Giacomo Domeniconi

We consider the problem of hedging a European call option under a discrete rebalancing schedule with trades subject to transaction costs. We formulate this as a stochastic optimal control problem aiming to maximize the hedge P&L with the quadratic variation of the P&L as a penalty term. We solve the optimization numerically, using deep stochastic optimal control and deep reinforcement learning when the market follows either the Black-Scholes or a stochastic volatility model. Furthermore, under the Black-Scholes model, we show that delta hedging is not the optimal hedging strategy when penalized for the quadratic variation. Our results show that data-driven methods outperform traditional delta-hedging strategies when accounting for transaction costs and pathwise variability. We observe how these methods are well-suited for multi-step optimization problems and can effectively balance hedging costs over time.

Functional annotation of genes and gene products is a major challenge in the post-genomic era. Nowadays, gene function curation is largely based on manual assignment of Gene Ontology (GO) annotations to genes by using published literature. The annotation task is extremely time-consuming, therefore there is an increasing interest in automated tools that can assist human experts.

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