BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//CREST - ECPv5.1.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:CREST
X-ORIGINAL-URL:https://crest.science
X-WR-CALDESC:Events for CREST
BEGIN:VTIMEZONE
TZID:Europe/Helsinki
BEGIN:DAYLIGHT
TZOFFSETFROM:+0200
TZOFFSETTO:+0300
TZNAME:EEST
DTSTART:20230326T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0300
TZOFFSETTO:+0200
TZNAME:EET
DTSTART:20231029T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20230601T113000
DTEND;TZID=Europe/Helsinki:20230601T123000
DTSTAMP:20260711T062817
CREATED:20230525T104540Z
LAST-MODIFIED:20230525T104540Z
UID:15010-1685619000-1685622600@crest.science
SUMMARY:Alessandra LUATI (University of Bologna) "On the optimality of score driven models"
DESCRIPTION:Finance & Financial Econometrics: \nTime: 11.30 am\nDate: 1th of June 2023\nRoom 3001 \nAlessandra LUATI (University of Bologna) “On the optimality of score driven models” \nAbstract :Score-driven models have been recently introduced as a general framework to specify time-varying parameters of conditional densities. The score enjoys stochastic properties that make these models easy to implement and convenient to apply in several contexts\, ranging from biostatistics to finance. Score-driven parameter updates have been shown to be optimal in terms of locally reducing a local version of the Kullback–Leibler divergence between the true conditional density and the postulated density of the model. A key limitation of such optimality property is that it holds only locally both in the parameter space and sample space\, yielding to a definition of local Kullback–Leibler divergence that is in fact not adivergence measure. The current paper shows that score-driven updates satisfy stronger optimality properties that are based on a global definition of Kullback–Leibler divergence. In particular\, it is shown thatscore-driven updates reduce the distance between the expected updated parameter and the pseudo-true parameter. Furthermore\, depending on the conditional density and the scaling of the score\, the optimality result can hold globally over the parameter space\, which can be viewed as a generalisation of the monotonicity property of the stochastic gradient descent scheme. Several examples illustrate how the results\nderived in the paper apply to specific models under different easy-to-check assumptions\, and provide a formal method to select the link-function and the scaling of the score.\n(Joint with Paolo Gorgi (VU Amsterdam) et Sacha Lauria (University of Bologna))\n \nOrganizers:\nJean-Michel ZAKOIAN (CREST) \nSponsors:\nCREST \n
URL:https://crest.science/event/alessandra-luati-university-of-bologna-on-the-optimality-of-score-driven-models/
CATEGORIES:Finance-Insurance,Financial Econometrics
ATTACH;FMTTYPE=:
END:VEVENT
END:VCALENDAR