Emmanuel Fragniere, J. Gondzio and Xi Yang
Abstract
Operational risks are defined as risks of human
origin. Unlike financial risks that can be handled in a financial
manner (e.g. insurances, savings, derivatives), the treatment of
operational risks calls for a ``managerial approach". Consequently,
we propose a new way of dealing with operational risk, which relies
on the well known aggregate planning model. To illustrate this idea,
we have adapted this model to the case of a
back office of a bank specializing in the trading of derivative
products. Our contribution corresponds to several improvements
applied to stochastic programming techniques. First, the model
is transformed into a multistage
stochastic program in order to take into account the randomness
associated with the volume of transaction demand and with the
capacity of work provided by qualified and non-qualified
employees. Second, as advocated by Basel II, we calculate the
probability distribution based on Bayesian Network to circumvent
the difficulty to obtain data in operations. Third, we go a
step further by relaxing the traditional assumption in stochastic
programming that imposes a strict independence between the decision
variables and the random elements. Comparative results show that
these improved stochastic programming models tend generally
to allocate more human expertise in order to hedge operational risks.
Finally, we employ the dual solutions of the stochastic programs
to detect periods and nodes that are at risk in terms of expertise
availability.
Key words: Operational Risk, Stochastic Programming, Aggregate Planning Model