本文選題：上市國有銀行 + 長期風(fēng)險　； 參考：《遼寧大學(xué)》2017年碩士論文

【摘要】：中國與世界經(jīng)濟(jì)的聯(lián)系越來越緊密,這在促進(jìn)金融業(yè)之間相互交流取長補(bǔ)短的基礎(chǔ)上,也促進(jìn)了國際銀行業(yè)的發(fā)展。但是,隨之而來的風(fēng)險也日趨加劇,尤其是2008年金融危機(jī)以來,給世界金融業(yè)敲響了警鐘。國際金融的自由化和金融的不斷開放使得銀行作為金融體系的核心風(fēng)險不斷加劇,很容易由于某個國家區(qū)域性風(fēng)險引發(fā)整個世界的金融危機(jī)。因此,如何有效的防范和規(guī)避銀行風(fēng)險,并制定策略減少銀行風(fēng)險對于實(shí)體經(jīng)濟(jì)的影響,已經(jīng)成為中國各個監(jiān)管機(jī)構(gòu)關(guān)注的焦點(diǎn)。加之中國處于經(jīng)濟(jì)轉(zhuǎn)型重要時期,如何防范國際乃至國內(nèi)銀行之間的風(fēng)險傳染,已經(jīng)成為銀行業(yè)風(fēng)險檢測的重要組成部分。首先,文章以中國工商銀行為例,利用銀行日收益率數(shù)據(jù)對工商銀行的短期風(fēng)險值進(jìn)行模擬。在分析中,對數(shù)據(jù)是否滿足ARCH類模型進(jìn)行分析,最后分析得出符合ARCH類模型要求。最終對ARCH、GARCH、EGARCH等進(jìn)行模擬,并列表分析了各個模型處理數(shù)據(jù),最終選取EGARCH模型的整個過程,得出具體的風(fēng)險值。在分析中,我們以中國五家國有上市銀行作為研究對象,分析各個銀行股票日收益、上證指數(shù)和銀行指數(shù)的長短期風(fēng)險。其次,文章利用模型分析影響銀行長期風(fēng)險的各個宏觀經(jīng)濟(jì)影響因素。在總結(jié)前人分析基礎(chǔ)和結(jié)合當(dāng)今經(jīng)濟(jì)發(fā)展現(xiàn)狀基礎(chǔ)上,選取了六個宏觀經(jīng)濟(jì)指標(biāo),并構(gòu)建了向量自回歸模型,對影響銀行長期風(fēng)險波動的宏觀經(jīng)濟(jì)影響因素進(jìn)行實(shí)證分析。最后,具體分析各個變量對風(fēng)險的具體影響,以及傳導(dǎo)過程。最后,通過對五家銀行進(jìn)行模擬和銀行業(yè)長期風(fēng)險和宏觀影響因素進(jìn)行分析,我們可以分析風(fēng)險研究結(jié)果:第一,ARCH類模型通過各個種類的比較分析,很好的解釋了自2010年至2016年的風(fēng)險狀況,并很好的模擬了中國經(jīng)濟(jì)轉(zhuǎn)型時期的風(fēng)險。即當(dāng)前五家上市國有銀行長期風(fēng)險較為穩(wěn)定,并處于國家銀監(jiān)會合理范圍內(nèi)。通過短期和長期風(fēng)險的分別測量,我們可以很好的應(yīng)對風(fēng)險,采取有針對性的措施。第二,對五大國有銀行進(jìn)行對比,風(fēng)險均值相差不多,但是風(fēng)險波動情況相差很大,尤其是交通銀行,應(yīng)該加大風(fēng)險防范,其他銀行根據(jù)自己實(shí)際情況進(jìn)行適當(dāng)調(diào)整,爭取把風(fēng)險控制在合理范圍內(nèi)。最后的實(shí)證分析顯示:GDP、CPI、HP及匯率的增長率對長期風(fēng)險影響較大,對整個經(jīng)濟(jì)運(yùn)行有較大影響。
[Abstract]:China and the world economy are more and more closely linked, which not only promotes the mutual exchange between the financial industry, but also promotes the development of the international banking industry. However, the attendant risks are becoming more and more serious, especially since the 2008 financial crisis, which has sounded the alarm bell for the world financial industry. The liberalization of international finance and the continuous opening of finance make banks as the core risk of the financial system continuously aggravated, it is easy to cause the whole world financial crisis because of the regional risk of a country. Therefore, how to effectively prevent and avoid bank risk, and formulate strategies to reduce the impact of bank risk on the real economy, has become the focus of Chinese regulators. In addition, China is in an important period of economic transition, how to prevent the risk contagion between international and domestic banks has become an important part of banking risk detection. Firstly, taking ICBC as an example, the paper simulates the short-term risk value of ICBC by using the data of daily rate of return. In the analysis, whether the data meets the ARCH class model is analyzed, and finally, it is found that the data meets the requirements of the ARCH class model. Finally, the paper simulates the EGARCH model, and analyzes the processing data of each model in parallel table. Finally, the whole process of EGARCH model is selected, and the specific risk value is obtained. In the analysis, we take five state-owned listed banks in China as the research object, and analyze the long-term and short-term risk of each bank's stock daily income, Shanghai stock index and bank index. Secondly, the paper uses the model to analyze the macro-economic factors that affect the long-term risks of banks. On the basis of summing up the previous analysis basis and combining with the present economic development situation, this paper selects six macroeconomic indicators, and constructs a vector autoregressive model to make an empirical analysis of the macroeconomic impact factors that affect the long-term risk volatility of banks. Finally, the specific impact of each variable on the risk, as well as the conduction process. Finally, through the simulation of five banks and the analysis of the long-term risk and macroscopical factors of banking, we can analyze the results of risk research: first, through the comparative analysis of various types of arch model, A good explanation of the risks from 2010 to 2016, and a good simulation of the risks of China's economic transition. That is, the five listed state-owned banks have a stable long-term risk and are within the reasonable scope of the CBRC. Through the short-term and long-term risk measurement, we can deal with the risk well, take targeted measures. Second, comparing the five major state-owned banks, the average value of risk is not much different, but the fluctuation of risk is very different. Especially the Bank of Communications, we should step up risk prevention, and other banks should make appropriate adjustments according to their actual situation. Try to keep the risk within a reasonable range. Finally, the empirical analysis shows that the growth rate of the CPIHP and the exchange rate have a great influence on the long-term risk and the whole economic operation.
【學(xué)位授予單位】：遼寧大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F832.33

