The Gerber
Statistic
The Gerber
Statistic
Hudson Bay seeks research opportunities with academia, through which we can share our unique portfolio construction philosophy.
Hudson Bay has co-authored a series of white papers with Dr. Harry Markowitz, winner of the Nobel Memorial Prize in Economic Sciences, in connection with his pioneering work in Modern Portfolio Theory. This collaboration reflects our belief that Hudson Bay’s investment process is an enhanced application of Dr. Markowitz’s framework in a multi-asset, multi-strategy alpha portfolio hedge fund environment.
Hudson Bay Tribute
to Dr. Markowitz
07/22/2024
By adjusting the maturity profile of its debt issuance, Treasury is dynamically managing financial conditions and through them, the economy. We dub this novel tool “activist Treasury issuance,” or ATI. This approach may become a regular policy tool, influencing political business cycles and driving market conditions.
02/01/2022
The purpose of this article is to introduce the Gerber statistic, a robust co-movement measure for covariance matrix estimation for the purpose of portfolio construction. The Gerber statistic extends Kendall’s Tau by counting the proportion of simultaneous co-movements in series when their amplitudes exceed data-dependent thresholds.
04/15/2021
Can artificial intelligence replace human intelligence in investing? Some people are betting yes, believing that quantitative analysis outperforms human analysis — that crunching numbers, machine learning, algorithms, backtesting, prediction models and other forms of computational wizardry will inevitably obsolete the need for human judgment when assessing risks and rewards in investing.
06/21/2019
Optimizing a portfolio to reduce exposure to downside risk can be difficult, and usually involves third or higher order statistical moments of the portfolio's return distribution. Mean-semivariance optimization simplifies this problem by using only the first two moments of the distribution and by penalizing returns below a predetermined reference. Although this penalty introduces a nonlinearity, mean-semivariance optimization can be performed easily and efficiently using the critical line algorithm provided that the covariance matrix is estimated from an historical record of asset returns. In practice, this proviso is not restrictive. This chapter reviews the theory of the critical line algorithm and presents sample computer code for applying the algorithm to mean-variance and mean-semivariance portfolio optimization. It also reviews a method for finding the efficient mean-semivariance portfolio for any given feasible desired expected portfolio return.
02/21/2019
We introduce a principal components model for securities’ returns. The components are non-normal, exhibiting significant skewness and kurtosis. The model can explain a large proportion of the variance of the securities’ returns with only one or two components.
Third and higher-order components individually contribute so little that they can be considered to be noise terms.
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