Matúš Maciak, Ostap Okhrin, Michal Pešta
arXiv 5 Jan 2018 · Econometrics
arXiv:1801.01792 · PDF · DOI · OpenAlex · Extracted main text
An intensive research sprang up for stochastic methods in insurance during the past years. To meet all future claims rising from policies, it is requisite to quantify the outstanding loss liabilities. Loss reserving methods based on aggregated data from run-off triangles are predominantly used to calculate the claims reserves. Conventional reserving techniques have some disadvantages: loss of information from the policy and the claim's development due to the aggregation, zero or negative cells in the triangle; usually small number of observations in the triangle; only few observations for recent accident years; and sensitivity to the most recent paid claims. To overcome these dilemmas, granular loss reserving methods for individual claim-by-claim data will be derived. Reserves' estimation is a crucial part of the risk valuation process, which is now a front burner in economics. Since there is a growing demand for prediction of total reserves for different types of claims or even multiple lines of business, a time-varying copula framework for granular reserving will be established.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Hautsch, N., Okhrin, O., and Ristig, A (2015) Efficient iterative maximum likelihood estimation of high-parameterized time series models self | 0.644 | 2 | 2 | 100% |
| 2 | Hudecová, S. and Pesta, M (2013) Modeling dependencies in claims reserving with GEE | 0.511 | 2 | 1 | 100% |
| 3 | Pesta, M. and Okhrin, O (2014) Conditional least squares and copulae in claims reserving for a single line of business self | 0.511 | 2 | 1 | 100% |
| 4 | Antonio, K. and Plat, R (2014) Micro-level stochastic loss reserving for general insurance | 0.405 | 1 | 1 | 100% |
| 5 | Arjas, E (1989) The claims reserving problem in non-life insurance: Some structural ideas | 0.405 | 1 | 1 | 100% |
| 6 | England, P. D. and Verrall, R. J (2002) Stochastic claims reserving in general insurance (with discussion) | 0.405 | 1 | 1 | 100% |
| 7 | Gijbels, I., Omelka, M., Pesta, M., and Veraverbeke, N (2017) Score tests for covariate effects in conditional copulas | 0.405 | 1 | 1 | 100% |
| 8 | Haastrup, S. and Arjas, E (1996) Claims reserving in continuous time: A nonparametric bayesian approach | 0.405 | 1 | 1 | 100% |
| 9 | Jewell, W (1989) Predicting IBNYR events and delays, part I continuous time | 0.405 | 1 | 1 | 100% |
| 10 | Jewell, W (1990) Predicting IBNYR events and delays, part II discrete time | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 21 scored citations.