本文選題：歐債危機(jī) + 風(fēng)險(xiǎn)管理��； 參考：《東北財(cái)經(jīng)大學(xué)》2012年碩士論文

【摘要】：2011年由于歐債危機(jī)的持續(xù)爆發(fā),歐元美元作為外匯市場(chǎng)主要的貨幣對(duì)因此大受影響,對(duì)于以歐元美元為代表的外匯市場(chǎng)的風(fēng)險(xiǎn)管理技術(shù)有必要進(jìn)行深入研究。交易者在積極參與外匯市場(chǎng)交易之外,必要的風(fēng)險(xiǎn)管理控制是必須的,同時(shí)辨識(shí)及測(cè)量金融風(fēng)險(xiǎn)也已成為金融機(jī)構(gòu)和監(jiān)管部門關(guān)注的焦點(diǎn)。目前,在眾多風(fēng)險(xiǎn)測(cè)量工具中,VaR模型已成為各金融機(jī)構(gòu)測(cè)量市場(chǎng)風(fēng)險(xiǎn)的重要工具。 資產(chǎn)收益率的概率分布具有尖峰厚尾特征并具有波動(dòng)集聚性,但一般資產(chǎn)收益率都假設(shè)正態(tài)分布,而VaR模型預(yù)測(cè)的準(zhǔn)確度與是否能反映收益率尖峰厚尾的特性是分不開(kāi)的。GARCH模型在金融領(lǐng)域有著廣泛的應(yīng)用,運(yùn)用GARCH模型進(jìn)行資產(chǎn)組合的選擇和風(fēng)險(xiǎn)管理,已經(jīng)在金融市場(chǎng)中得到了廣泛的重視。GARCH-VaR模型是進(jìn)行外匯風(fēng)險(xiǎn)管理的重要工具,但是殘差分布的不同對(duì)模型風(fēng)險(xiǎn)測(cè)量的結(jié)果會(huì)產(chǎn)生顯著差異。通過(guò)GED廣義誤差分布來(lái)代替標(biāo)準(zhǔn)正態(tài)分布運(yùn)用,將GARCH模型和GED分布結(jié)合以運(yùn)用VaR方法可以很好地選擇資產(chǎn)組合并進(jìn)行風(fēng)險(xiǎn)管理。這相對(duì)于僅對(duì)VaR方法進(jìn)行簡(jiǎn)單的應(yīng)用來(lái)說(shuō),提高了模型的精確度和適用性。 本文以歐元兌美元匯率為例,比較了殘差分布為正態(tài)分布、t分布和GED分布的GARCH-VaR模型。具體的結(jié)構(gòu)為： 第一章為本文的緒論,主要闡明本文的選題背景、研究意義和現(xiàn)階段國(guó)內(nèi)外研究現(xiàn)狀,并指出本文的創(chuàng)新與不足。 第二章主要介紹VaR方的原理與各種計(jì)算方法,為后文做前述準(zhǔn)備。 第三章主要是結(jié)合GARCH模型,介紹了各種基于不同殘差分布下的GARCH-VaR模型,并介紹了模型的參數(shù)估計(jì)和VaR回測(cè)檢驗(yàn)。 第四章為本文的論證核心,通過(guò)對(duì)歐元美元匯率數(shù)據(jù)進(jìn)行序列特征分析,驗(yàn)證了收益率序列的平穩(wěn)性檢驗(yàn)和ARCH效應(yīng)。在此基礎(chǔ)上通過(guò)比較模型的樣本外預(yù)測(cè)結(jié)果,直觀上比較了殘差分布為正態(tài)分布、t分布和GED分布的GARCH-VaR模型在風(fēng)險(xiǎn)管理上的優(yōu)劣。同時(shí)給出了模型的回測(cè)檢驗(yàn)結(jié)果以證明模型的論證結(jié)果是合正確的。 本文第五章綜合前四章所述,給出本文的研究結(jié)論。 本文經(jīng)過(guò)比較分析發(fā)現(xiàn)：在95%置信水平下,殘差分布為正態(tài)分布下的模型的失敗率過(guò)高,說(shuō)明正態(tài)分布下的VaR低估了匯率波動(dòng)的風(fēng)險(xiǎn)；在99%置信水平下殘差分布為t分布的模型失敗率明顯不合理,說(shuō)明t分布下的VaR值高估了外匯波動(dòng)風(fēng)險(xiǎn)。