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"It's compliated" because the trading strategy performance will depend on the data which is most likely serially correlated. So you want to look into bootstrap approaches for time series such as the block bootstrap, or the wild bootstrap. Another approach would be to look into 'random portfolios' or an approximation thereof. The basic idea is to test how ...

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R package TTR has rolling window algorithms and understands day counting etc. It stands on the shoulders of xts (which extends zoo) and quantmod

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Unless you're doing this as a purely educational exercise, it looks like you may be overcomplicating things. Sharpe ratios follow a student's t distribution. You can thus use standard approaches to test hypotheses or create confidence intervals for each Sharpe estimate. A quick google turned up this paper that addresses topics similar to what you're ...

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Your answer is correct. You included .5 in the exponent and therefore got an annualized result. 6.118% divided by 2 is your bootstrapped 6 month spot rate.

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You think you make a mistake where you actually don´t make one. The exercise is just like it is. Resulting in $$r_{6m}>r_{12m}$$ The difference in your both answers, based on the same rounding, lays in the different basis for the logarithm. $$r_{6m} = - 2 \log_e \left( \frac{99.8-102.5 e^{-r_{1y}} }{2.5} \right) = \textbf{6.118%}$$ r_{6m} = - 2 \log_{...

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Actually, it is not very clear the legislation. However, from some slides that EIOPA used in a conference I tried to build back their computation and what I found is that: 1) you simulate 10k with lenght T, so you have a matrix 10k x T 2) you sum over the Ts so you get a vector 10k x 1 3) you have to go in the risk neutral world so from every element ...

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The correct procedure for parametric bootstrap is: 1) fit the data with a distribution of the parametric family (normal, Student's t, etc.; you should choose the one that fits the data in the best way, using some criteria to choose, such as Akaike Information Criteria or others); 2) draw n random samples from the fitted distribution, and estimate the ...

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I am not sure you have the same definition of bootstrap than myself: bootstrap is mainly a way to estimate the variance of estimators when you do not have a closed form formula to obtain it directly (thanks to Efron's theorem). It means if you want the variance of your estimator of returns or covariance, you could use bootstrapping. Bad news: if you ...

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