February 25, 2020
3:00PM - 4:00PM
209 W Eighteenth Ave (EA), Room 170
Add to Calendar
2020-02-25 16:00:00
2020-02-25 17:00:00
Recruitment Talk - Lu Yang
Title: Dependence Modeling of Mixed Insurance Claim Data
Speaker: Lu Yang - University of Amsterdam, Department of Economics and Business
Abstract: Multivariate claim data are common in insurance applications, e.g. claims of each policyholder for different types of insurance coverages. Understanding the dependencies among such multivariate risks is essential for the solvency and profitability of insurers. Effectively modeling insurance claim data is challenging due to their special complexities. At the policyholder level, claims data usually follow a two-part mixed distribution: a probability mass at zero corresponding to no claim and an otherwise positive claim from a skewed and long-tailed distribution. Copula models are often employed in order to simultaneously model the relationship between outcomes and covariates while flexibly quantifying the dependencies among the different outcomes. However, due to the mixed data feature, specification of copula models has been a problem. We fill this gap by developing a consistent nonparametric copula estimator for mixed data. Under our framework, both the models for the i) marginal relationship between covariates and claims and ii) dependence structure between claims can be chosen in a principled way. We show the uniform convergence of the proposed nonparametric copula estimator. Using the claim data from the Wisconsin Local Government Property Insurance Fund, we illustrate that our nonparametric copula estimator can assist analysts in identifying important features of the underlying dependence structure, revealing how different claims or risks are related to one another.
Seminar Link with Department of Statistics
209 W Eighteenth Ave (EA), Room 170
OSU ASC Drupal 8
ascwebservices@osu.edu
America/New_York
public
Date Range
Add to Calendar
2020-02-25 15:00:00
2020-02-25 16:00:00
Recruitment Talk - Lu Yang
Title: Dependence Modeling of Mixed Insurance Claim Data
Speaker: Lu Yang - University of Amsterdam, Department of Economics and Business
Abstract: Multivariate claim data are common in insurance applications, e.g. claims of each policyholder for different types of insurance coverages. Understanding the dependencies among such multivariate risks is essential for the solvency and profitability of insurers. Effectively modeling insurance claim data is challenging due to their special complexities. At the policyholder level, claims data usually follow a two-part mixed distribution: a probability mass at zero corresponding to no claim and an otherwise positive claim from a skewed and long-tailed distribution. Copula models are often employed in order to simultaneously model the relationship between outcomes and covariates while flexibly quantifying the dependencies among the different outcomes. However, due to the mixed data feature, specification of copula models has been a problem. We fill this gap by developing a consistent nonparametric copula estimator for mixed data. Under our framework, both the models for the i) marginal relationship between covariates and claims and ii) dependence structure between claims can be chosen in a principled way. We show the uniform convergence of the proposed nonparametric copula estimator. Using the claim data from the Wisconsin Local Government Property Insurance Fund, we illustrate that our nonparametric copula estimator can assist analysts in identifying important features of the underlying dependence structure, revealing how different claims or risks are related to one another.
Seminar Link with Department of Statistics
209 W Eighteenth Ave (EA), Room 170
Department of Mathematics
math@osu.edu
America/New_York
public
Title: Dependence Modeling of Mixed Insurance Claim Data
Speaker: Lu Yang - University of Amsterdam, Department of Economics and Business
Abstract: Multivariate claim data are common in insurance applications, e.g. claims of each policyholder for different types of insurance coverages. Understanding the dependencies among such multivariate risks is essential for the solvency and profitability of insurers. Effectively modeling insurance claim data is challenging due to their special complexities. At the policyholder level, claims data usually follow a two-part mixed distribution: a probability mass at zero corresponding to no claim and an otherwise positive claim from a skewed and long-tailed distribution. Copula models are often employed in order to simultaneously model the relationship between outcomes and covariates while flexibly quantifying the dependencies among the different outcomes. However, due to the mixed data feature, specification of copula models has been a problem. We fill this gap by developing a consistent nonparametric copula estimator for mixed data. Under our framework, both the models for the i) marginal relationship between covariates and claims and ii) dependence structure between claims can be chosen in a principled way. We show the uniform convergence of the proposed nonparametric copula estimator. Using the claim data from the Wisconsin Local Government Property Insurance Fund, we illustrate that our nonparametric copula estimator can assist analysts in identifying important features of the underlying dependence structure, revealing how different claims or risks are related to one another.