本文關(guān)鍵詞：基于GARCH-M模型對中國股票市場風險溢價的研究　出處：《重慶大學》2013年碩士論文　論文類型：學位論文

  更多相關(guān)文章： 波動率 風險溢價 半?yún)?shù)模型 GARCH-M 局部多項式

【摘要】：風險溢價是金融經(jīng)濟學的一個核心概念，對它的有效度量直接影響著資產(chǎn)定價，投資分析和風險管理等金融市場活動。風險溢價的水平可以直觀的反映金融市場帶給投資者的風險回報，對投資者的投資決策具有指導意義。尤其是在中國，金融市場變化日新月異，各種各樣的金融衍生品不斷出現(xiàn)，金融市場風險變得越來越難以捉摸，研究風險溢價的水平可以幫助我們更好的了解市場的微觀結(jié)構(gòu)以及金融資產(chǎn)的流動性等問題，從宏觀上把握市場的走勢。 眾所周知，風險溢價的研究最早起源于資本資產(chǎn)定價理論，，認為風險承擔者應該獲得相應的風險回報。在對波動率風險溢價進行分析時，GARCH-M模型是常用的工具之一，但它作為參數(shù)模型，不可避免地給出一個具體的模型形式并對模型誤差做出假設(shè)，當這些假設(shè)不成立時，統(tǒng)計推斷便不再精確，甚至沒有實際意義。針對這些缺點，人們將非參數(shù)技術(shù)引用到時間序列分析中，提出了半?yún)?shù)GARCH-M模型，參數(shù)部分可對模型的估計結(jié)果進行一定的解釋，而非參數(shù)部分則彌補了參數(shù)模型的缺陷，降低了估計值的偏差。 本文提出的半?yún)?shù)非對稱GARCH-M模型繼承了參數(shù)和非參數(shù)模型的優(yōu)點，將波動率方程部分處理成傳統(tǒng)的TGARCH過程，而均值方程處理成非參數(shù)函數(shù)的形式，能更好的擬合波動率與風險溢價之間的關(guān)系。文中首先分別采用局部多項式擬合和加權(quán)最小二乘法對均值函數(shù)和波動率參數(shù)進行了估計并證明了其大樣本性質(zhì)，包括漸近正態(tài)性和相合性。其次通過模擬實驗驗證了半?yún)?shù)模型的可行性，并與參數(shù)模型比較證明了其優(yōu)良性。最后，我們將半?yún)?shù)TGARCH-M模型用于上證指數(shù)波動率風險溢價的實證研究。結(jié)果表明，在MSE和QLIKE準則下，半?yún)?shù)TGARCH-M模型的擬合優(yōu)度明顯高于參數(shù)模型。同時，我們利用半?yún)?shù)模型估計結(jié)果對波動率與超額收益率之間的關(guān)系進行了分析，說明風險溢價的曲線是非線性非單調(diào)的，所得結(jié)論符合中國股票市場的現(xiàn)狀。
[Abstract]:Risk premium is a core concept of financial economics, and its effective measurement directly affects asset pricing. Investment analysis, risk management and other financial market activities. The level of risk premium can directly reflect the return on risk brought by the financial market, which has guiding significance for investors to make investment decisions, especially in China. Financial market changes with each passing day, a variety of financial derivatives continue to appear, financial market risks become more and more elusive. Studying the level of risk premium can help us better understand the microstructure of the market and the liquidity of financial assets, and grasp the trend of the market from the macro perspective. As we all know, the research of risk premium originated from the capital asset pricing theory. GARCH-M model is one of the commonly used tools, but as a parameter model, it inevitably gives a specific model form and makes assumptions about model errors, when these assumptions do not hold true. Statistical inference is no longer accurate, even without practical significance. In view of these shortcomings, non-parametric techniques are applied to time series analysis, and a semi-parametric GARCH-M model is proposed. The parameter part can explain the estimation result of the model to a certain extent, but the non-parametric part can make up the defect of the parameter model and reduce the deviation of the estimated value. The semi-parametric asymmetric GARCH-M model inherits the advantages of both parametric and non-parametric models, and the volatility equation is partially treated as a traditional TGARCH process. The mean equation is treated as a nonparametric function. Firstly, the local polynomial fitting and the weighted least square method are used to estimate the mean function and the volatility parameter, and the properties of large sample are proved. It includes asymptotic normality and consistency. Secondly, the feasibility of the semi-parametric model is verified by simulation experiments, and its superiority is proved by comparison with the parametric model. We apply the semi-parametric TGARCH-M model to the empirical study of the risk premium of Shanghai stock index volatility. The results show that under the MSE and QLIKE criteria. The goodness of fit of semi-parametric TGARCH-M model is obviously higher than that of parametric model. At the same time, we use semi-parametric model to estimate the relationship between volatility and excess return. It shows that the curve of risk premium is nonlinear and nonmonotone, and the conclusion is in line with the present situation of Chinese stock market.
【學位授予單位】：重慶大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F832.51;F224;O212.1

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相關(guān)期刊論文 前1條

1 魯萬波;;基于非參數(shù)GARCH模型的中國股市波動性預測[J];數(shù)理統(tǒng)計與管理;2006年04期

本文編號：1394360