本文選題：VaR + 高頻波動(dòng)率��； 參考：《重慶大學(xué)》2013年碩士論文

【摘要】：金融市場(chǎng)的風(fēng)險(xiǎn)度量一直是學(xué)術(shù)界和風(fēng)險(xiǎn)監(jiān)管當(dāng)局關(guān)注的重點(diǎn)。傳統(tǒng)的風(fēng)險(xiǎn)度量大多數(shù)都是基于低頻日間數(shù)據(jù)建立的GARCH類模型或SV類模型。雖然這些模型本身能較好的度量時(shí)間序列的波動(dòng)狀況，但股市日內(nèi)交易頻繁，低頻數(shù)據(jù)模型會(huì)損失大量的日內(nèi)重要信息。現(xiàn)有研究表明，傳統(tǒng)的GARCH類模型并不能直接用于估計(jì)高頻波動(dòng)率。建立有效的高頻數(shù)據(jù)風(fēng)險(xiǎn)度量模型，為金融機(jī)構(gòu)和監(jiān)管當(dāng)局的風(fēng)險(xiǎn)監(jiān)控提供一種有效的理論方法參考和政策建議具有重大意義。 本文結(jié)合前人對(duì)已實(shí)現(xiàn)類高頻波動(dòng)率的研究，對(duì)已實(shí)現(xiàn)波動(dòng)率RV、已實(shí)現(xiàn)雙冪次波動(dòng)率RBV和賦權(quán)已實(shí)現(xiàn)雙冪次波動(dòng)率WRBV進(jìn)行比較，針對(duì)WRBV具有的長(zhǎng)記憶性，建立了ARFIMA-WRBV-VaR模型對(duì)中國(guó)股市風(fēng)險(xiǎn)進(jìn)行度量，，并與采用低頻日間收益率序列建立的GARCH類模型相比較。 實(shí)證結(jié)果表明：ARFIMA-WRBV-VaR模型比EGARCH-VaR模型估計(jì)效果更好。而且，已實(shí)現(xiàn)類高頻波動(dòng)率出現(xiàn)了跳躍點(diǎn)、日內(nèi)U型周期性日歷效應(yīng)和長(zhǎng)記憶性特征，這些特征受市場(chǎng)微觀結(jié)構(gòu)中的信息不對(duì)稱和投資者心理等因素影響。進(jìn)而為風(fēng)險(xiǎn)監(jiān)控提出了完善信息披露機(jī)制和增強(qiáng)投資者素質(zhì)的政策建議。
[Abstract]:Financial market risk measurement has been the focus of academic and risk regulatory authorities. Most of the traditional risk measures are GARCH models or SV models based on low frequency day data. Although these models themselves can better measure the volatility of time series, the intraday trading of stock market is frequent, and the low-frequency data model will lose a lot of important information. Existing studies show that the traditional GARCH model can not be directly used to estimate high frequency volatility. It is of great significance to establish an effective risk measurement model of high frequency data and to provide an effective theoretical and methodological reference and policy advice for the risk monitoring of financial institutions and regulatory authorities. In this paper, we compare the realized volatility RV, the realized double power volatility RBV and the weighted double power volatility rate WRBV with the previous researches on the realized high frequency volatility, aiming at the long memory property of WRBV. The ARFIMA-WRBV-VaR model is established to measure the Chinese stock market risk and compared with the GARCH model based on the low-frequency daytime yield series. The empirical results show that the ratio ARFIMA-WRBV-VaR model is more effective than EGARCH-VaR model. Moreover, the realized high frequency volatility shows jumping points, intraday U-type periodic calendar effects and long memory characteristics, which are influenced by the information asymmetry in the market microstructure and investor psychology. Then it puts forward some policy suggestions to improve the information disclosure mechanism and enhance the quality of investors for risk monitoring.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F224;F832.51

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