代寫essay：基於GARCH模型構建財務模型
這是本研究的重要部分之一，以制定所需的和最好的模型，能夠處理這麽多的數據，並能夠做時間序列分析。在上述各種方法的幫助下，最終確定了GARCH模型。這是最終確定的，因為它是分析或回歸財務時間序列數據和特征的最佳方法或模型之一(Gurarda et al.， 2016)。該模型也適用,因為在中國上市公司收集到的數據高於50,有很多數據點的數量等一系列誤差項的方差或創新實際上函數相關的總體大小錯誤在之前的時期。
代寫essay ：基於GARCH模型構建財務模型
這就是為什麽選擇GARCH模型作為本研究的最合適的方法，使用三種不同的模型，從給定的數據中提供所需的結果或輸出。GARCH模型的波動率為:
t =μ+∅rt-1
t2 =ω+αt-12 +βt-12
rt = t + t
t = tzt
在那裏,
獨立恒等分布過程
rt =回報
t =殘差
t= rt的條件均值
t2= rt的條件方差
在給定條件下
∅= < 1
ω> 0
α,β≥0
α+β> 1
這些條件適合GARCH模型在分析杠桿效應過程中采用。杠桿效應通常是由於負回報對未來波動性的影響大於正回報。
代寫essay ：基於GARCH模型構建財務模型
Methodology: Build the financial model based on the GARCH model
This is one of the important part of this study to formulate the required and best model that is capable of handling this much data and is able to do the time series analysis. With help of the various methods as discussed above, the GARCH model was finalized. This was finalized because it is one of the best method or model to analyse or regress the financial time series data and characterize (Gurarda et al., 2016). This model is also applied because in the collected data above for 50 Chinese listed companies, there are lot number data points which were there in a series such that the variance for the error term or the innovation it actually the function which is related to the overall size of the error taken during the previous time periods.
代寫essay ：基於GARCH模型構建財務模型
This was the reason for choosing GARCH model as the best suitable method with three different models for this study to provide the required results or outputs form the given data. In GARCH model the volatility is given as:
t=μ+∅rt-1
t2=ω+αt-12+βt-12
rt=t+t
t=tzt
Where,
zt= independent and identically distributed process
rt=returns
t=residuals
t= conditional mean of rt
t2= conditional variance of rt
With given conditions as
∅=<1
ω>0
α,β≥0
α+β>1
These conditions are suitable for GARCH model to adopt in the process for analysing the leverage effect. The leverage effect is normally caused with the fact that negative returns have a greater influence on future volatility than do positive returns.
