  This research focuses on the bid optimization problem in the real-time
bidding setting for online display advertisements, where an advertiser, or the
advertiser's agent, has access to the features of the website visitor and the
type of ad slots, to decide the optimal bid prices given a predetermined total
advertisement budget. We propose a risk-aware data-driven bid optimization
model that maximizes the expected profit for the advertiser by exploiting
historical data to design upfront a bidding policy, mapping the type of
advertisement opportunity to a bid price, and accounting for the risk of
violating the budget constraint during a given period of time. After employing
a Lagrangian relaxation, we derive a parametrized closed-form expression for
the optimal bidding strategy. Using a real-world dataset, we demonstrate that
our risk-averse method can effectively control the risk of overspending the
budget while achieving a competitive level of profit compared with the
risk-neutral model and a state-of-the-art data-driven risk-aware bidding
approach.
