  The 2008 mortgage crisis is an example of an extreme event. Extreme value
theory tries to estimate such tail risks. Modern finance practitioners prefer
Expected Shortfall based risk metrics (which capture tail risk) over
traditional approaches like volatility or even Value-at-Risk. This paper
provides a quantum annealing algorithm in QUBO form for a dynamic asset
allocation problem using expected shortfall constraint. It was motivated by the
need to refine the current quantum algorithms for Markowitz type problems which
are academically interesting but not useful for practitioners. The algorithm is
dynamic and the risk target emerges naturally from the market volatility.
Moreover, it avoids complicated statistics like generalized pareto
distribution. It translates the problem into qubit form suitable for
implementation by a quantum annealer like D-Wave. Such QUBO algorithms are
expected to be solved faster using quantum annealing systems than any classical
algorithm using classical computer (but yet to be demonstrated at scale).
