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The Blundell Ward filter is a fairly commonly used method for removing first order autocorrelation see; http://www.scribd.com/doc/142748206/Impact-of-Auto-correlation-on-Expected-Maximum-Drawdown#scribd


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If you assume first order correlation and stationnary assumptions and no autocorrelation between true returns and estimated returns, the answer is the following Denote by $ R^e $ the estimated return, $R^t $ the true return and $ \rho $ the autocorrelation coefficient By assumptions, you have that $ R^e(t)= \rho R^e(t-1) + (1-\rho ) R^t(t) $ $ Cov( ...


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got my answer myself, and the answer is: That depends, but people mostly use close price. http://www.macroption.com/calculating-moving-average-prices/


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In mathematics and statistics, a stationary process (or strict(ly) stationary process or strong(ly) stationary process) is a stochastic process whose joint probability distribution does not change when shifted in time. Consequently, parameters such as the mean and variance, if they are present, also do not change over time and do not follow any trends.


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I am struggling a little to understand your question, but I suspect I know where you're running into trouble, because it is a question we frequently use on students to test their understanding of how R estimates ARIMA models. When you estimate an ARIMA model in R, the output is provided in what we often call Normal form. For the specific case of an AR(1) ...


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GARCH models are essentially white noise models with some time dependency. The reason GARCH models are used is because they have a lot of nice properties. The main being that the Conditional Volatility is time-dependent. This means that volatility can cluster. It's true that conditional vol will regress towards "normality" as a random walk process with ...


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You can see fairly quickly that an exact answer to this question is not going to be feasible because your functional transformation is to take the square root of $\sigma_t^2$, and the square root function has a countably infinite number of derivatives. This implies that a Taylor expansion is going to leave us with a countably infinite number of terms, most ...


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"conditional volatilities from GARCH models are not stochastic since at time $t$ the volatility is completely pre-determined (deterministic) given previous values"-https://en.wikipedia.org/wiki/Stochastic_volatility $\sigma_t$ is still a random variable in the sense that it has an unconditional distribution. However, this unconditional distribution is not ...


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The way to do gradual position entry and exit is to use multiple trend following rules, each of which is responsible for managing a part of the available capital. Only if all the trading rules agree will 100% of the capital be deployed. As a simple example, suppose you have three rules. The first rule is based on 10 day momentum; this rule produces a score ...


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Here is a collection of papers. The general idea is that the market has investor classes that share different expectations. When in bubble territory, many investors generally agree that assets are overpriced, but they still invest in expectation of more investors entering the market (the greater fools). There are also sophisticated investors who know assets ...


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You can do it with 6 data points. However two caveats: 1) With returns no evenly spaced apart you need some adjustments. This topic might help. 2) With only six data points you will get huge standard deviations, so almost sure your statistics will not be significant and you cannot do anything with them.



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