(USA). He has a PhD in Risk Management and Finance from the University of Georgia and holds the Certified Public Accountant (CPA.) Certificate. He is a member of the Academy of Financial Services, American Risk and Insurance Society, Casualty Actuarial Society and the Financial Management Association. He has published in a number of academic and industry journals, and has conducted seminars for faculty, students and policymakers from Poland and Mexico.

No abstract

<p>In this study we examine the robustness of fit for a multivariate and an autoregressive integrated moving average model to a data sample time series type.  The sample is a recurrent actuarial data set for a 10-year horizon.  We utilize this methodology to contrast with stochastic models to make projections beyond the data horizon. Our key results suggest that both types of models are useful for making predictions of actuarial liability levels given by PBO Projected Benefit Obligations on and off the horizon of the sample time series.  As we have seen in prior research, the use of multivariate models for control and auditing purposes is widely recommended.  Fast and reliable statistical estimates are desirable in all cases, whether for audit purposes or to verify and validate miscellaneous actuarial results.</p>

In this study, a multivariate regression model is determined that allows computation of the actuarial liability of social benefits using a group of potential predictors. In general, the previous or independent predictors are the same as those utilized in an actuarial valuation of labor commitments. Several linear and non-linear models are considered and tested in this study. Among the most important findings of this research is that the PBO or Actuarial Liability depends fundamentally on a linear basis of two fundamental variables in the quantification of Social Benefits --the Guarantees and the Social Benefits to Pay (PSP). The attractiveness of a relatively simple yet structurally robust model is a very attractive option in situations where managers are tasked to provide estimates in relatively short order, given that the two variables highlighted are generally readily available for most organizations. Realistically, companies are often required to give quick estimates, and they find it quite difficult to quantify an order of this magnitude. This model succeeds in filling this informational gap at the 4 percent interest rate.