BEGIN:VCALENDAR
VERSION:2.0
METHOD:PUBLISH
CALSCALE:GREGORIAN
PRODID:-//WordPress - MECv5.18.0//EN
X-ORIGINAL-URL:https://canssiontario.utoronto.ca/
BEGIN:VEVENT
UID:MEC-7fc7b7979ce9c02bb7a36e5500726053@canssiontario.utoronto.ca
DTSTART:20231207T190000Z
DTEND:20231207T200000Z
DTSTAMP:20230301T220400Z
CREATED:20230301
LAST-MODIFIED:20231128
SUMMARY:Statistical Sciences ARES: Jean-François Bégin
DESCRIPTION:\nJoin us at the Statistical Sciences Applied Research and Education Seminar (ARES) with\n\n\n\nJean-François Bégin\n\n\n\nAssociate ProfessorDepartment of Statistics and Actuarial ScienceSimon Fraser University\n\n\n\nFree Hybrid (In-person/Online) Event | Registration Required\n\n\n\nTalk Title\n\n\n\nNew Developments in Economic Scenario Generator Modelling\n\n\n\nAbstract\n\n\n\nOver the last 40 years, various frameworks have been proposed to model economic and financial variables. These frameworks—called economic scenario generators—are comprehensive models that allow actuaries and risk managers to grasp the long-term uncertainty underlying financial market values and economic variables. Their primary aim is to generate a set of future scenarios covering a range of plausible outcomes. The main end-users of these frameworks are pension, life insurance, and banking practitioners. In this presentation, we explore two important modelling questions relating to economic scenario generators and their use: model uncertainty and model averaging.\n\n\n\nGiven today’s knowledge and technology, one could construct complicated frameworks to fit the data better. However, this process would lead to highly parametrized models, which goes against the idea of parsimony in statistics—the desire to explain phenomena using fewer parameters. The first part of this presentation investigates this tradeoff: would a more complex economic scenario generator perform better, or would a simple model accomplish the same performance? To answer this question, we propose a new complex generator that nests versions of well-known actuarial frameworks. We then assess whether complex models perform better than simple models using both in- and out-of-sample analyses.\n\n\n\nSecond, we investigate model averaging. This strategy has been used extensively in data-heavy domains such as weather forecasting and in fields where forecasts come from diverse methods and datasets, such as election polls. In our context, we answer whether we can create better, more reliable economic scenario generators by combining them. This part of the presentation considers a recently proposed Bayesian averaging technique and data from multiple countries.\n\n\n\nSpeaker Profile\n\n\n\nDr. Jean-François Bégin is an Associate Professor of Actuarial Science in the Department of Statistics and Actuarial Science at Simon Fraser University in British Columbia. He is a specialist in financial modelling as well as statistical and mathematical applications to finance and insurance. Before joining Simon Fraser University, he received his PhD from HEC Montréal in Financial Engineering. He is also a Fellow of both the Society of Actuaries and the Canadian Institute of Actuaries. Over the past few years, his research program focused on the construction of complex models for long-term economic predictions, the understanding and management of credit risk, the modelling of option prices, and the development of sustainable retirement solutions and designs.\n
URL:https://canssiontario.utoronto.ca/event/ares-jean-francois-begin/
ORGANIZER;CN=CANSSI Ontario:MAILTO:esther.berzunza@utoronto.ca
CATEGORIES:Applied Research and Education Seminar​
LOCATION:Southam Hall, Library Road, Ottawa, ON
ATTACH;FMTTYPE=image/jpeg:https://canssiontario.utoronto.ca/wp-content/uploads/2023/03/Bégin-Jean-François.jpeg
END:VEVENT
END:VCALENDAR