tag:blogger.com,1999:blog-3864161393184469427.post5662997990434874144..comments2024-02-14T03:47:16.245-05:00Comments on work: A Good Introduction to GARCH and EWMA (Exponentialy Weighted Moving Average)minutemanhttp://www.blogger.com/profile/09798035945782569111noreply@blogger.comBlogger8125tag:blogger.com,1999:blog-3864161393184469427.post-29506188001571872702020-05-05T15:42:52.982-04:002020-05-05T15:42:52.982-04:00
@Jeff L
1. Right click the pics on a new window....<br />@Jeff L <br />1. Right click the pics on a new window. <br />2. Assuming mean zero, squared returns and residuals coincide. That is the assumption missing, otherwise the notation used is correct overall<br /><br />What is unclear to me is why does he no Variance forecasts with EWMA. <br />Clearly, you can do that.Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-3864161393184469427.post-74927499676906840282019-11-26T15:15:20.502-05:002019-11-26T15:15:20.502-05:00The formula for the average, unconditional varianc...The formula for the average, unconditional variance in the GARCH (1, 1) model is missing some brackets. Your graphic uses alpha0 / (1 - alpha1 + beta). It should read alpha0 / (1 - (alpha1 + beta)), or alpha0 / (1 - alpha1 - beta). Jeff Lhttps://www.blogger.com/profile/09796286053188659154noreply@blogger.comtag:blogger.com,1999:blog-3864161393184469427.post-13797104647442518982019-11-26T12:41:15.526-05:002019-11-26T12:41:15.526-05:00Hull notation and Linda notation are *not* mere di...Hull notation and Linda notation are *not* mere differences in notation (I think you got this incorrect information from the Bionic Turtle website). While the ARCH results can sometimes be similar, the methodology is very, very different. One uses squared lagged residuals while the other uses squared lagged returns. That is not a mere notation difference (e.g. I use "alpha" as my squared lagged residual coefficient, while you use "c" as you squared lagged residual coefficient). It is a fundamentally different calculation (e.g. squared lagged residuals are calculated entirely differently to squared lagged returns, and their values will, generally speaking, be very different). Not sure how else to explain the difference, even the formulae you provide on this blog page point to the radically different approaches. Jeff Lhttps://www.blogger.com/profile/09796286053188659154noreply@blogger.comtag:blogger.com,1999:blog-3864161393184469427.post-35459902689801050692019-11-25T16:56:40.187-05:002019-11-25T16:56:40.187-05:00All of your images (including equations etc) are c...All of your images (including equations etc) are cropped, regardless of whether the page is viewed in Chrome, Edge, Tor, etc. Which makes reading this article very time consuming. At a guess, it will send many potential eyeballs to other sites.Jeff Lhttps://www.blogger.com/profile/09796286053188659154noreply@blogger.comtag:blogger.com,1999:blog-3864161393184469427.post-78773749943412506572019-02-25T21:39:50.415-05:002019-02-25T21:39:50.415-05:00no such thing as good or not, any is okno such thing as good or not, any is okasdfzxhhttps://www.blogger.com/profile/00614166525824726672noreply@blogger.comtag:blogger.com,1999:blog-3864161393184469427.post-43072445713790630752017-03-25T05:18:37.796-04:002017-03-25T05:18:37.796-04:00i would like to know that in GARCH(1,1) Model we a...i would like to know that in GARCH(1,1) Model we are checking ARCH as well as GARCH effect. i will give example of my case here so that you will get my question sir and will help to solve my problem. i have one dependent variable i.e one year bond returns of India and 4 independent variables like interest rate, exchange rate,GDP and deficit finance. in order to calculate mean variance(ARCH effect) we need one dependent variable and one independent variable. can i take dependent variable i.e bond returns and any one independent variable of my choice to run mean variance or i have to follow any rule in order to know which variable out of 4 independent variables shall i use for mean variance and rest 3 independent variables for variance equation(GARCH effect). kindly guide me sir i will be highly obliged to you.<br /><br />Anonymoushttps://www.blogger.com/profile/09713863161520464436noreply@blogger.comtag:blogger.com,1999:blog-3864161393184469427.post-30916498406401847982016-05-23T16:20:16.209-04:002016-05-23T16:20:16.209-04:00In order to calculate the weights alpha, beta and ...In order to calculate the weights alpha, beta and gamma, you need to acquire the returns of the asset the volatility of which you want to model, assume a probability distribution for them, consider their probability density function (normal or log-normal or whatever you think it is) and finally apply Maximum Likelihood Estimation. All you want to do is to maximize the likelihood function (i.e. find the values of the parameters alpha beta and gamma that are most likely to lead to the observations you have already collected). That's the very vague idea, you will find numerous sources online. I hope this helps. Anonymoushttps://www.blogger.com/profile/07301990295934108768noreply@blogger.comtag:blogger.com,1999:blog-3864161393184469427.post-26411055407777828962015-08-13T03:04:00.528-04:002015-08-13T03:04:00.528-04:00No author is explaining how weights for alpha , be...No author is explaining how weights for alpha , beta and gama are calculated for Garch. In all examples why alpha is .20 where as beta is .7. Why such a big difference. Are weights assigned arbitrarily or there is any method to calculate. Can author throw some light on this.Unknownhttps://www.blogger.com/profile/04747483037450723083noreply@blogger.com