Add to ORKGIn this paper we construct a stochastic model and derive approximation formulae to estimate the standard error of prediction under the loss ratio approach of assessing premium liabilities. We focus on the future claims component of premium liabilities and examine the weighted and simple average loss ratio estimators. The resulting mean square error of prediction contains the process error component and the estimation error component, in which the former refers to future claims variability while the latter refers to the uncertainty in parameter estimation. We illustrate the application of our model to public liability data and simulated data.15 page(s
It is often necessary to estimate probability distributions to describe the loss processes covered b...
The future development when an insurance company is in a difficult circumstance can be described by ...
After the revolution in fixed income valuation technologies that occurred in the mid 1980s, the new ...
In this paper we define a specific measure of error in the estimation of loss ratios; specifically, ...
Motivation. The new solvency regimes now emerging, insist that capital requirements align with the u...
This paper is concerned with the estimation of forecast error, particularly in relation to insurance...
The estimation of loss reserves for incurred but not reported (IBNR) claims presents an important ta...
This thesis deals with a description of three claims reserving methods - with stochastic models for ...
A new method of forecasting the pricing kernel, i.e., stochastic claim inflation or link ratio funct...
abstract: The use of generalized linear models in loss reserving is not new; many statistical models...
The vast literature on stochastic loss reserving concentrates on data aggregated in run-off triangle...
To meet future liabilities general insurance companies will set-up reserves. Predicting future cash-...
Abstract. Using an approach based on Bayesian inference, we propose a method to compute an estimate ...
of the most popular claims reserving methods. Whereas a formula for the prediction error of the CL m...
We present a novel stochastic model for claims reserving that allows us to combine claims payments a...
It is often necessary to estimate probability distributions to describe the loss processes covered b...
The future development when an insurance company is in a difficult circumstance can be described by ...
After the revolution in fixed income valuation technologies that occurred in the mid 1980s, the new ...
In this paper we define a specific measure of error in the estimation of loss ratios; specifically, ...
Motivation. The new solvency regimes now emerging, insist that capital requirements align with the u...
This paper is concerned with the estimation of forecast error, particularly in relation to insurance...
The estimation of loss reserves for incurred but not reported (IBNR) claims presents an important ta...
This thesis deals with a description of three claims reserving methods - with stochastic models for ...
A new method of forecasting the pricing kernel, i.e., stochastic claim inflation or link ratio funct...
abstract: The use of generalized linear models in loss reserving is not new; many statistical models...
The vast literature on stochastic loss reserving concentrates on data aggregated in run-off triangle...
To meet future liabilities general insurance companies will set-up reserves. Predicting future cash-...
Abstract. Using an approach based on Bayesian inference, we propose a method to compute an estimate ...
of the most popular claims reserving methods. Whereas a formula for the prediction error of the CL m...
We present a novel stochastic model for claims reserving that allows us to combine claims payments a...
It is often necessary to estimate probability distributions to describe the loss processes covered b...
The future development when an insurance company is in a difficult circumstance can be described by ...
After the revolution in fixed income valuation technologies that occurred in the mid 1980s, the new ...