[闲谈] GARCH 模式简介

not4weak

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.

not4weak

Generalized AutoRegressive Conditional Heteroskedasticity

wsjboy

Generalized AutoRegressive Conditional Heteroskedasticity

谢谢，没有听说过，学习

tianfangye

形成模式或算法的东西最后都会败给博弈玩家

aimei

回复 4# tianfangye

天方欢迎亚

北京哥哥

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？

sjwi

雪域高原

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！

tianfangye

回复 5# aimei

恭喜艾美，BBRY 盘后暴涨

tianfangye

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

北京哥哥

回复 10# tianfangye


    多谢，我是指您说的博弈玩儿家是指谁？

newflower888

OMG, it's so cool but I don't understand:S0002)

wenxue

没有听说过，学习