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 喬雪利;;商業(yè)銀行多元化經(jīng)營與績效和風(fēng)險相關(guān)性研究[J];中國商貿(mào);2013年19期

2 楊義迅;蘇越良;;滬深股市的波動性分析——基于t分布下GARCH和SV模型的比較[J];河南科學(xué);2013年03期

3 高國華;潘英麗;;基于動態(tài)相關(guān)性的我國銀行系統(tǒng)性風(fēng)險度量研究[J];管理評論;2013年01期

4 曹廷求;朱博文;;貨幣政策、銀行治理與風(fēng)險承擔(dān)[J];金融論壇;2012年12期

5 朱信凱;韓磊;曾晨晨;;信息與農(nóng)產(chǎn)品價格波動:基于EGARCH模型的分析[J];管理世界;2012年11期

6 劉慧悅;劉金全;張小宇;;金融危機(jī)前后我國貨幣政策傳導(dǎo)機(jī)制的檢驗(yàn)與識別[J];上海經(jīng)濟(jì)研究;2012年11期

7 賴艷麗;;GARCH模型族在滬深300中的比較研究[J];上海管理科學(xué);2012年04期

8 劉孟飛;張曉嵐;張超;;我國商業(yè)銀行業(yè)務(wù)多元化、經(jīng)營績效與風(fēng)險相關(guān)性研究[J];國際金融研究;2012年08期

9 酈金梁;雷曜;李樹憬;;市場深度、流動性和波動率——滬深300股票指數(shù)期貨啟動對現(xiàn)貨市場的影響[J];金融研究;2012年06期

10 張雪蘭;何德旭;;貨幣政策立場與銀行風(fēng)險承擔(dān)——基于中國銀行業(yè)的實(shí)證研究(2000—2010)[J];經(jīng)濟(jì)研究;2012年05期

相關(guān)博士學(xué)位論文 前1條

1 桓宇;巴塞爾協(xié)議下商業(yè)銀行資本充足率調(diào)整行為研究[D];武漢大學(xué);2011年

相關(guān)碩士學(xué)位論文 前1條

1 肖變英;我國上市公司股票價格與宏觀經(jīng)濟(jì)的互動關(guān)系研究[D];武漢科技大學(xué);2004年

，

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