St. Petersburg Portfolio Games
Abstract
We investigate the performance of the constantly rebalanced portfolios, when the random vectors of the market process X _ i are independent, and each of them distributed as ( X ^(1), X ^(2), ..., X ^( d ), 1), d ≥ 1, where X ^(1), X ^(2), ..., X ^( d ) are nonnegative iid random variables. Under general conditions we show that the optimal strategy is the uniform: (1/ d , ..., 1/ d , 0), at least for d large enough. In case of St. Petersburg components we compute the average growth rate and the optimal strategy for d = 1,2. In order to make the problem non-trivial, a commission factor is introduced and tuned to result in zero growth rate on any individual St. Petersburg components. One of the interesting observations made is that a combination of two components of zero growth can result in a strictly positive growth. For d ≥ 3 we prove that the uniform strategy is the best, and we obtain tight asymptotic results for the growth rate.
Cite
Text
Györfi and Kevei. "St. Petersburg Portfolio Games." International Conference on Algorithmic Learning Theory, 2009. doi:10.1007/978-3-642-04414-4_11Markdown
[Györfi and Kevei. "St. Petersburg Portfolio Games." International Conference on Algorithmic Learning Theory, 2009.](https://mlanthology.org/alt/2009/gyorfi2009alt-st/) doi:10.1007/978-3-642-04414-4_11BibTeX
@inproceedings{gyorfi2009alt-st,
title = {{St. Petersburg Portfolio Games}},
author = {Györfi, László and Kevei, Péter},
booktitle = {International Conference on Algorithmic Learning Theory},
year = {2009},
pages = {83-96},
doi = {10.1007/978-3-642-04414-4_11},
url = {https://mlanthology.org/alt/2009/gyorfi2009alt-st/}
}