發(fā)布時(shí)間：2018-04-16 11:13

  本文選題：股市波動(dòng) + GARCH-MIDAS�。� 參考：《中國礦業(yè)大學(xué)》2017年碩士論文

【摘要】：目前在經(jīng)典計(jì)量經(jīng)濟(jì)學(xué)研究方法中主要體現(xiàn)兩種現(xiàn)象:一方面在研究過程中均使用相同頻率的數(shù)據(jù);另一方面許多研究以低頻率的股票市場(chǎng)數(shù)據(jù)為研究對(duì)象的數(shù)據(jù)指標(biāo),使得我國股票市場(chǎng)數(shù)據(jù)與宏觀外生解釋變量的數(shù)據(jù)具有相同的頻率。在對(duì)不同頻率的時(shí)間序列數(shù)據(jù)進(jìn)行處理時(shí),通常將高頻率的數(shù)據(jù)轉(zhuǎn)化為低頻率或者同頻率的數(shù)據(jù),這極有可能損失混頻(高頻)數(shù)據(jù)包含的信息有效性。本文正是在這樣的情況下提出以混頻數(shù)據(jù)作為研究對(duì)象的數(shù)據(jù)指標(biāo),既包含低頻數(shù)據(jù)又包含高頻數(shù)據(jù)。傳統(tǒng)的同頻模型由于受到數(shù)據(jù)頻率的限制,不能得到宏觀經(jīng)濟(jì)解釋變量和我國股票市場(chǎng)波動(dòng)之間的因果關(guān)系,Engle提出的經(jīng)典廣義自回歸條件異方差混頻數(shù)據(jù)抽樣模型稱作GARCH-MIDAS(Generalized AutoRegressive Conditional Heteroskedasticity),將混合抽樣技術(shù)方法(Mixed Data Sampling)融入傳統(tǒng)的GARCH模型中,將波動(dòng)率分解為短期和長期因素,并巧妙地將宏觀經(jīng)濟(jì)因素作為長期波動(dòng)率的解釋因子,使數(shù)據(jù)能夠得到更充分地利用。而本文與經(jīng)典GARCH-MIDAS模型有所不同,本文在解釋變量加入美元兌人民幣匯率高頻數(shù)據(jù),并采用美元兌人民幣匯率的已實(shí)現(xiàn)波動(dòng)率來解釋我國股市長期波動(dòng),拓展經(jīng)典GARCH-MIDAS模型。在實(shí)證研究中,分別從水平效應(yīng)和波動(dòng)效應(yīng)兩個(gè)角度建立多個(gè)單因素和多因素GARCH-MIDAS模型對(duì)我國股市波動(dòng)進(jìn)行分析估計(jì)。本文主要選取月度貨幣供應(yīng)、月度消費(fèi)者價(jià)格指數(shù)和日度匯率為指標(biāo)研究股市波動(dòng)�；诟倪M(jìn)GARCH-MIDAS模型的研究結(jié)果顯示:貨幣供應(yīng)量的水平值和波動(dòng)率均對(duì)我國股票市場(chǎng)波動(dòng)有顯著的正向影響關(guān)系。消費(fèi)者價(jià)格指數(shù)的水平效應(yīng)與我國股票市場(chǎng)波動(dòng)有顯著的負(fù)相關(guān)關(guān)系,而在波動(dòng)效應(yīng)上與我國股票市場(chǎng)波動(dòng)之間的關(guān)系不顯著。美元兌人民幣匯率在水平效應(yīng)和波動(dòng)效應(yīng)均對(duì)股票市場(chǎng)波動(dòng)產(chǎn)生顯著的負(fù)相關(guān)關(guān)系。多因素模型的估計(jì)結(jié)果與單因素模型的估計(jì)結(jié)果基本相同,但是由于多因素模型估計(jì)的參數(shù)較多,可能存在過度參數(shù)化等問題,這些問題使得一些系數(shù)不再顯著。同時(shí),對(duì)研究模型進(jìn)行預(yù)測(cè)能力分析發(fā)現(xiàn)單因素模型和多因素模型均有很強(qiáng)的預(yù)測(cè)能力,而且基于多因素水平效應(yīng)模型的預(yù)測(cè)效果比基于單因素水平效應(yīng)模型的預(yù)測(cè)效果更好。此外,通過比較發(fā)現(xiàn)多因素混頻模型比單因素混頻模型可以更好地刻畫我國股票市場(chǎng)價(jià)格波動(dòng)的長期成分。結(jié)合我國實(shí)際情況提出以下政策建議,包括提高宏觀調(diào)控政策效力、發(fā)展健康股市、引導(dǎo)理性投資及完善披露機(jī)制等。
[Abstract]:At present, there are two main phenomena in classical econometrics research methods: on the one hand, the data of the same frequency are used in the research process; on the other hand, many studies take the low-frequency stock market data as the data index.It makes the data of Chinese stock market have the same frequency as the data of macroscopical exogenous explanatory variables.When processing time series data with different frequencies, the high frequency data is usually converted to low frequency or the same frequency data, which is likely to lose the validity of the information contained in the mixing (high frequency) data.In this paper, the data index of mixing data is proposed, which includes both low frequency data and high frequency data.The traditional co-frequency model is limited by the data frequency.The causality between macroeconomic explanatory variables and stock market volatility can not be obtained. The classical generalized autoregressive conditional heteroscedasticity mixed data sampling model proposed by Engle is called GARCH-MIDAS(Generalized AutoRegressive Conditional heteroscedastic sampling. The mixed Data sampling method is described as mixed Data sampling.Into the traditional GARCH model,The volatility is decomposed into short and long term factors, and macroeconomic factors are used as the explanation factors of long term volatility, so that the data can be used more fully.This paper is different from the classical GARCH-MIDAS model in explaining the variables by adding the high frequency data of USD / RMB exchange rate and using the realized volatility rate of USD- RMB exchange rate to explain the long-term volatility of China's stock market and extend the classical GARCH-MIDAS model.In the empirical study, we establish several single-factor and multi-factor GARCH-MIDAS models from the perspective of horizontal effect and volatility effect to analyze and estimate the volatility of China's stock market.This paper mainly selects monthly money supply, monthly consumer price index and daily exchange rate as indicators to study stock market volatility.The results based on the improved GARCH-MIDAS model show that both the level of money supply and the volatility have significant positive effects on the volatility of China's stock market.There is a significant negative correlation between the horizontal effect of the consumer price index and the volatility of the stock market in China, but there is no significant relationship between the fluctuation effect and the volatility of the stock market in China.Dollar / RMB exchange rate has a significant negative correlation with stock market volatility both in horizontal effect and volatility effect.The estimation results of multivariate model are basically the same as those of single factor model. However, due to the large number of parameters estimated by the multivariate model, there may be some problems such as over-parameterization, which make some coefficients less significant.At the same time, it is found that both the single-factor model and the multi-factor model have strong predictive ability, and the prediction effect based on the multi-factor horizontal effect model is better than that based on the single-factor horizontal effect model.In addition, it is found that the multi-factor mixing model can better describe the long-term components of the stock market price volatility in China than the single-factor mixing model.According to the actual situation of our country, this paper puts forward the following policy suggestions, including improving the effect of macro-control policy, developing healthy stock market, guiding rational investment and perfecting the disclosure mechanism, etc.
【學(xué)位授予單位】：中國礦業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F832.51;F124;F224

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