本文關鍵詞：基于價格極差-ARMA-GARCH-POT模型的風險價值研究　出處：《西華師范大學》2016年碩士論文　論文類型：學位論文

  更多相關文章： VaR 價格極差 ARMA模型 GARCH模型 極值理論

【摘要】：近年來,隨著信息科技迅猛發(fā)展,中國證券市場也在發(fā)生著日新月異的變化。然而我國的金融市場是一個較新興的市場,存在著市場結(jié)構(gòu)不健全、投機性較強等問題。除此之外,全球一體化進程不斷的深入,各種政策和消息在給全球金融市場帶來沖擊時,也給我國的金融市場帶來沖擊,證券市場就會隨之受到影響。隨著我國證券市場的不斷發(fā)展,面臨的風險也越來越多。如何準確度量風險,將損失降到最低就變得越來越重要。而當今世界度量金融資產(chǎn)風險價值的主流方法便是VaR模型,大量的國內(nèi)外學者都在研究如何用Va R模型來度量風險,提高風險價值的有效性,這對度量金融風險具有重要的意義。因此本文在前人的基礎上,將引入價格極差的GARCH模型與方差-協(xié)方差法和極值理論三者相結(jié)合改進VaR模型,旨在提高VaR值的有效性。本文從金融市場風險的背景展開研究,詳細闡述了風險價值VaR的定義和基本的計算方法,介紹了應用最廣泛的估計VaR的組合模型,即GARCH模型和方差-協(xié)方差模型的組合,從這個模型中可以知道估計VaR需要知道兩個量:即對金融時間序列建立GARCH模型后得到的標準差序列和時間序列概率分布的分位數(shù)。因此可以從這兩方面入手改進模型,運用包含股票收盤價、最高價和最低價的極差-GARCH模型來提高標準差的有效性;在運用方差-協(xié)方差與GARCH模型構(gòu)成的組合模型估計VaR時,通常都是在正態(tài)假設的情況下進行的,但是研究表明時間序列往往具有尖峰厚尾的特點,所以基于正態(tài)分布假設求VaR的方法一般就會低估尾部風險,而極值理論是直接處理時間序列數(shù)據(jù)的尾部,并且不需要對損失數(shù)據(jù)預先假設服從任何分布,因此由極值理論求出的分位數(shù)更為有效。將這兩個模型組合在一起構(gòu)成了新模型:價格極差---POTGARCHARMA模型,最后經(jīng)過實證分析,價格極差---POTGARCHARMA模型求出的VaR值,比未改進前的模型估計出的VaR值的失敗率更接近于顯著性水平,表明了新模型價格極差---POTGARCHARMA模型確實提高了VaR值的有效性,說明文中對VaR模型的改進是可行的。本文最后對實證分析得出的結(jié)論進行總結(jié),并詳細闡述了本文的不足之處,在不足之處的基礎上,提出了以后的一些研究方向。
[Abstract]:In recent years, with the rapid development of information technology, China's securities market is also changing with each passing day. However, China's financial market is a relatively new market, there is a market structure is not perfect. In addition, the process of global integration continues to deepen, various policies and news to the global financial market impact, but also to our financial market impact. The securities market will be affected. With the continuous development of China's securities market, there are more and more risks. How to accurately measure the risk. It becomes more and more important to minimize losses, and the mainstream method of measuring the risk value of financial assets in the world today is the VaR model. A large number of scholars at home and abroad are studying how to use the VaR model to measure risk and improve the effectiveness of the value of risk, which is of great significance to the measurement of financial risk. The GARCH model of price range is combined with the variance-covariance method and the extreme value theory to improve the VaR model. In order to improve the effectiveness of VaR, this paper studies the background of risk in financial markets, and expounds the definition and basic calculation method of VaR in detail. In this paper, the most widely used combinatorial model for estimating VaR is introduced, that is, the combination of GARCH model and variance-covariance model. From this model, you can see that estimating VaR needs to know two quantities:. That is the quantiles of the standard deviation series and the probability distribution of the time series after the establishment of the GARCH model for the financial time series, so we can improve the model from these two aspects. The GARCH model, which includes the closing price, the highest price and the lowest price, is used to improve the effectiveness of the standard deviation. When using the combination of variance-covariance and GARCH model to estimate VaR, it is usually carried out under the normal assumption, but the research shows that the time series often have the characteristics of peak and thick tail. Therefore, the method of calculating VaR based on normal distribution hypothesis generally underestimates tail risk, while extreme value theory deals with the tail of time series data directly, and does not presuppose any distribution of lost data. Therefore, the quantiles derived from the extreme value theory are more effective. The two models are combined to form a new model: price difference-POTGARCHARMA model, and finally through empirical analysis. The VaR value calculated by POTGARCHARMA model is closer to the significant level than the VaR value estimated by the unimproved model. It shows that the price of the new model is very poor-POTGARCHARMA model does improve the validity of VaR value. The improvement of VaR model is feasible. Finally, the conclusion of empirical analysis is summarized, and the inadequacies of this paper are described in detail, on the basis of deficiency. Some future research directions are proposed.
【學位授予單位】：西華師范大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：F224

【參考文獻】

相關期刊論文 前10條

1 白曉燕;陳琴;;基于VaR-GARCH模型的我國外匯儲備匯率風險度量[J];武漢理工大學學報(社會科學版);2013年06期

2 趙樹然;任培民;趙昕;;基于CARR-EVT整體方法的動態(tài)日VaR和CVaR模型研究[J];數(shù)量經(jīng)濟技術經(jīng)濟研究;2012年11期

3 周瑩;焦建玲;;基于GARCH-VaR模型的石油價格風險研究[J];合肥工業(yè)大學學報(自然科學版);2011年09期

4 范玉妹;玄婧;;ARMA算法在GDP預測中的應用[J];江南大學學報(自然科學版);2010年06期

5 陳維龍;;GARCH-VAR模型在開放式基金風險度量中的應用[J];哈爾濱商業(yè)大學學報(社會科學版);2010年02期

6 桂文林;韓兆洲;潘慶年;;POT模型中GPD“厚尾”性及金融風險測度[J];數(shù)量經(jīng)濟技術經(jīng)濟研究;2010年01期

7 邵錫棟;殷煉乾;;基于實現(xiàn)極差和實現(xiàn)波動率的中國金融市場風險測度研究[J];金融研究;2008年06期

8 葉華;王躍平;;VaR在商業(yè)銀行匯率風險管理中的應用[J];經(jīng)濟與社會發(fā)展;2007年02期

9 郭名媛;張世英;;基于MRS-GARCH模型的VaR方法及其在上海股市的應用[J];西北農(nóng)林科技大學學報(社會科學版);2006年02期

10 李紅權,馬超群;中國證券投資基金績效評價的理論與實證研究[J];財經(jīng)研究;2004年07期

相關碩士學位論文 前2條

1 錢思思;基于價格極差-Garch模型的VaR風險研究[D];西南財經(jīng)大學;2012年

2 張艷;金融市場風險度量的VaR模型改進[D];遼寧大學;2009年

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