本文關(guān)鍵詞： 可轉(zhuǎn)換債券 GARCH族模型 VaR　出處：《首都經(jīng)濟(jì)貿(mào)易大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：可轉(zhuǎn)換公司債券以其兼具債性和股性的特殊結(jié)構(gòu)優(yōu)勢(shì)越來(lái)越受到投資者歡迎,“向下債券保底,向上收益可期”的美譽(yù)也被市場(chǎng)中絕大多數(shù)的投資者所認(rèn)可。然而,這種特殊的結(jié)構(gòu)也使得可轉(zhuǎn)債面臨諸多復(fù)雜的風(fēng)險(xiǎn)因素,這些風(fēng)險(xiǎn)相互影響進(jìn)而使得可轉(zhuǎn)債的風(fēng)險(xiǎn)度量工作難度加大�；诖�,本文以我國(guó)可轉(zhuǎn)債市場(chǎng)為研究對(duì)象,利用市場(chǎng)的日交易數(shù)據(jù)和數(shù)學(xué)模型探索我國(guó)可轉(zhuǎn)債市場(chǎng)的風(fēng)險(xiǎn)度量模型,在此基礎(chǔ)上試圖探尋我國(guó)可轉(zhuǎn)債市場(chǎng)的總體風(fēng)險(xiǎn)水平,并對(duì)可轉(zhuǎn)債和可交換債的風(fēng)險(xiǎn)特點(diǎn)進(jìn)行研究。本論文共分為六章,前兩章在介紹本文研究背景和意義的基礎(chǔ)上利用收集到的數(shù)據(jù)對(duì)我國(guó)可轉(zhuǎn)債的一級(jí)和二級(jí)市場(chǎng)特點(diǎn)進(jìn)行總結(jié),分析了可轉(zhuǎn)債投資所面臨的復(fù)雜投資風(fēng)險(xiǎn)并對(duì)重點(diǎn)風(fēng)險(xiǎn)進(jìn)行重點(diǎn)介紹,明確了可轉(zhuǎn)債風(fēng)險(xiǎn)度量工作的重要意義。第三章引入VaR模型,對(duì)VaR的不同計(jì)算方法進(jìn)行綜述并最終決定利用參數(shù)法來(lái)測(cè)度我國(guó)可轉(zhuǎn)債的風(fēng)險(xiǎn)水平。同時(shí)引入基于不同分布的GARCH族模型,以更加精確的模擬可轉(zhuǎn)債市場(chǎng)波動(dòng)路徑。第四章以我國(guó)中證轉(zhuǎn)債指數(shù)2004年1月2日至2016年12月30日之間共計(jì)3158交易日的收盤數(shù)據(jù)為樣本,在數(shù)據(jù)統(tǒng)計(jì)檢驗(yàn)的基礎(chǔ)上利用基于GARCH族模型的參數(shù)法測(cè)度VaR,并進(jìn)行回測(cè)檢驗(yàn)。最終發(fā)現(xiàn)基于t分布下GARCH族模型均會(huì)對(duì)市場(chǎng)風(fēng)險(xiǎn)高估,基于正態(tài)分布和GED分布下三種模型計(jì)算結(jié)果相近,除GED分布下EGARCH(2,2)模型的預(yù)測(cè)結(jié)果略高于5%之外,其他模型均能夠較好的預(yù)測(cè)中證轉(zhuǎn)債的市場(chǎng)風(fēng)險(xiǎn)。其中,從風(fēng)險(xiǎn)控制與管理角度,GED分布下的TGARCH模型預(yù)測(cè)效果最優(yōu),能夠達(dá)到最優(yōu)的的風(fēng)險(xiǎn)預(yù)測(cè)效果。第五章利用兩組相同評(píng)級(jí)的可轉(zhuǎn)債和可交換債數(shù)據(jù)嘗試分析二者風(fēng)險(xiǎn)水平差異,實(shí)證結(jié)果證明可轉(zhuǎn)債的總體風(fēng)險(xiǎn)水平要小于可交換債,因此投資可交換債更要對(duì)風(fēng)險(xiǎn)進(jìn)行嚴(yán)格管控。最后在全文的研究基礎(chǔ)上,本文進(jìn)行系統(tǒng)總結(jié)并提出文章研究的不足之處。
[Abstract]:Convertible corporate bonds are becoming more and more popular among investors because of their special structural advantages of both debt and stock. The reputation of "keeping the bottom down and earning up" is also recognized by the vast majority of investors in the market. This special structure also makes convertible bonds face a lot of complex risk factors, which influence each other and make it more difficult to measure the risks of convertible bonds. Based on this, this paper takes China's convertible bond market as the research object. Based on the daily transaction data and mathematical model of the market, the paper explores the risk measurement model of China's convertible bond market, and then attempts to explore the overall risk level of China's convertible bond market. This paper is divided into six chapters. The first two chapters summarize the characteristics of the primary and secondary markets of China's convertible bonds on the basis of the background and significance of the research. This paper analyzes the complex investment risks faced by convertible bond investment, introduces the key risks, and clarifies the significance of the risk measurement of convertible bonds. Chapter three introduces the VaR model. This paper summarizes the different calculation methods of VaR and finally decides to use the parameter method to measure the risk level of convertible bonds in China. At the same time, the GARCH family model based on different distributions is introduced. Using a more accurate simulation of the volatility path in the convertible bond market. Chapter 4th is based on the closing data of China's China Securities Exchange Index from January 2nd 2004 to December 30th 2016 for a total of 3,158 trading days. On the basis of statistical test of data, the parameter method based on GARCH family model is used to measure VaR, and the back test is carried out. Finally, it is found that the GARCH family model based on t distribution will overestimate the market risk. Based on normal distribution and GED distribution, the calculation results of the three models are similar. Except for the GED distribution, the prediction results of EGARCHX 2 + 2) model are higher than 5%, and all the other models can better predict the market risk of securities to bonds. From the point of view of risk control and management, the TGARCH model under GED distribution has the best prediction effect and can achieve the optimal risk forecasting effect. Chapter 5th tries to analyze the difference of risk level between the two groups of convertible and exchangeable debt data with the same rating. The empirical results show that the overall risk level of convertible bonds is lower than that of exchangeable bonds, so the investment convertible bonds should be strictly controlled. Finally, on the basis of the research of the full text, this paper makes a systematic summary and puts forward the deficiencies of this paper.
【學(xué)位授予單位】：首都經(jīng)濟(jì)貿(mào)易大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F832.51

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