基于GARCH-VaR模型對房地產(chǎn)上市公司的財務風險研究
發(fā)布時間：2018-10-09 11:02
【摘要】：自2007年次貸危機爆發(fā)以后,迫使各國開始重視風險管理的研究。經(jīng)過“金融風暴”的爆發(fā),財務風險無處不在,企業(yè)必須時刻注意識別和防范風險。本文嘗試著對目前發(fā)展的財務風險管理系統(tǒng)提出適合企業(yè)的財務風險預警系統(tǒng)。目前我國房地產(chǎn)行業(yè)發(fā)展迅猛,社會及政府部門對其發(fā)展態(tài)勢給予了高度關(guān)注。在經(jīng)濟形勢嚴峻的情況下,防范房地產(chǎn)上市公司內(nèi)部財務風險就顯得尤為重要。 本文針對新形勢的發(fā)展狀態(tài)下,提出了預測房地產(chǎn)上市公司財務風險的方法。本文針對房地產(chǎn)行業(yè)龍頭公司—M房地產(chǎn)上市公司進行了研究分析,進而通過M房地產(chǎn)上市公司的研究方法,推廣到所有房地產(chǎn)上市公司的研究中,并分析了共36家房地產(chǎn)上市公司的財務風險值。本文選取了五大類財務指標,包括：每股指標、盈利能力、成長能力、營運能力、償債及資本結(jié)構(gòu)共22個財務指標進行研究,選取時間為2002年3月31日至2013年3月31日期間的季度財務數(shù)據(jù)共45個季度數(shù)據(jù)。本文利用熵權(quán)法來進行測算權(quán)重,從而算出了綜合測評財務指標序列,再對該序列進行時間序列分析的相關(guān)檢驗,主要檢驗包括：單位根檢驗、自相關(guān)性檢驗、ARCH-LM檢驗等。綜合測評財務指標序列通過了單位根檢驗,但存在著3階的自相關(guān)性,同時還檢驗出其存在著高階ARCH效應,因此對其建立GARCH模型,模型檢驗后發(fā)現(xiàn)消除了之前存在的ARCH效應,結(jié)論是模型通過檢驗,可以利用GARCH模型來分析。本文通過GARCH模型來計算風險VaR值,從而得到綜合測評財務指標值計算出相應的財務風險值。為了分析財務風險值,采用自回歸模型(VAR模型)來預測下一期的風險值,預測階數(shù)為5階,預測模型的擬合優(yōu)度達到71.4%,效果較好。另一方面,本文通過建立房地產(chǎn)上市公司財務風險轉(zhuǎn)移概率矩陣進一步度量所有房地產(chǎn)上市公司的風險轉(zhuǎn)移概率,從而能夠更加有效地控制風險。 本文通過建立的預測模型,能夠合理的分析出下一期的財務風險值,從而可以達到防范風險,預測風險的目的,能夠提前為房地產(chǎn)上市公司的風險防范采取適當措施,從而能夠為房地產(chǎn)行業(yè)提供更加有效的規(guī)避方法。本文已建立起適合企業(yè)自身發(fā)展的財務風險管理體系,但同時也要不斷完善財務風險管理的系統(tǒng),使預測效果達到更佳。
[Abstract]:Since the subprime mortgage crisis broke out in 2007, countries began to attach importance to risk management research. After the outbreak of "financial storm", financial risks are everywhere, enterprises must always pay attention to identify and guard against risks. This paper attempts to put forward a financial risk early warning system suitable for enterprises to develop the current financial risk management system. At present, the real estate industry is developing rapidly in our country, and the society and government departments pay close attention to it. In the severe economic situation, it is particularly important to guard against the internal financial risks of listed real estate companies. In view of the development of the new situation, this paper puts forward a method to predict the financial risk of real estate listed companies. This article has carried on the research analysis to the real estate industry leading company -M real estate listed company, then through the M real estate listed company's research method, popularized to all the real estate listed company's research, And analyzed a total of 36 real estate listed companies financial risk value. This paper selects five kinds of financial indicators, including: per share index, profitability, growth capacity, operating capacity, debt service and capital structure of a total of 22 financial indicators to study. A total of 45 quarterly financial data were selected from March 31, 2002 to March 31, 2013. In this paper, entropy weight method is used to calculate the financial index sequence of comprehensive evaluation, and then the correlation test of time series analysis of this series is carried out. The main tests include unit root test, autocorrelation test and ARCH-LM test. The financial index sequence of comprehensive evaluation has passed the unit root test, but there is a third order autocorrelation, and at the same time, the existence of high order ARCH effect is also tested. Therefore, the GARCH model is established, and the former ARCH effect is eliminated after the model test. The conclusion is that GARCH model can be used to analyze the model. In this paper, the risk VaR value is calculated by GARCH model, and the corresponding financial risk value is calculated by synthetically evaluating the financial index value. In order to analyze the financial risk value, the autoregressive model (VAR model) is used to predict the risk value in the next period. The prediction order is 5 order, and the goodness of fit of the prediction model is 71.4. The effect is good. On the other hand, this paper further measures the risk transfer probability of all listed real estate companies by establishing the financial risk transfer probability matrix of real estate listed companies, so as to control the risk more effectively. Through the prediction model, this paper can reasonably analyze the value of financial risk in the next period, so as to achieve the purpose of risk prevention, forecast risk, and take appropriate measures for the risk prevention of listed real estate companies in advance. Thus, the real estate industry can provide a more effective way to circumvent. This paper has established a financial risk management system suitable for the enterprise's own development, but at the same time, it is necessary to continuously improve the financial risk management system so as to achieve a better forecast effect.
