On February 14th from 12:00 to 13.00 Giuseppe Buccheri from Scuola Normale Superiore will give a seminar on "A General Class of Score-Driven Smoother".

The event will take place in room B, first floor, building B.

Abstract:
We show that, in the steady-state, Kalman filter and smoother recursions can be re-parameterized in terms of the score of the conditional density and the Fisher matrix. Since in the new representation the predictive filter has the form of score-driven models, we introduce, by analogy, a score-driven update filter (SDU) and smoother (SDS). In this new framework, we can recover smoothed estimates of observation-driven models and assess filtering uncertainty. We test both empirically and through simulations the advantages of SDU and SDS over standard score-driven filters and exact simulation-based methods. Ongoing research on applications to financial risk management will be illustrated.