Tag Info

New answers tagged

0

Analyzing seasonal time series "by hand" is not a good idea because there is a lot of time series machinery developed just for that. A simple example in R can be found here. You can apply clustering if it feels more natural but the main question is whether your model works out of sample.


1

Such an approach is done by the systemic investor blogger in his blog Time Series Matching with Dynamic Time Warping.


0

The approach you describe of looking at the valuation metrics in one period versus the returns in the next is similar to cross-sectional factor models, like Barra, or the Fama-Macbeth procedure. In these methods, instead of looking at the correlation, you do a cross-sectional regression of the returns (or excess returns or alpha) against whatever factors, ...


1

There are two things: First: You have one stock of $B$ (worth \$30) and the calculation tells you to short 1.14 stocks of $A$. Of course you can only short whole stocks. So you would have to decide wether to short 0,1 or 2 stocks. This is a question of contract size, or in this case just size. Second: Usually we speak about hedging in portfolio context. In ...


2

An implied correlation $\rho_i(k_1,k_2)$ is a correlation that matches the $(k_1,k_2)$ tranche price $P_{k_1}^{k_2}$ (usually computed under a gaussian or student t copula) $$ C(k_1,k_2,\rho_i(k_1,k_2)) = P_{k_1}^{k_2} $$ For mezzanine tranches, there can sometimes be two different implied correlations matching the tranche price. A base correlation ...


1

Are you really interested in ranking different indicators, or do you just want to know how you should combine them to make the best predictor possible? Is there any reason you can't use several together? Correlation coefficients would certianly be a reasonable starting point for this. You have two obvious problems that come up if you do it this way: A ...


2

The technique is sometimes referred to as full information maximum likelihood. It is more general than the technique you describe, but it is similar. Basically you start with the data with the longest horizon and get the covariance matrix, then for the data with the next longest horizon you regress them against the data with the longest horizon, finally you ...


-1

Both ways are equivalent (assuming we are talking about net returns, and not forgetting any kind of transaction cost). Remember that returns are percentages: they are calculated as $$ \frac{P_1 - P_0}{P_0}\times 100$$ [where $P_0$ is the price at the beginning of the period and $P_1$ is the price at the end] so it does not matter what currency you quote the ...



Top 50 recent answers are included