【學位授予單位】：內(nèi)蒙古工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F299.233.42
本文編號：2259094
[Abstract]:Since the subprime mortgage crisis broke out in 2007, countries began to attach importance to risk management research. After the outbreak of "financial storm", financial risks are everywhere, enterprises must always pay attention to identify and guard against risks. This paper attempts to put forward a financial risk early warning system suitable for enterprises to develop the current financial risk management system. At present, the real estate industry is developing rapidly in our country, and the society and government departments pay close attention to it. In the severe economic situation, it is particularly important to guard against the internal financial risks of listed real estate companies. In view of the development of the new situation, this paper puts forward a method to predict the financial risk of real estate listed companies. This article has carried on the research analysis to the real estate industry leading company -M real estate listed company, then through the M real estate listed company's research method, popularized to all the real estate listed company's research, And analyzed a total of 36 real estate listed companies financial risk value. This paper selects five kinds of financial indicators, including: per share index, profitability, growth capacity, operating capacity, debt service and capital structure of a total of 22 financial indicators to study. A total of 45 quarterly financial data were selected from March 31, 2002 to March 31, 2013. In this paper, entropy weight method is used to calculate the financial index sequence of comprehensive evaluation, and then the correlation test of time series analysis of this series is carried out. The main tests include unit root test, autocorrelation test and ARCH-LM test. The financial index sequence of comprehensive evaluation has passed the unit root test, but there is a third order autocorrelation, and at the same time, the existence of high order ARCH effect is also tested. Therefore, the GARCH model is established, and the former ARCH effect is eliminated after the model test. The conclusion is that GARCH model can be used to analyze the model. In this paper, the risk VaR value is calculated by GARCH model, and the corresponding financial risk value is calculated by synthetically evaluating the financial index value. In order to analyze the financial risk value, the autoregressive model (VAR model) is used to predict the risk value in the next period. The prediction order is 5 order, and the goodness of fit of the prediction model is 71.4. The effect is good. On the other hand, this paper further measures the risk transfer probability of all listed real estate companies by establishing the financial risk transfer probability matrix of real estate listed companies, so as to control the risk more effectively. Through the prediction model, this paper can reasonably analyze the value of financial risk in the next period, so as to achieve the purpose of risk prevention, forecast risk, and take appropriate measures for the risk prevention of listed real estate companies in advance. Thus, the real estate industry can provide a more effective way to circumvent. This paper has established a financial risk management system suitable for the enterprise's own development, but at the same time, it is necessary to continuously improve the financial risk management system so as to achieve a better forecast effect.
【學位授予單位】：內(nèi)蒙古工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F299.233.42
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