We derive sharp probability bounds on the tails of a product of symmetric nonnegative random variables using only information about their first two moments. If the covariance matrix of the random variables is known exactly, these bounds can be computed numerically using semidefinite programming. If only an upper bound on the covariance matrix is available, the probability bounds on the right tails can be evaluated analytically. The bounds under precise and imprecise covariance information coincide for all left tails as well as for all right tails corresponding to quantiles that are either sufficiently small or sufficiently large. We also prove that all left probability bounds reduce to the trivial bound 1 if the number of random variables in the product exceeds an explicit threshold. Thus, in the worst case, the weak-sense geometric random walk defined through the running product of the random variables is absorbed at 0 with certainty as soon as time exceeds the given threshold. The techniques devised for constructing Chebyshev bounds for products can also be used to derive Chebyshev bounds for sums, maxima, and minima of non-negative random variables.
Napat Rujeerapaiboon recently joined the Department of Industrial Systems Engineering and Management, National University of Singapore, in 2018. He holds a PhD degree in Risk Analytics and Optimisation from École Polytechnique Fédérale de Lausanne. His research interests are in the areas of robust and distributionally robust optimisation, data-driven optimisation, and risk analytics.
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