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The current industry standard approach evaluates reinsurance effectiveness by calculating capital cost savings as the product of a fixed capital cost rate and the required capital which is released. Reinsurance is deemed value-creating if the resulting capital cost savings is more than the profit margin ceded to support the purchase-a Return On Risk-Adjusted Capital (RORAC) approach.
Motivation: There is a growing need for effective, practical methods of operational risk analysis in all industries. Risk professionals are learning to develop business?unit?level risk distributions, combine those distributions into an aggregate risk model, and use that aggregate risk model to either assign risk charges back to the business units, or to evaluate cost?benefit of mitigation strategies.
Merton and Perold (1993) offered a framework for determining risk capital in a financial firm based on the cost of the implicit guarantee the firm provides to tis subsidiaries to make up any operating shortfall. Merton and Perold assume the price of such guarantees is observable from the market at large. For an insurer, this may not be a realistic assumption.
Merton and Perold (1993) offered a framework for determining risk capital in a financial firm based on the cost of the implicit guarantee the firm provides to its subsidiaries to make up any operating shortfall. Merton and Perold assume the price of such guarantees is observable from the market at large. For an insurer, this may not be a realistic assumption.
In this paper, we show an especially simple way to produce Myers-Read capital allocations in simulations, by using the Ruhm-Mango-Kreps (RMK) conditional risk algorithm. The algorithm uses only weighted averages. In particular, it does not require any calculus, even though the Myers-Read formula is a differential equation.
This paper introduces a capital consumption methodology for the price evaluation of reinsurance in a stochastic environment.
This paper introduces a capital consumption methodology for the price evaluation of reinsurance in a stochastic environment.
In this paper, a method will be illustrated which begins at the aggregate (portfolio) level for evaluating risk, and ends by producing prices for the component individual risks, effectively allocating the total portfolio risk charge. The result is an internally consistent allocation of diversification benefits.
This paper describes efforts to estimate the "portfolio effect" -- the diversification benefit from assembling a portfolio - by simulating the implied portfolio-level capital safety standard for various contract-level capital safety standards. The results showed that apparently aggressive contract-level capital standards still implied conservative portfolio-level capital safety standards.
This paper will discuss the use of a Dynamic Financial Analysis (DFA) model to assist a client company in determining the total capital required to support its underwriting activities, and the portion of that total required capital allocated to each operating
division. It will discuss issues related to risk measures, capital adequacy standards, and allocation techniques.
The unallocated loss adjustment expense (ULAE) reserve has traditionally been estimated by the paid-to-paid method (PTP), which compares paid calendar year claims department expenses to paid calendar year losses and then applies the resulting ratio to claims reserves.
More recently, Wendy A.
One of the biggest challenges facing the securitization of insurance risk is the translation of pricing techniques between the insurance and capital market worlds. At their heart the two worlds share similar purposes: assigning prices to uncertain future cash flow patterns. While the purposes are similar, historically the techniques and terminology have been somewhat disjoint.
Random uniform numbers in the range [0, 1) are used to invert the distributions of DFA variables and generate realized values. They are also perhaps the most often overlooked “parameters” of a DFA model. As the number of variables to simulate goes up, the number of iterations needed to reach satisfactory convergence increases as well.
Diversification of exposure concentration means geographical balancing amongst capacity providers - insurers, reinsurers, or capital market participants. But how to diversify those exposures is still unsettled. Efforts to this point have focused on balancing the exposures which have already been written by insurers00via catastrophe reinsurance (regular or securitized), several proposed catastrophe indices, even direct exposure exchanges.
Two well known methods for calculating risk load - Marginal Surplus and Marginal Variance - are applied to output from catastrophe modeling software. Risk loads for these marginal methods are calculated for sample new and renewal pricing are examined. For new situations, both current methods allocate the full marginal impact of the addition of a new account to that new account.
Two well-known methods for calculating risk load -- Marginal Surplus and Marginal Variance -- are applied to output from catastrophe modeling software. Risk loads for these “marginal methods” are calculated for sample new and renewal accounts. Differences between new and renewal pricing are examined. For new situations, both current methods allocate the full marginal impact of addition of a new account lo that new account.
