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[闲谈] GARCH 模式简介

坛里高手如云,竟然有人提出了GARCH模式,下面是简介


In reference to modeling fitting, this means the residuals vary in magnitude.  Volatility clustering means the data is auto correlated.  GARCH is a statistical tool that helps predict the residuals in k data
ARCH means Autoregressive Conditional Heteroskedasiticy and is closely related to GARCH. The simplest method to predict stock volatility is an n day standard deviation, and let’s consider a rolling year with 252 trading days.  If we want to predict stock prices for the next day, the mean is usually a safe starting point.
But the mean treats each day with the same weight.  Giving the recent past more significance is more logical, with perhaps an exponential weighted average being a better method to predict tomorrow’s stock price.
However, this method does not capture any data older than a year, and the weighting is rather arbitrary.  The ARCH model, however, varies weights on each residual such that the best fit is obtained.  The GARCH (General Autoregressive Conditional Heteroscedasiticy) is similar, but gives recent data more significance.
The GARCH(p,q) model has two characteristic parameters; p is the number of GARCH terms and q is the number of ARCH terms. GARCH(1,1) is defined by the following equation.

h is variance, ε is the residual squared, t denotes time. ω, α and β are empirical parameters determined by maximum likelihood estimation. The equation tells us that tomorrow’s variance is a function of
  • today’s squared residual,
  • today’s variance,
  • the weighted average long-term variance

GARCH(1,1) captures only once square residual and one square variance.
This is not a magic wand, and financial analysts should be use the approach with a high degree of caution.  Given the appropriate circumstance, the predicted variance can greatly differ from the actual variance. Techniques such as the Ljung box text are used to determine if any autocorrelation remains in the residuals.
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江南有丹桔,经冬犹绿林。 岂伊地气暖,自有岁寒心。 可以荐佳客,奈何阻重深。 运命唯所遇,循环不可寻。 徒言树桃李,此木岂无阴。
没有听说过,学习
OMG, it's so cool but I don't understand:S0002)
回复 10# tianfangye


    多谢,我是指您说的博弈玩儿家是指谁?
老大能否给展展?
北京哥哥 发表于 2015-1-21 08:53


市场经济预测用数学(回归)模型实践上仅适合风险评估用, 目前没听说一个炒股投资成功的例子。
https://en.wikipedia.org/wiki/Long-Term_Capital_Management

http://blog.sina.com.cn/s/blog_881535bf010113nz.html
回复 5# aimei

恭喜艾美,BBRY 盘后暴涨
回复 1# not4weak


  N大大, 谢谢分享, 能否问一下GARCH 里面的参数是什么? 如果用SAS, 可否借用一下SAS code?I am predicting direction, if you can borrow you code, I can apply that to my code to predict day High - low.

多谢!
形成模式或算法的东西最后都会败给博弈玩家
tianfangye 发表于 2015-1-20 23:49



    老大能否给展展?
回复 4# tianfangye

天方欢迎亚
形成模式或算法的东西最后都会败给博弈玩家
Generalized AutoRegressive Conditional Heteroskedasticity

谢谢,没有听说过,学习
绝不和进行人身攻击者争论,我的一条纪律。避免进行人身攻击的最简单方法是就事论事。
Generalized AutoRegressive Conditional Heteroskedasticity
江南有丹桔,经冬犹绿林。 岂伊地气暖,自有岁寒心。 可以荐佳客,奈何阻重深。 运命唯所遇,循环不可寻。 徒言树桃李,此木岂无阴。
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