## Twelve Dimensional Chess is Stupid

Chess and the Curse of Dimensionality

read more Tue 16 October 2018
by Steven

Chess and the Curse of Dimensionality

read more Wed 28 March 2018
by Steven

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

read more 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 …

read more 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 …