Grant: Discovery Grants Program - Individual
HEC Montréal
$19,000.00
Extreme values and robustness
FY 2011-12
Summary
The stochastic modeling of extremes is important to many fields. Estimation of the incidence and severity of floods, large winds, large precipitations (rain, snow, or ice), and so on are essential to engineering when many structural designs rely on data-based methodologies for the estimation of design loads. Underestimation of probable extreme loads leads to catastrophic structural failure; recall the state of the transmission towers after the Ice Storms.
This area of modeling is also important in insurance and finance. Insurance companies face probable large (catastrophic) claims and a company's solvency depends critically on the size and the frequency of large claims. The issue is very real with recent environmental disasters such as Hurricane Andrew, the Mississippi floods, the 1998 Ice Storms, and Hurricane Katrina causing havoc and eventual financial failures in the insurance industry. On the financial markets, large price movements are rare, but important. We recall the October 1987 market drop, and we have yet to find the bottom in the October 2008 drop. Increases in volatility of daily stock market indices have attracted much attention from both practitioners and researchers. Companies (and individuals) have taken on huge losses due to unexpected market moves. We need accurate measures of market and insurance risks and that requires accurate modeling of the distribution of large losses and claims.
Many agree that there is a lack of practical statistical methodology in this area. Some progress has been made in the last few years: more realistic models accounting for non-stationarity and correlation; modest multivariate models making better use of available data; Bayesian analyses incorporating ''expert'' knowledge; and robust models identifying and downweighting ''small'' extremes (reducing underestimation). However, there is still a long way to go. The purpose of the proposed research is to further the progress made by these recent methodologies and develop new methods.
Program
Discovery Grants Program - Individual
Recipient
Location
Quebec, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
170049-2009
Name nom
Dupuis, Debbie
Department département
Méthodes quantitatives de gestion, Service de l'enseignement des
Organization id
65
Institution établissement
HEC Montréal
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2011
Competition year année de concours
2009
Award amount
19000
Program id
RGPIN
Program nane en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
14
Committee name en
Statistical Sciences
Committee name fr
Statistique
Area of application code
1206
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Engineering
Area of application fr
Génie
Research subject code
3009
Research subject group en
Statistics and probability
Research subject group fr
Statistique et probabilité
Research subject en
Parametric inference
Research subject fr
Inférence paramétrique
Partie
3
Nb partie
5
Application title
Extreme values and robustness
Keyword
NSERC
Application summary
The stochastic modeling of extremes is important to many fields. Estimation of the incidence and severity of floods, large winds, large precipitations (rain, snow, or ice), and so on are essential to engineering when many structural designs rely on data-based methodologies for the estimation of design loads. Underestimation of probable extreme loads leads to catastrophic structural failure; recall the state of the transmission towers after the Ice Storms.
This area of modeling is also important in insurance and finance. Insurance companies face probable large (catastrophic) claims and a company's solvency depends critically on the size and the frequency of large claims. The issue is very real with recent environmental disasters such as Hurricane Andrew, the Mississippi floods, the 1998 Ice Storms, and Hurricane Katrina causing havoc and eventual financial failures in the insurance industry. On the financial markets, large price movements are rare, but important. We recall the October 1987 market drop, and we have yet to find the bottom in the October 2008 drop. Increases in volatility of daily stock market indices have attracted much attention from both practitioners and researchers. Companies (and individuals) have taken on huge losses due to unexpected market moves. We need accurate measures of market and insurance risks and that requires accurate modeling of the distribution of large losses and claims.
Many agree that there is a lack of practical statistical methodology in this area. Some progress has been made in the last few years: more realistic models accounting for non-stationarity and correlation; modest multivariate models making better use of available data; Bayesian analyses incorporating ''expert'' knowledge; and robust models identifying and downweighting ''small'' extremes (reducing underestimation). However, there is still a long way to go. The purpose of the proposed research is to further the progress made by these recent methodologies and develop new methods.
