gilgamath

## Another Confidence Limit for the Markowitz Signal Noise ratio

Wed 28 March 2018 by Steven

Another confidence limit on the Signal Noise ratio of the Markowitz portfolio.

## Markowitz Portfolio Covariance, Elliptical Returns

Mon 12 March 2018 by Steven E. Pav

In a previous blog post, I looked at asymptotic confidence intervals for the Signal to Noise ratio of the (sample) Markowitz portfolio, finding them to be deficient. (Perhaps they are useful if one has hundreds of thousands of days of data, but are otherwise awful.) Those confidence intervals came from revision four of my paper on the Asymptotic distribution of the Markowitz Portfolio. In that same update, I also describe, albeit in an obfuscated form, the asymptotic distribution of the sample Markowitz portfolio for elliptical returns. Here I check that finding empirically.

Suppose you observe a $$p$$ vector of returns drawn from an elliptical distribution with mean $$\mu$$, covariance $$\Sigma$$ and 'kurtosis factor', $$\kappa$$. Three times the kurtosis factor is the kurtosis of marginals under this assumed model. It takes value $$1$$ for a multivariate normal. This model of returns is slightly more realistic than multivariate normal, but does not allow for skewness of asset returns, which seems unrealistic.

Nonetheless, let $$\hat{\nu}$$ be the Markowitz portfolio built on a sample of $$n$$ days of independent returns:

$$\hat{\nu} = \hat{\Sigma}^{-1} \hat{\mu},$$

where $$\hat{\mu}, \hat{\Sigma}$$ are the regular 'vanilla' estimates of mean and covariance. The vector $$\hat{\nu}$$ is, in a sense, over-corrected, and we need to cancel out a square root of $$\Sigma$$ (the population value). So we will consider the distribution of $$Q \Sigma^{\top/2} \hat{\nu}$$, where $$\Sigma^{\top/2}$$ is the upper triangular Cholesky factor of $$\Sigma$$, and where $$Q$$ is an orthogonal matrix ($$Q Q^{\top} = I$$), and where $$Q$$ rotates $$\Sigma^{-1/2}\mu$$ onto $$e_1$$, the first basis vector:

$$Q \Sigma^{-1/2}\mu = \zeta e_1,$$

where $$\zeta$$ is the Signal to Noise ratio of the population Markowitz portfolio: $$\zeta = \sqrt{\mu^{\top}\Sigma^{-1}\mu} = \left\Vert … ## A Lack of Confidence Interval Thu 15 February 2018 by Steven E. Pav For some years now I have been playing around with a certain problem in portfolio statistics: suppose you observe \(n$$ independent observations of a $$p$$ vector of returns, then form the Markowitz portfolio based on those returns. What then is the distribution of what I call the 'signal to noise ratio' of that Markowitz portfolio, defined as the true expected return divided by the true volatility. That is, if $$\nu$$ is the Markowitz portfolio, built on a sample, its 'SNR' is $$\nu^{\top}\mu / \sqrt{\nu^{\top}\Sigma \nu}$$, where $$\mu$$ is the population mean vector, and $$\Sigma$$ is the population covariance matrix.

This is an odd problem, somewhat unlike classical statistical inference because the unknown quantity, the SNR, depends on population parameters, but also the sample. It is random and unknown. What you learn in your basic statistics class is inference on fixed unknowns. (Actually, I never really took a basic statistics class, but I think that's right.)

Paulsen and Sohl made some progress on this problem in their 2016 paper on what they call the Sharpe Ratio Information Criterion. They find a sample statistic which is unbiased for the portfolio SNR when returns are (multivariate) Gaussian. In my mad scribblings on the backs of envelopes and scrap paper, I have been trying to find the distribution of the SNR. I have been looking for this love, as they say, in all the wrong places, usually hoping for some clever transformation that will lead to a slick proof. (I was taught from a young age to look for slick proofs.)

Having failed that mission, I pivoted to looking for confidence intervals for the SNR (and maybe even prediction intervals on the out-of-sample Sharpe ratio of the in-sample Markowitz portfolio). I realized that some of the work I had done …

## geom cloud.

Thu 21 September 2017 by Steven E. Pav

I wanted a drop-in replacement for geom_errorbar in ggplot2 that would plot a density cloud of uncertainty. The idea is that typically (well, where I work), the ymin and ymax of an errorbar are plotted at plus and minus one standard deviation. A 'cloud' where the alpha is proportional to a normal density with the same standard deviations could show the same information on a plot with a little less clutter. I found out how to do this with a very ugly function, but wanted to do it the 'right' way by spawning my own geom. So the geom_cloud.

After looking at a bunch of other ggplot2 extensions, some amount of tinkering and hair-pulling, and we have the following code. The first part just computes standard deviations which are equally spaced in normal density. This is then used to create a list of geom_ribbon with equal alpha, but the right size. A little trickery is used to get the scales right. There are three parameters: the steps, which control how many ribbons are drawn. The default value is a little conservative. A larger value, like 15, gives very smooth clouds. The se_mult is the number of standard deviations that the ymax and ymin are plotted at, defaulting to 1 here. If you plot your errorbars at 2 standard errors, change this to 2. The max_alpha is the alpha at the maximal density, i.e. around y.

# get points equally spaced in density
equal_ses <- function(steps) {
xend <- c(0,4)
endpnts <- dnorm(xend)
geom_cloud <- function(mapping …