本文選題：風(fēng)險測度　切入點：一致性公理　出處：《華中師范大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：在經(jīng)濟全球化、金融一體化的背景下,證券市場發(fā)揮著越來越重要的作用,但也不可避免地帶來了極大的風(fēng)險。而新興的中國金融市場,受國外投資策略及國內(nèi)經(jīng)濟環(huán)境的雙重影響,具有其特有的性質(zhì)。因此,研究現(xiàn)有的幾種風(fēng)險測度方法及其在中國證券市場上的適用性非常有必要。本文在現(xiàn)有文獻(xiàn)的研究基礎(chǔ)上,從理論和實證兩個方面對均值-標(biāo)準(zhǔn)差、VaR、CVaR、熵凸風(fēng)險測度、等熵風(fēng)險測度、修正后的熵凸風(fēng)險測度和等熵風(fēng)險測度、HMCR和極小值測度的風(fēng)險識別能力進(jìn)行分析。在理論方面,主要從一致性公理和隨機占優(yōu)一致性以及風(fēng)險測度的凸性、信息容量、風(fēng)險測度值大小方面對幾種風(fēng)險測度的風(fēng)險識別能力進(jìn)行分析。在實證方面,一個角度是采用Spearman秩檢驗法檢驗幾種風(fēng)險測度的風(fēng)險識別能力,另一個角度就是通過組合選擇考察幾種風(fēng)險測度的實際擇股能力。其中,對擇股能力的研究比較重要,分單階段、多階段和不同市場組合進(jìn)行分析。本文的研究樣本為上證50指數(shù)及其成分股收盤價日對數(shù)收益率�？傻靡韵陆Y(jié)論:在理論層面,標(biāo)準(zhǔn)差測度不滿足單調(diào)性,VaR不滿足凸性,熵凸風(fēng)險測度、等熵風(fēng)險測度、HMCR和極小值測度為一致性風(fēng)險測度,滿足凸性的條件。除標(biāo)準(zhǔn)差之外,其它幾種風(fēng)險測度都可以表示為譜風(fēng)險測度的形式,其不同點在于對權(quán)函數(shù)的選擇,HMCR和等熵風(fēng)險測度涵蓋了整個收益率分布的信息量,相應(yīng)的具有較大的風(fēng)險測度值,VaR和CVaR分別僅涵蓋了收益率分布的一個尾部點和一段尾部區(qū)間。標(biāo)準(zhǔn)差不滿足一階隨機占優(yōu)一致性,VaR和CVaR分別滿足一階和二階隨機占優(yōu)一致性,熵凸風(fēng)險測度、等熵風(fēng)險測度和HMCR具有更高階的隨機占優(yōu)一致性。從理論上推測,滿足一致性、凸性、涵蓋越多的信息容量、具有較大的風(fēng)險值及滿足較高階隨機占優(yōu)一致性的風(fēng)險測度與其具有較高的風(fēng)險識別能力是一致的。在實證層面,根據(jù)幾種風(fēng)險測度在不同置信水平下的Spearman秩相關(guān)系數(shù),可知具有較高階隨機占優(yōu)一致性的風(fēng)險測度同樣具有較高的風(fēng)險識別能力。組合選擇的結(jié)果也證明了這一點,即HMCR(p=3)、極小值測度、熵凸風(fēng)險測度、等熵風(fēng)險測度的擇股能力優(yōu)于CVaR、VaR和標(biāo)準(zhǔn)差,當(dāng)然這一結(jié)論受市場組合的影響,并在股市較為穩(wěn)定時比較明顯。在對幾種風(fēng)險測度的擇股能力進(jìn)行研究時,考慮了多階段及不同市場組合下的影響。根據(jù)多階段組合選擇結(jié)果,幾種風(fēng)險測度的組合優(yōu)化能力顯著增高,且多階段組合優(yōu)化過程消除了樣本內(nèi)異常值的影響,使得組合優(yōu)化結(jié)果更符合理論預(yù)期。根據(jù)本文研究的五種市場組合的優(yōu)化結(jié)果,認(rèn)為投資者在根據(jù)樣本內(nèi)組合權(quán)重進(jìn)行投資時,基于幾種風(fēng)險測度的積極型投資策略更適宜預(yù)測股市為熊市的情況,指數(shù)型被動投資策略較適宜預(yù)測股市為牛市或股市波動較大的情況。
[Abstract]:In the context of economic globalization and financial integration, the securities market is playing a more and more important role, but inevitably brings great risks. The dual influence of foreign investment strategy and domestic economic environment has its unique nature. It is necessary to study several existing risk measurement methods and their applicability in China's securities market. Based on the existing literature, this paper makes a theoretical and empirical study on the mean-standard deviation VaRCvar, entropy convex risk measurement. Isentropic risk measure, modified entropy convex risk measure and isentropic risk measure HMCR and minimum measure are analyzed. In theory, the consistency axiom, random dominance consistency and convexity of risk measure are analyzed. In the aspect of information capacity and risk measure value, the paper analyzes the risk identification ability of several kinds of risk measures. In the empirical aspect, the Spearman rank test method is used to test the risk recognition ability of several risk measures. Another angle is to examine the actual stock selection ability of several kinds of risk measures through combination selection. Among them, the research on stock selection ability is more important, which is divided into single stage. The study sample of this paper is the daily logarithmic return rate of Shanghai Stock Exchange 50 Index and its constituent stock closing price. The following conclusions can be drawn: at the theoretical level, the standard deviation measure is not satisfied with monotonic VaR and does not satisfy convexity. Entropy convex risk measure, isentropic risk measure HMCR and minimum measure are consistent risk measures, which satisfy the conditions of convexity. Except for standard deviation, several other risk measures can be expressed as spectral risk measures. The difference is that the selection of weight function and isentropic risk measure cover the information of the whole return distribution. The corresponding risk measure values and CVaR only cover a tail point and a tail interval of the return distribution respectively. The standard deviation does not satisfy the first-order random dominant consistency and the CVaR satisfies the first-order and second-order random dominant consistency, respectively. Entropy convex risk measure, isentropic risk measure and HMCR have higher order random dominant consistency. The risk measure with larger risk value and satisfying higher order random dominant consistency is consistent with its higher risk identification ability. At the empirical level, according to the Spearman rank correlation coefficient of several risk measures at different confidence levels, It can be seen that the risk measure with higher order random dominance consistency also has higher risk identification ability, and the result of combination selection also proves this point, that is, HMCRP p3, minimum measure, entropy convex risk measure. The stock selection ability of Isentropic risk measure is better than that of Cvar VaR and standard deviation. Of course, this conclusion is influenced by the market combination and is obvious when the stock market is relatively stable. The effects of multi-stage and different market combinations are considered. According to the results of multi-stage portfolio selection, the combination optimization ability of several risk measures is improved significantly, and the influence of outliers in samples is eliminated by the multi-stage combinatorial optimization process. According to the optimization results of five market combinations studied in this paper, it is considered that when investors invest according to the weight of the portfolio in the sample, The positive investment strategy based on several risk measures is more suitable to predict the stock market as a bear market, and the exponential passive investment strategy is more suitable to predict the situation that the stock market is a bull market or that the stock market fluctuates greatly.
【學(xué)位授予單位】：華中師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F224;F832.51

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