Quote from DT-waw:
Thank you. His analysis shows that forecasting volatility using Exp. Weighted MA can produce higher return with the same St. Dev. than using GARCH. There're no details how he calculated this.
Are you sure? On page 5 he describes how ARCH processes are used. He compares (through regression) the results of a statistical analysis of the historical variance (volatility) of returns to the returns predicted by both techniques.
To do the same thing all you or I would have to do is find a set of closing prices, calculate the difference from day to day and use that as our 'return series'. All page 5 is saying is that once you have that series all you have to do is find the average of the series over so many days then compute the standard deviation over the same time frame for all the different models. Do that for all models for a number of different time frames. Once that is done, compare the models' results for all the time frames. Each model will produce a curve based on that model's average and standard deviation for different time frames. Use a linear or other regression calculation to find the difference between the predicted value and the historical value. If the level of significance is fairly low, as it appears to be in
most cases, then we can reject the hypothesis that modeling has little economic impact.
Looking to the charts...if for each data point you divide the standard deviation by the expected return the result is called the coefficient of variation or volatility at that particular point on the efficiency frontier. Ideally, what you want is "money for nuthin' and chicks for free". But, since you aren't, probably, going to get that
it is best to get the highest return for the level of volatility in returns or "sigma" (he mentioned on page 5) you are willing to tolerate.
I think I've got that covered. Hopefully, there aren't any mistakes. If you find some, please let me know.
), the problem is to find it and the reason that researchers didn't find it is because they try to extract knowledge from pure datas which is an idiocy from paradigm point of view because the model's knowledge is transcendant to the datas that is to say you cannot deduce it from the datas alone but only if you have the idea of how market really works or you will play with datas and stochastic models much like a monkey see: