# Tag Info

8

The PortfolioAnalytics package will create weights without reference to current weights, if that's what you want. It should also have much of the reporting that you like from Rmetrics fPortfolio. There is a longer seminar presentation on Portfolioanalytics from 2010's R/Finance conference here: Complex Portfolio Optimization with Generalized Business ...

7

This is the website to the R/Finance conference this year. Tons of great links. http://www.rinfinance.com/agenda/ Brian Peterson's slide (Building and Testing Quantitative Strategy Models in R) mentions Portfolio-Analytics (which I think is based on R/Metrics). And here is a paper based on Portfolio-Analytics. http://cran.r-project.org/web/packages/...

3

It depends a lot on the structure of the ETF, it could be : * In the "terms and conditions" of the (highly possible) total return swap of the fund * Portfolio insurance * Option combination (or cap & floor) I think it's in the swap details, already saw that a few times.

3

The calculation of rebalanced portfolio returns using PerformanceAnalytics functions makes use of what the package authors call "end-of-period" weights. As described in the documentation for Return.portfolio, the rebalancing uses the weights for the last trading day of the period to rebalance the portfolio after the markets close on that day. As an ...

2

If two or more (I(1)) time series are cointegrated, then this means that you can find a linear combination of them that is mean-reverting. Thus, if you create a portfolio with weights that are proportional to this linear combination, then the portfolio returns will also be mean-reverting. There is a large literature on cointegration and asset prices and ...

2

First of all, AM is always greater than or equal to GM $$x_1 + x_2 + ... + x_n \geq \sqrt[n]{x_1x_2...x_n}~\forall x_i \geq 0$$ You can prove it by induction from $\frac{x_1 + x_2}{2} \geq \sqrt{x_1x_2}$ or put $f(x) = \ln(x), p_i = \frac{1}{n}$ to Jensen's inequality to get it. The equality holds when $x_1 = x_2 = ... = x_n$. For author 1 and 2, We ...

2

I believe the assumption here in the first quote is that returns are either strictly positive or strictly negative and the authors are comparing the effect of volatility on geometric returns to arithmetic. The issue of diversification benefits have little to do with this difference as opposed to a time varying covariance matrix. The benefits of ...

2

You can't really combine the assets' log returns. You should calculate percentage returns for the three assets. Then at each time step, the portfolio's total return is: $r(i) = 0.5 \times \text{asset1_return}(i) + 0.25 \times \text{asset2_return}(i) + 0.25 \times \text{asset3_return}(i)$ Once you've calculated the time series of the portfolio's returns, ...

2

I haven't completely followed your question. Are you asking about the optimal rebalance frequency in the presence of t-costs and a changing alpha signal? Usually you would include the t-cost estimate as another term in your optimization (to constrain the weights). This acts to limit the trading "aggressiveness". In other words, you can think of the ...

1

I think this entire complicated-sounding problem can be shoe-horned into a traditional mean-variance optimization. However, there are multiple embedded sub-problems, each worthy of specific attention (this is why I recommend you split the question up further into multiple smaller questions). Your expected returns can and should be updated as frequently as ...

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