相比之下,殘差分布為GED分布的GARCH模的VaR預(yù)測(cè)在95%和99%置信水平下表現(xiàn)都比較穩(wěn)定,尤其在更高置信水平下表現(xiàn)更加穩(wěn)定,對(duì)于尖峰厚尾特征明顯的外匯市場(chǎng)收益率的風(fēng)險(xiǎn)管理具有指導(dǎo)意義。 本文的創(chuàng)新之處在于： (1)相對(duì)于傳統(tǒng)的VaR,本文的模型將資產(chǎn)收益的尖峰厚尾特征考慮進(jìn)去,并針對(duì)其特點(diǎn)采用GARCH-GED函數(shù)來(lái)描述。 (2)在實(shí)證分析時(shí),本文并不僅僅應(yīng)用GARCH-GED函數(shù),而是將其與GARCH-N函數(shù)、GARCH-t函數(shù)所得VaR分別與實(shí)際樣本外數(shù)據(jù)進(jìn)行比較分析,更具有說(shuō)服力。 (3)在研究GARCH-GED等模型之前,本文對(duì)EGARCH模型也進(jìn)行了研究,并通過(guò)實(shí)證分析否定了外匯收益率存在不對(duì)稱性。同時(shí)針對(duì)這一情況給出自己的解釋。 當(dāng)然由于本人的水平有限,本文還有不少不足： (1)應(yīng)用GARCH模型進(jìn)行波動(dòng)率預(yù)測(cè)時(shí),隱含了一個(gè)假設(shè)條件即假設(shè)歷史會(huì)重演,歷史數(shù)據(jù)可以反映未來(lái)。但相對(duì)于壓力測(cè)試、情景模擬方法,這一假設(shè)使得在歷史數(shù)據(jù)的基礎(chǔ)上獲得的未來(lái)預(yù)測(cè)效果受到局限。 (2)本文GARCH模型進(jìn)行參數(shù)估計(jì)時(shí)只是采用局部最優(yōu)化的方法,而非 是更為精確的全局最優(yōu)化方法,例如模擬退火方法。 (3)本文在進(jìn)行三個(gè)模型進(jìn)行比較時(shí)沒(méi)有考慮VaR模型的歷史模擬法和蒙特卡洛隨機(jī)模擬方法,如果將其也納入比較中將更能說(shuō)明問(wèn)題。同時(shí)還有一個(gè)實(shí)證分析數(shù)據(jù)選取上的不足,即由于外匯市場(chǎng)每日發(fā)布許多可以左右市場(chǎng)情一緒的市場(chǎng)消息和經(jīng)濟(jì)數(shù)據(jù),外匯數(shù)據(jù)以每日數(shù)據(jù)為依據(jù)大大降低了市場(chǎng)波動(dòng)的真實(shí)性,可以考慮將每日數(shù)據(jù)改為高頻數(shù)據(jù)進(jìn)行研究。
[Abstract]:As a result of the sustained outbreak of the European debt crisis in 2011, the euro dollar as the main currency of the foreign exchange market has been greatly affected. It is necessary to study the risk management technology of the foreign exchange market represented by the euro dollar. Identification and measurement of financial risks have also become the focus of financial institutions and regulatory authorities. At present, in many risk measurement tools, the VaR model has become an important tool for the financial institutions to measure the market risk.
