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"Estimated Impact on the Federal Deficit and Insurance Premiums from Creating a New Health Plan Tier with an Actuarial Value Level of 50 Percent" (PDF). Archived from the original (PDF) on 2014-09-08. (Report to the Council for Affordable Health Coverage) Mendelson, Dan (June 27, 2011). "Establishing Sensible Cost Sharing for Medicare Cancer ...
The key with a net premium valuation is that the premiums being valued are theoretical measures - they make no reference to the actual premiums being charged by the insurer. This technique is a well-established actuarial valuation method, that became popular because of its simplicity, consistency, and ease of calculation.
The chain-ladder or development [1] method is a prominent [2] [3] actuarial loss reserving technique. The chain-ladder method is used in both the property and casualty [1] [4] and health insurance [5] fields. Its intent is to estimate incurred but not reported claims and project ultimate loss amounts. [5]
As a result, actuarial science developed along a different path, becoming more reliant on assumptions, as opposed to the arbitrage-free risk-neutral valuation concepts used in modern finance. The divergence is not related to the use of historical data and statistical projections of liability cash flows, but is instead caused by the manner in ...
It is generally equal to the actuarial present value of the future cash flows of a contingent event. In the insurance context an actuarial reserve is the present value of the future cash flows of an insurance policy and the total liability of the insurer is the sum of the actuarial reserves for every individual policy.
Net asset value is the difference between the total assets and liabilities of an insurance company. For companies, the net asset value is usually calculated at book value. This needs to be adjusted to market values for EV purposes. Furthermore, this value may be discounted to reflect the "lock in" of some of the assets by their nature.
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Actuarial credibility describes an approach used by actuaries to improve statistical estimates. Although the approach can be formulated in either a frequentist or Bayesian statistical setting, the latter is often preferred because of the ease of recognizing more than one source of randomness through both "sampling" and "prior" information.