本文關(guān)鍵詞：基于高頻金融數(shù)據(jù)的已實(shí)現(xiàn)賦權(quán)中位數(shù)方法及其應(yīng)用　出處：《重慶理工大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 中位數(shù)已實(shí)現(xiàn)波動(dòng)率(MedRV) 已實(shí)現(xiàn)賦權(quán)中位數(shù)波動(dòng)率(WMedRV) 分?jǐn)?shù)階自回歸移動(dòng)平均模型(ARFIMA)

【摘要】：金融市場(chǎng)信息連續(xù)影響著證券市場(chǎng)價(jià)格的運(yùn)動(dòng)過(guò)程,因此數(shù)據(jù)采集的離散程度會(huì)導(dǎo)致信息丟失的程度。但隨著存儲(chǔ)和計(jì)算技術(shù)的快速發(fā)展,使采集市場(chǎng)實(shí)時(shí)交易的飽含豐富信息的高頻數(shù)據(jù)成為可能,所以對(duì)高頻數(shù)據(jù)的度量與應(yīng)用逐漸成為金融分析領(lǐng)域的重要趨勢(shì)之一。金融資產(chǎn)收益波動(dòng)率的度量是風(fēng)險(xiǎn)評(píng)估的核心,它直接決定著對(duì)風(fēng)險(xiǎn)管理的有效識(shí)別。常見(jiàn)的風(fēng)險(xiǎn)度量方法是在值風(fēng)險(xiǎn)法(VaR),且計(jì)算在值風(fēng)險(xiǎn)(VaR)的模型也在不斷完善,國(guó)內(nèi)外學(xué)者對(duì)已有的在值風(fēng)險(xiǎn)法(VaR)博采眾長(zhǎng),先后提出各種風(fēng)險(xiǎn)度量模型。本文旨在：通過(guò)理論分析尋找較優(yōu)的風(fēng)險(xiǎn)度量模型；比較我國(guó)深市和滬市兩個(gè)證券市場(chǎng)間的異同，，其創(chuàng)新點(diǎn)如下： 結(jié)合“已實(shí)現(xiàn)波動(dòng)率(RV)、已實(shí)現(xiàn)二次冪變差(BV)、已實(shí)現(xiàn)三次冪變差(TV)、已實(shí)現(xiàn)中位數(shù)波動(dòng)率(MedRV)”理論,提出了包含已實(shí)現(xiàn)中位數(shù)波動(dòng)率(MedRV)和已實(shí)現(xiàn)賦權(quán)波動(dòng)率(RRV)的已實(shí)現(xiàn)賦權(quán)中位數(shù)波動(dòng)率(WMedRV),并證明了該波動(dòng)率的的無(wú)偏性和最小方差性;通過(guò)比較不同波動(dòng)率的描述統(tǒng)計(jì)量,證明了該波動(dòng)率具有良好的穩(wěn)定性。最后將不同的已實(shí)現(xiàn)波動(dòng)率度量方法應(yīng)用于我國(guó)兩個(gè)證券市場(chǎng)的VaR值計(jì)算。 另外，為了能夠通過(guò)對(duì)比來(lái)觀察已實(shí)現(xiàn)賦權(quán)中位數(shù)波動(dòng)率(WMedRV)方法的有效性,本文設(shè)計(jì)了經(jīng)典的VaR方法在我國(guó)兩個(gè)證券市場(chǎng)中的應(yīng)用。
[Abstract]:Financial market information continuously affects the movement of the stock market price, so the discrete degree of data acquisition will lead to the degree of information loss. However, with the rapid development of storage and computing technology. It is possible to collect high-frequency data with rich information for real-time trading in the market. Therefore, the measurement and application of high-frequency data has gradually become one of the important trends in the field of financial analysis. The measurement of volatility of financial assets returns is the core of risk assessment. It directly determines the effective identification of risk management. Domestic and foreign scholars have put forward a variety of risk measurement models. The purpose of this paper is to find a better risk measurement model through theoretical analysis; Comparing the similarities and differences between Shenzhen and Shanghai stock markets, the innovations are as follows: Based on the theory of "realized fluctuation rate (RV), second power variation difference (BV), third power variation difference (TVV) and median fluctuation rate (MedRV)". The realized weighted median volatility rate (WMedRV) including realized median volatility (MedRV) and realized weighted volatility (RRV) is proposed. The unbias and minimum variance of the volatility are proved. It is proved that the volatility has good stability by comparing the description statistics of different volatility. Finally, different realized volatility measurement methods are applied to the calculation of VaR value in two stock markets in China. In addition, in order to observe the effectiveness of the weighted median volatility (WMedRV) method by comparison, this paper designs the application of the classical VaR method in two securities markets in China.
【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】：2014
【分類號(hào)】：F830.9;F224

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本文編號(hào)：1372598