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"... we develop a granular occurrence and development model for non-life claims that allows to resolve the inconsistency in traditional pricing techniques between actual, complete observations on the one hand and best estimates on the other hand. We illustrate our proposed model on a reinsurance portfolio, where large uncertainties in the best estimates originate from long reporting and settlement delays, low claim frequencies and heavy (even extreme) claim sizes."
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"Stackelberg game. The reinsurer is the leader in the game and maximizes its expected utility by selecting its optimal investment strategy and a safety loading in the reinsurance contract it offers to the insurer. The reinsurer can assess how the insurer will rationally react on each action of the reinsurer. The insurance company is the follower and maximizes its expected utility by choosing its investment strategy and the amount of reinsurance the company purchases at the price offered by the reinsurer. "
"Traditional techniques for calculating outstanding claim liabilities such as the chain ladder are notoriously at risk of being distorted by outliers in past claims data. Unfortunately, the literature in robust methods of reserving is scant, with notable exceptions … we put forward two alternative robust bivariate chain-ladder techniques to extend the approach of Verdonck and Van Wouwe (2011)."
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"The growing sophistication of insurance pricing, particularly for property-casualty insurance and reinsurance risk, has created a proliferation of approaches used in practice. Even within firms, pricing methodologies can vary from line to line, ranging from simplistic expected loss ratio targets to sophisticated return on capital models and even more sophisticated probability transform methods."
" In quantifying the solvency capital requirement gradient for cyber risk measurement according to Solvency II, a dangerous paradox emerges: an insurance company can be ranked as solvent according to Pillar 1 without adequately evaluating the operational solvency capital requirements under Pillar 2. "
"The methodologies examined include filtered historical simulation, extreme value theory, Monte Carlo simulation and historical simulation. Autoregressive-moving-average and generalized-autoregressive-conditional-heteroscedasticity models are used to estimate VaR."