# Tag Info

5

It appears that you are re-running the regression with each new data point. Instead, you should use an update/online formula (see an excellent answer by the famous Dr. Huber at stats.se). You can find an implementation in the R package biglm. If it doesn't have all the features you need (no windowing out of old data) you can at least adapt it and use it ...

5

Step 1: Get your data from SQL into R -> http://www.r-bloggers.com/?s=SQL Step 2: Run your analysis/optimizations like -> http://www.r-bloggers.com/portfolio-optimization-in-r-part-1/ or http://blog.streeteye.com/blog/2012/01/portfolio-optimization-and-efficient-frontiers-in-r/ or via RMetrics: ...

3

There are a lot of code in Eric Zivots recent class in computational finance. http://spark-public.s3.amazonaws.com/compfinance/R%20code/portfolio.r http://spark-public.s3.amazonaws.com/compfinance/R%20code/testport.r http://spark-public.s3.amazonaws.com/compfinance/R%20code/rollingPortfolios.r Also, you can google some slides in his class where he ...

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This pdf says on page two that the paper was never published. I don't know the reason but you could try to mail the authors of the papers were the article is mentioned. Since it was never published it might be less encumbered by copyright than usual.

2

first of all, there is nothing wrong in a currency-only portfolio to be dollar long and short the cross currencies. If that is what your model predicts and if you have a high confidence in the predictions and standard error being low then why do you have issues being dollar long and short the other currencies? You can implement boundary conditions, such as ...

2

A partial answer… For Black-Litterman, an equilibrium no arbitrage condition such as interest rate parity suggests investors would be indifferent between investing in either the foreign or domestic currency. Thus, you could use a constant zero return for all currencies in your opportunity set as the equilibrium model. What this ultimately would do is act ...

2

In your set up where you have just two assets, risky asset and risk-free asset, where weight of risky asset is w1, and consequently weight of risk-free asset is 1 - w1: w1 = 1, => You invest all your money in the risky asset. w1 = 0, => You invest all your money in the risk-free asset 0 < w1 < 1, => You invest some of your money in the risky ...

2

From a general point of view and to answer directly to your originial question, you should only have to modify the inputs to the MATLAB function you refer to. As a matter of fact, fmincon is an optimizer looking to process a broad variety of problems as explained in the documentation: fmincon attempts to find a constrained minimum of a scalar function of ...

2

You are not doing anything wrong. You just need to multiply the absolute return by the currency conversion factor. Example: You trade 200,000,000 yen notional and generate a return of 16% on that notional, then simply multiply 32,000,000 jpy gain by your conversion factor 0.0126 to yield a return of 403,200 USD. The return of 16% was generated on the ...

2

One approach is to use an exponential utility function: $U(x) = -e^{-\lambda x}$. Here, $\lambda$ records what is known as the absolute risk aversion. Exponential utility functions are nice because they have a wealth independence property (of course, this may be seen as a drawback). As we will see below, the initial capital $X$ plays no part in the ...

2

You should have a look at chapter 8 (p. 261ff.) of Hedge Fund Market Wizards by Jack D. Schwager Excerpt from there (but it is much more detailed in the book): Perhaps the most potent risk control Platt employs in BlueCrest’s discretionary strategy is maintaining an extremely tight rein on what a trader can lose before capital is withdrawn. A mere 3 ...

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There is a formula for calculating ES from a normal distribution. There is also a formula for ES of arbitrary distributions using a Cornish-Fisher expansions (easy for univariate processes but frustrating for multivariate). However, the most common approach is a scenario representation of the distribution. This could include using the historical distribution ...

2

Let's start by replacing $\sigma$ by its estimator formula $\sigma^2=\frac{1}{n}\sum^n_{i=1}(x_i-\mu)^2$. Now, by replacing $\mu$ by its estimator $\mu=\frac{1}{n}\sum^n_{i=1}x_i$ in the formula for the variance we obtain: $\sigma^2=\frac{1}{2n^2}\sum^n_{j=1}\sum^n_{i=1}(x_i-x_j)^2$. For the individual asset, the variance will write ...

2

I think some some terminology got mixed up here. Let $r_t$, $t=1,\ldots,T$ be a series of iid excess returns with the estimated mean excess return $\bar{r}= \sum_{t=1}^Tr_t$. Then the Stutzer Index $S$ is defined as $S=\frac{|\bar{r}|}{\bar{r}}\sqrt{2I_p}$ with $I_p$ being the "Stutzer Information Statistic", $I_p=\max_\theta -\log(\frac{1}{T}\sum_{t=1}^T ... 1 In mean-variance analysis, you combine different assets to minimize variance and maximize expected return. The hyperbola is not a function of the number of assets, but of their mean and variance. If the efficient frontier where a tangent to the y-axis (which can't be) or nearly a tangent, that would mean you would have almost zero portfolio-variance, which ... 1 I perform this kind of analysis using the risk contribution concept. I understand from this post that your already know about the contributions, but let's just restate the idea here for the sake of completeness. We have a portfolio of$n$assets with allocation$w \in \mathbb{R}^n$and volatility$\sigma_P(w)$. The marginal risk contribution of asset$i$... 1 I did some calculations in mathematica in the 3 asset case. Assume we have exposures$w_i,i=1,2,3$and volatilities$\sigma_i,i=1,2,3$and correlations$\rho_{1,2},\rho_{1,3},\rho_{2,3}$. Let's assume$\sigma_1=\sigma_2=\sigma_3=\sigma$for some arbitrary positive$\sigma$. For the weights we assume$w_2=w_3 = 0.5$and we have a short in asset 1 of$w_1 = ...

1

You should definitely check out the Virtual Stock Exchange Games* by Marketwatch it provides simple interface, and many options for the rules of the game. Its instantly online, free, and uses real-time prices, but it only allows trading NASDAQ stocks, as far as I know. These games are meant to be played by students, and thought, so I hope it fits your ...

1

I'm currently also using daily returns which I want to annualize. This is my approach: For every month, I calculate the simple return using the formula: (end-of-month closing price / beginning-of-month closing price) - 1. I use the Excel formula somproduct(geomean(A1:A12+1)-1) to find the monthly compounded return. Finally, I annualize the result of step 2 ...

1

Generally I would annualize risk and returns even when an asset's returns/general time series (ts) does not span over the full year So, both, FB and G present risk and return over the past year. For risk and return that is calculated over longer periods I would not include an asset in the portfolio of which you have no ts available to measure risk and ...

1

The term in sample and out of sample are commonly used in any kind of optimization or fitting methods (MVO is just a particular case). When you make the optimization, you compute optimal parameters (usually the weights of the optimal portfolio in asset allocation) over a given data sample, for example, the returns of the securities of the portfolio for the ...

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I do not think they are directly applicable to MVO because inherently you always model the efficient frontier or asset selection on in-sample data and the result is measured out-of-sample. You can't say, "hey I model it in-sample over 2005 data and then I measure the performance of the portfolio over 2006 data and compare that with results derived from 2010 ...

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The controversy surrounding commodity futures flows from Gorton and Rouwenthorst (2004). The authors show an equal-weight portfolio of long positions in commodity futures provides a Sharpe ratio greater than the one earned by holding a cap-weighted portfolio of U.S. stocks (beginning in the 1950's through 2004 or so). In essence, why should holding a ...

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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 ...

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