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:20260329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0300
TZOFFSETTO:+0200
TZNAME:EET
DTSTART:20261025T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20260507T110000
DTEND;TZID=Europe/Helsinki:20260507T120000
DTSTAMP:20260709T184459
CREATED:20260417T140800Z
LAST-MODIFIED:20260417T140800Z
UID:18922-1778151600-1778155200@crest.science
SUMMARY:Leopoldo Catania (Aarhus University) ": Autoregressive Models with Non-Causal ARCH Volatility""
DESCRIPTION:Finance-Insurance\nTime: 11.00 am\nDate:07th of May  2026\nRoom 3001 \nLeopoldo Catania (Aarhus University) “: Autoregressive Models with Non-Causal ARCH Volatility” \nAbstract : This paper introduces a novel non-causal (forward-looking) ARCH specification in which conditional heteroskedasticity depends on leads of the process. When observed in calendar time\, this time inversion allows large past shocks to affect the entire conditional distribution rather than only its scale\, as in standard ARCH models. The resulting forward-looking dynamics can generate symmetric bimodal predictive densities\, providing a new interpretation of economic uncertainty. We establish the stochastic properties of autoregressive processes with errors following the proposed non-causal ARCH specification and derive sufficient conditions for consistency and asymptotic normality of an Approximate Quasi–Maximum Likelihood Estimator (AQMLE). A kernel-based estimator of the marginal error distribution and the predictive density is also developed. Simulation results demonstrate good finite-sample performance. An empirical application to monthly CPI data shows that the proposed model captures distributional dynamics more effectively than traditional approaches\, particularly during periods of elevated uncertainty and volatility. \nJoint work : Gabriele Mingoli \n  \n
URL:https://crest.science/event/leopoldo-catania-aarhus-university-autoregressive-models-with-non-causal-arch-volatility/
CATEGORIES:Finance-Insurance,Seminars
ATTACH;FMTTYPE=:
END:VEVENT
END:VCALENDAR