本文選題：高頻　切入點(diǎn)：波動(dòng)率預(yù)測(cè)　出處：《復(fù)旦大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：伴隨技術(shù)進(jìn)步,高頻金融應(yīng)運(yùn)而生。高頻交易逐漸增加的同時(shí),面臨著低頻模型失效、高頻數(shù)據(jù)噪音、交易時(shí)效性等問題。高頻波動(dòng)率的表示、分析和預(yù)測(cè)是解決上述問題的關(guān)鍵,也是研究的著眼點(diǎn)。本文是基于高頻波動(dòng)率代理變量、相關(guān)預(yù)測(cè)模型的比較研究。本文采用滬深300股指期貨的逐筆數(shù)據(jù),以5分鐘已實(shí)現(xiàn)波動(dòng)率為代理變量,利用多種損失函數(shù)比較了不同預(yù)測(cè)方法的預(yù)測(cè)效率。分析過程引入VPIN作為外部信息,以降低微觀結(jié)構(gòu)噪音的影響；同時(shí)使用HAR-VPIN模型檢驗(yàn)了VPIN的預(yù)測(cè)效度,解決了VPIN原有檢驗(yàn)手段不足、預(yù)測(cè)效率受質(zhì)疑的問題。本文基于分析結(jié)果,結(jié)合高頻交易的風(fēng)險(xiǎn)管理實(shí)踐,提出高頻波動(dòng)率預(yù)測(cè)模型的應(yīng)用場(chǎng)景,分析高頻波動(dòng)率的應(yīng)用成果。研究的主要結(jié)論是：1 VPIN的計(jì)算穩(wěn)健性在高頻預(yù)測(cè)研究中可控。通過不同籃子數(shù)量、起始點(diǎn),以及買賣方向打標(biāo)算法等參數(shù)分析VPIN對(duì)參數(shù)的敏感性,發(fā)現(xiàn)質(zhì)疑研究的主要錯(cuò)誤在于打標(biāo)算法和時(shí)間框架的應(yīng)用錯(cuò)誤。HAR-VPIN回歸模型表明,VPIN能夠解釋成交量信息,是波動(dòng)率的主要驅(qū)動(dòng)因子。2利用高頻數(shù)據(jù)計(jì)算已實(shí)現(xiàn)波動(dòng)率是較好的方法。比較幾種高頻波動(dòng)率代理變量,建議使用5分鐘作為波動(dòng)率預(yù)測(cè)的樣本區(qū)間,既避免了更高頻率的微觀結(jié)構(gòu)噪音,又避免了更低頻率的信息時(shí)效性損失。3HAR-VPIN模型在絕大多數(shù)損失函數(shù)下預(yù)測(cè)能力較強(qiáng)。比較各種預(yù)測(cè)模型發(fā)現(xiàn),HAR-VPIN模型由于包括了其他模型所不具備的“外部信息”,預(yù)測(cè)誤差相對(duì)較低,僅在部分損失函數(shù)度量下弱于IGARCH模型。
[Abstract]:With the development of technology, high frequency finance emerges as the times require. At the same time, the high frequency trading is gradually increasing, and it faces the problems of low frequency model failure, high frequency data noise, transaction timeliness and so on. Analysis and prediction are the key to solve the above problems, and are also the focus of the research. This paper is based on high-frequency volatility proxy variables, the comparative study of relevant forecasting models. This paper adopts the data of Shanghai and Shenzhen 300 stock index futures. The prediction efficiency of different prediction methods is compared by using a variety of loss functions using 5 minutes' realized volatility as a proxy variable. VPIN is introduced as the external information in the analysis process to reduce the influence of microstructural noise. At the same time, the HAR-VPIN model is used to test the prediction validity of VPIN, which solves the problem that the original test means of VPIN are insufficient and the forecasting efficiency is questioned. Based on the analysis results, this paper combines the risk management practice of high frequency trading. The application scenario of high frequency volatility prediction model is put forward, and the application results of high frequency volatility are analyzed. The main conclusion of the study is that the calculation robustness of 1: 1 VPIN is controllable in the high frequency prediction research. After analyzing the sensitivity of VPIN to parameters, we find that the main error of the research is the error of marking algorithm and time frame. HAR-VPIN regression model shows that VPIN can interpret the information of trading volume. It is a good method to calculate realized volatility by using high frequency data. Comparing several kinds of proxy variables of high frequency volatility, it is suggested to use 5 minutes as sample interval for volatility prediction. To avoid the higher frequency of microstructural noise, It also avoids the loss of information timeliness of lower frequency. 3HAR-VPIN model has strong prediction ability under most loss functions. Comparing various prediction models, it is found that the HAR-VPIN model includes "external information" that other models do not have. The measurement error is relatively low, It is weaker than IGARCH model only in partial loss function metric.
【學(xué)位授予單位】：復(fù)旦大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：F832.51

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 唐勇;張世英;;高頻數(shù)據(jù)的加權(quán)已實(shí)現(xiàn)極差波動(dòng)及其實(shí)證分析[J];系統(tǒng)工程;2006年08期

，

本文編號(hào)：1625707