本文選題：CVaR + CES　； 參考：《廣西師范大學(xué)》2015年碩士論文

【摘要】：最近三十年,受到經(jīng)濟全球化、信息技術(shù)以及金融理論等因素的影響,全球金融市場得到了迅速發(fā)展。這使得全球的金融市場變得更加開放,全球范圍內(nèi)的資本流通速度加快而且更加自由化。在全球金融市場中不同風(fēng)險特性的資本得到重新配置和組合,這導(dǎo)致全球金融市場的運作方式和風(fēng)險的表現(xiàn)產(chǎn)生了很大程度的改變,因而金融市場出現(xiàn)了前所未有過的波動。與此同時,金融機構(gòu)為了規(guī)避金融風(fēng)險,提升市場競爭力,開展了一系列的金融創(chuàng)新活動,在技術(shù)進步和管制放松的刺激情況下,這些活動就顯得異�；钴S[2]。隨著全球經(jīng)濟的快速發(fā)展,金融全球化、自由化逐漸加強。隨之而產(chǎn)生的市場風(fēng)險也成為人們的關(guān)注熱點,全球范圍內(nèi)的專家學(xué)者都對其展開了研究。怎樣去量化市場風(fēng)險,也即怎樣去測定市場風(fēng)險,就成為擺在我們面前急需解決的一個大問題。標準差(Standard Deviation)、絕對離差(Absolute Deviation、偏差(Deviation)、下端部分矩(Lower Partial Moment)、風(fēng)險價值VaR、條件風(fēng)險價值CVaR、條件期望損失CES等度量方法是目前正在使用或已經(jīng)被提出來作為度量風(fēng)險的主要工具。其中,由于巴塞爾委員會對于VaR的認可,VaR開始受到全球金融分析方面專家的青睞,被選用作為金融機構(gòu)風(fēng)險管理的國際統(tǒng)一標準。隨著VaR模型及其計算方法的不斷發(fā)展和優(yōu)化,以及Artzner在1999年初次提出一致性公理后,將VaR作為風(fēng)險度量的標準遭到了質(zhì)疑,因為有研究者在理論和實證分析這兩方面都證實了VaR對于次可加性并不滿足,因而得出VaR不是一致性風(fēng)險度量的結(jié)論[3]。在此情況下,人們?yōu)榱藦浹aVaR的不足,于是就開始構(gòu)造和設(shè)計一個既容易估計和計算又滿足一致性公理的風(fēng)險度量。Rockafellar[5]和Uryasev提出了CVaR;Scaillet[6]提出了CES的非參數(shù)估計；Artzner[7]等提出了最壞條件期望WCE；Acerbi[4]提出了譜風(fēng)險度量等等,并證明了它們既是一致性風(fēng)險度量又能夠方便計算。其中CES和CVaR應(yīng)用的較為廣泛,因為它們相對于VaR度量更有優(yōu)勢。本文介紹了國內(nèi)外對VaR、CES以及CVaR的研究情況,并且簡單介紹了四種窗寬選擇的方法：主觀選擇法、參照標準分布法、經(jīng)驗法則和無偏最小平方交叉實證法。在模擬研究中,窗寬通過經(jīng)驗法則選出,使用R軟件編程,生成隨機數(shù),運用局部線性估計模擬出CVaR和CES的估計值,列出不同分位數(shù)p下兩者的數(shù)值,并進行相應(yīng)的比較,分析CVaR和CES的變化趨勢。然后,以深成指數(shù)和上證指數(shù)為研究樣本,用Eviews軟件畫出兩只股票的日收益率圖像,同時對股票日收益率進行ADF檢驗。從ADF檢驗和日收益率圖像可以得出,深成指數(shù)序列和上證指數(shù)日收益率序列都是平穩(wěn)的。然后運用模擬中的局部線性估計對條件風(fēng)險價值CVaR和條件期望損失CES進行估計,計算出股票市場數(shù)據(jù)的(:VaR和CES值,統(tǒng)計分析出超過真實VaR的百分比,以此來驗證模擬中的結(jié)論。
[Abstract]:In the last three decades, the global financial market has developed rapidly under the influence of economic globalization, information technology and financial theory. This makes global financial markets more open, global capital flows faster and more liberalized. In the global financial market, the capital with different risk characteristics has been reconfigured and combined, which has led to a great change in the operation mode and risk performance of the global financial market, resulting in unprecedented fluctuations in the financial market. At the same time, in order to avoid financial risks and enhance market competitiveness, financial institutions have launched a series of financial innovation activities. With the rapid development of the global economy, financial globalization and liberalization are gradually strengthened. The market risk has become the focus of attention, which has been studied by experts and scholars all over the world. How to quantify the market risk, that is, how to measure the market risk, has become a big problem that needs to be solved in front of us. Standard deviation, absolute deviation, lower Partial moment, risk value, conditional expectation loss, CES and so on, are currently used or proposed as the main tools to measure risk. Among them, due to the Basel Committee's recognition of VaR began to be favored by experts in the field of global financial analysis, it is chosen as the international standard of risk management of financial institutions. With the continuous development and optimization of the VaR model and its calculation methods, and after Artzner first proposed the consistency axiom in 1999, the use of VaR as the criterion of risk measurement has been questioned. Because some researchers have proved that VaR is not satisfied with subadditivity in both theoretical and empirical analysis, it is concluded that VaR is not a consistent risk measure [3]. In this case, in order to make up for the deficiencies of VaR, So we began to construct and design a risk measure which is easy to estimate and calculate and satisfy the consistency axiom. Rockafellar [5] and Uryasev proposed Cvar Rn scale [6], and CES's nonparametric estimation Artzner [7], and so on, and proposed the worst-case expectation WCEE _ Cer _ bi [4] and proposed spectral risk measurement, and so on. It is proved that they are not only consistent risk measurement but also easy to calculate. CES and CVaR are widely used because they have more advantages than VaR metrics. This paper introduces the domestic and foreign research on CVaR and CVaR, and briefly introduces four methods of window width selection: subjective selection method, reference standard distribution method, empirical rule and unbiased least square cross empirical method. In the simulation study, the window width is selected by the rule of thumb, the random number is generated by using R software, the local linear estimation is used to simulate the estimated values of CVaR and CES, and the values under different quantiles p are listed and compared accordingly. Analyze the change trend of CVaR and CES. Then, using Shencheng Index and Shanghai Stock Exchange Index as the research samples, the daily yield image of two stocks is drawn by Eviews software, and the ADF test of the daily return rate of stock is carried out at the same time. From the ADF test and the daily yield image, it can be concluded that both the Shenzhen exponent series and the Shanghai stock index daily yield series are stable. Then the local linear estimator is used to estimate the conditional risk value (CVaR) and the conditional expectation loss (CES). The ratio of CES and CES of the stock market data is calculated and the percentage above the real VaR is statistically analyzed to verify the conclusion in the simulation.
【學(xué)位授予單位】：廣西師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：F224;F830

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