The probability distribution of the rate of return on assets has the characteristics of peak and thick tail and volatility clustering, but the general return on assets assumes normal distribution, and the accuracy of the VaR model and the characteristic that can reflect the thick tail of the yield peak are inseparable from the.GARCH model in the financial field, and the asset group is carried out by using the GARCH model. The combination selection and risk management have gained extensive attention in the financial market. The.GARCH-VaR model is an important tool for the management of foreign exchange risk. However, the difference in residual distribution will produce significant differences in the results of the model risk measurement. The GED generalized error distribution is used to replace the standard normal distribution, and the GARCH model and GED are divided. The combination of cloth to use the VaR method can make a good choice of portfolio and risk management. This improves the accuracy and applicability of the model compared to the simple application of the VaR method only.
In this paper, the euro dollar exchange rate as an example, the residual distribution is normal distribution, t distribution and GED distribution model. The specific structure for GARCH-VaR:
The first chapter is the introduction, mainly expounds the research background, research significance and research status at home and abroad, and points out the innovation and shortcomings of this paper.
The second chapter mainly introduces the principle and calculation method of VaR, do the preparation for the later.
The third chapter is mainly based on the GARCH model, introduced a variety of different GARCH-VaR models based on the distribution of the residuals, and introduces the parameter estimation and VaR model of back testing.
The fourth chapter is the core of the argument. Through the analysis of the sequence characteristics of the exchange rate data of the euro dollar, the stability test and ARCH effect of the yield sequence are verified. On this basis, the GARCH-VaR model of the residual distribution as the normal distribution, the t distribution and the GED distribution is intuitively compared by comparing the sample prediction results of the model. The management of quality. At the same time gives the model of back testing results to prove the model is correct. The results demonstrate
In the fifth chapter, comprehensive chapter four the conclusions of this study are given.
Through the comparison and analysis, we find that the failure rate of the model under the normal distribution under the 95% confidence level is too high, which indicates that the VaR under the normal distribution underestimates the risk of exchange rate fluctuation; the failure rate of the model under the 99% confidence level is obviously unreasonable, which indicates that the VaR value under the t distribution overestimates the Foreign Exchange Volatility wind. In contrast, the VaR prediction of the GARCH model with the residual distribution of GED distribution is more stable at 95% and 99% confidence levels, especially at a higher confidence level, which is of guiding significance for the risk management of the foreign exchange rate of sharp peak and thick tail.
The innovation of this paper lies in:
(1) compared with the traditional VaR, this model will be leptokurtic features of asset returns into account, and aiming at the characteristics of the GARCH-GED function to describe.
(2) in the case of empirical analysis, this paper does not only apply the GARCH-GED function, but compares it with the GARCH-N function, the GARCH-t function and the actual data from the actual sample, and is more convincing.
(3) before studying GARCH-GED and other models, this paper also studies the EGARCH model, and denies the asymmetry of foreign exchange rate by empirical analysis. At the same time, we give an explanation for this situation.
Of course, due to my limited level, there are many deficiencies in this paper:
(1) when using the GARCH model to predict the volatility, a hypothesis is implied that the history will replay and the historical data can reflect the future. But relative to the pressure test, the scenario simulation method makes the future prediction results Limited on the basis of historical data.
(2) only by using the method of local optimization in the GARCH model to estimate the parameters, rather than
Is a more accurate global optimization methods, such as simulated annealing method.
(3) in the comparison of the three models, this paper does not consider the historical simulation method of VaR model and the Monte Carlo stochastic simulation method, if it is also included in the comparison, it will be more able to explain the problem. At the same time, there is a lack of empirical analysis data selection, that is, because the foreign exchange market will publish many markets that can be around the market situation every day. Field news and economic data, foreign exchange data, based on daily data, greatly reduce the authenticity of market volatility, and can consider changing daily data to high frequency data for research.
【學(xué)位授予單位】：東北財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：F831.6;F224

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