【摘要】：隨著我國匯率在兩次匯改之后由盯住美元制度走向更為市場化的浮動外匯管理制度，對其市場風險的研究顯得尤為必要。本文以美元兌人民幣匯率作為研究對象，內(nèi)容包括以下四個方面。 首先，利用長記憶性參數(shù)估計方法發(fā)現(xiàn)人民幣美元匯率存在顯著的長記憶特征。從結(jié)構(gòu)突變、頻率結(jié)構(gòu)和時間聚合三個角度考察該市場長記憶特征的根源，得到以下結(jié)論：（1）結(jié)構(gòu)突變是引起人民幣匯率市場長記憶特征的一大原因；（2）低頻序列是支撐長記憶特征的根源，高頻意味著隨機擾動；（3）長記憶特征存在期限結(jié)構(gòu)特征，即不同周期的數(shù)據(jù)呈現(xiàn)不同的長記憶動態(tài)特征。 其次，為了尋找最優(yōu)的人民幣匯率風險預(yù)測模型，本文以回測檢驗方法作為評價方法，介紹6種刻畫波動聚集性、非對稱性或長記憶性的風險模型，同時引入FHS技術(shù)、EVT技術(shù)和EVT-SKT等進行全面分析。結(jié)果表明：FIGARCH類模型通過檢驗且檢驗值處于低位，，但回測方法無法有效區(qū)別波動模型的優(yōu)劣性以及優(yōu)劣程度。 再次，基于傳統(tǒng)回測方法的不足，本文綜合監(jiān)管成本和超額虧損沖擊風險的概念，構(gòu)造一個新的風險評價指標Indic。應(yīng)用結(jié)果表明：Indic指標可以提供更為詳細的局部動態(tài)信息，且結(jié)合SPA檢驗方法能有效區(qū)別精選長記憶波動模型的優(yōu)劣性和優(yōu)劣程度，對風險度量模型擇優(yōu)具有一定的指導(dǎo)意義。 最后，目前的風險度量算法缺乏靈活與可控性，模型之間的選擇非此即彼。本文基于Boorstraping算法與模型樣本重構(gòu)的思想，將FHS技術(shù)和EVT技術(shù)相結(jié)合，獲得關(guān)于VaR的穩(wěn)健統(tǒng)計分布，同時設(shè)置分位數(shù)水平為滑點，構(gòu)造一類改進免疫遺傳算法--Indic-SPA檢驗方法尋找最優(yōu)的風險度量模型。結(jié)果表明：改進的算法能以最低的Indic成本獲得風險規(guī)避效用。
[Abstract]:With the change of exchange rate from dollar pegging system to more market-oriented floating exchange rate management system, it is necessary to study its market risk. This paper takes the exchange rate of US dollar to RMB as the object of study, including the following four aspects. First, the long memory parameter estimation method is used to find that the RMB dollar exchange rate has significant long memory characteristics. From the three angles of structural mutation, frequency structure and time aggregation, the causes of long memory characteristics in this market are investigated, and the following conclusions are obtained: (1) structural mutation is one of the major causes of long memory characteristics in RMB exchange rate market; (2) low frequency sequence is the root of supporting long memory feature, high frequency means random disturbance; (3) long memory feature exists term structure feature, that is, the data of different period presents different long memory dynamic feature. Secondly, in order to find the best prediction model of RMB exchange rate risk, this paper introduces six risk models which describe volatility aggregation, asymmetry or long memory, and introduces FHS technology. EVT technology and EVT-SKT were comprehensively analyzed. The results show that the FIGARCH model is tested and the test value is low, but the backmeasure method can not effectively distinguish the merits and demerits of the volatility model. Thirdly, based on the deficiency of the traditional method, this paper synthesizes the concepts of supervision cost and excess loss impact risk, and constructs a new risk evaluation index, Indic.. The application results show that the Indic index can provide more detailed local dynamic information, and the combination of SPA test method can effectively distinguish the advantages and disadvantages of the selected long-memory volatility model, which has a certain guiding significance for the risk measurement model to choose the best. Finally, the current risk measurement algorithm lacks flexibility and controllability, and the choice between models is either or. In this paper, based on the idea of Boorstraping algorithm and model sample reconstruction, the robust statistical distribution of VaR is obtained by combining FHS technique with EVT technique, and the quantile level is set as the sliding point. An improved immune genetic algorithm (Indic-SPA test) is constructed to find the best risk measurement model. The results show that the improved algorithm can obtain risk aversion utility at the lowest Indic cost.
【學位授予單位】：華南理工大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TP301.6;F832.6

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