠桿效應(yīng)特征,本文在已實(shí)現(xiàn)GARCH模型的波動(dòng)率方程中引入?yún)?shù)的擾動(dòng),提出了已實(shí)現(xiàn)NGARCH模型。在新模型中,引入的參數(shù)與誤差項(xiàng)序列成負(fù)相關(guān)關(guān)系,使得新息既在大小上對(duì)當(dāng)前收益作出擾動(dòng),又在方向上對(duì)當(dāng)前收益作出擾動(dòng)。2.鑒于模型的參數(shù)估計(jì)精度直接影響風(fēng)險(xiǎn)預(yù)測(cè)的準(zhǔn)確性,本文采用蒙特卡羅方法對(duì)提出的已實(shí)現(xiàn)NGARCH模型的參數(shù)估計(jì)的穩(wěn)健性進(jìn)行檢驗(yàn)。隨機(jī)模擬結(jié)果顯示,在%5的顯著性水平下,所有參數(shù)估計(jì)值的均方誤差均顯著。同時(shí),當(dāng)設(shè)定模擬次數(shù)為500次時(shí),隨著樣本量的增大,所有參數(shù)的估計(jì)值依然顯著。模擬結(jié)果表明,本文提出的已實(shí)現(xiàn)NGARCH模型的波動(dòng)率估計(jì)方法有較好的穩(wěn)健性。3.基于文中提出的已實(shí)現(xiàn)NGARCH模型對(duì)上證50指數(shù)和上證380指數(shù)5min頻率的高頻數(shù)據(jù)進(jìn)行了實(shí)證分析,并對(duì)其風(fēng)險(xiǎn)預(yù)測(cè)結(jié)果進(jìn)行了比率檢驗(yàn)。其次,對(duì)已實(shí)現(xiàn)NGARCH模型和已實(shí)現(xiàn)GARCH模型的風(fēng)險(xiǎn)預(yù)測(cè)結(jié)果進(jìn)行了比較,結(jié)果表明,已實(shí)現(xiàn)GARCH模型比已實(shí)現(xiàn)NGARCH模型高估了市場(chǎng)風(fēng)險(xiǎn)。本文提出的已實(shí)現(xiàn)NGARCH模型,為金融風(fēng)險(xiǎn)管理提供了新的方法,在一定程度上豐富了金融風(fēng)險(xiǎn)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(xué)位授予單位】：重慶理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F224;F832.51

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