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[Sorry, I'm new here and accidently posted this as an answer and its just meant as a comment responding to a question, but it does not let me delete answers to put it under comments. If I last long enough, I'm sure I'll figure out how to edit things.] PCA is an eigenvalue/eigenvector decomposition of the data frequently applied in risk management to look ...


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You need to assign each of the target variables to their own column and then train a model for each of your forecast horizons library(quantmod) symbol= getSymbols("AAPL",from="2010-03-01", auto.assign=F) close<-Cl(symbol) open<-Op(symbol) lc1<-lag(close) lc2<-lag(close,2) lc3<-lag(close,3) lo1<-lag(open) lo2<-lag(open,2) ...


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It is too much too text so I take screenshots and the link to Rob Hynman's blog entry: If you formulate the ARIMA model likes this: Then you get these long term forecasts:


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There is probably nothing wrong with your code although I did not check it in Mathematica. Normally, Geometric Brownian motion is just a model. Here, you simulate lots of paths and then average over it. The first plot gives something like $$ E(S_t) = S_0*\exp(\mu t) $$ with $S_0$ the initial stock price. However, because of the simulation, you do not get ...


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I dont think neither of both ideas are to be very fertile in the present structures of banks or asset management firms. There are several factors that have influenced the birth of algo trading. 1) The development of computer processing capacity behyond human capabilities which provided the hability to process more information than any team would. This will ...


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You are right - GARCH model models volatility. They write: " The GARCH [27] can be used to model changes in the variance of the errors as a function of time." What people often do is to fit an ARIMA model (that can be used to forecast a time series) and apply a GARCH model to the errors (which gives you a feeling for the forecast error). See Hyndman and ...


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Obviously a perfect forecast for interest rates is a bit hard to come by, such a thing would make the inventor quite a tidy sum. Broadly, the task you're seeking to accomplish falls under the banner of yield curve modeling, and there is a very substantial body of research in this area, including several good books. There are some canonical examples of ...


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You are probably computing autocorrelation in the prices. If you compute autocorrelation between the returns or log returns then you will not see the results you are getting. This is because: Tomorrow's price will always be influenced by lagged prices and the series will not look weak stationary if you plot it. The direct differencing doesn't help either ...


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Check your calculations, gold prices are indeed auto-correlated. acf(diff(log(OilGold$price_gold))) will yield no auto-correlation in gold log-returns.



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