Scientific Program Committee

are grouped by date, time and session.s are grouped by date, time and session. There is an alphabetical list of registrants at the end of this booklet. Thursday, August 6, 10:20AM to 12 Noon Session FA1 – Finance and Applications V206 10:20 AM Title: Actuarial Approach to Pricing Barrier Options Author and Presenter: Elias Shiu, U of Iowa Abstract: This talk will show that the time-honored method of Esscher transforms, together with the concept of adjustment coefficient in ruin theory, is an efficient tool for pricing barrier options. 10:45 AM Title: A Dynamic Model of Capital Appreciation of a Portfolio Author and Presenter: Natalia Humpheys, U of Texas at Dallas Author #2: Jiayi Wu, U of Texas at Dallas Abstract: Nonparametric model of a portfolio capital appreciation is based on statistical analysis of joint distribution of the portfolio assets. In our previous work it was explained how the joint distribution was estimated and then used to forecast capital appreciation of the available assets. The model that was thus created was stationary. In the current paper we analyze the behavior of the capital appreciation of a portfolio as we travel in time, thus creating a dynamic nonparametric model. New techniques to study the new behavior are introduced as we combine the statistical analysis of nonparametric regression and of the joint distribution of the portfolio of assets. We describe how the capital appreciation of a portfolio is forecasted under these changing conditions and complement our theory by a numerical example. Ample illustrations of the dynamic model are provided as well. Thursday, August 6, 10:20AM to 12 Noon Session FA1 – Finance and Applications V206 11:10 AM Title: The Loss Given Default in the Presence of Extreme Risks Author and Presenter: Zhongyi Yuan, The Pennsylvania State University Author #2: Qihe Tang, The University of Iowa Author #3: Li Wei, Renmin University of China Abstract: Consider a portfolio of multiple obligors subject to possible default. We propose a static structural model that connects the severity of individual default to the portfolio loss given default. Our results shed light on pricing of default protection insurance on a homogeneuous credit portfolio with random recovery. 11:35 AM Title: Pricing Credit Value Adjustment with a Random Recovery Rate via the Structural Default Model Author and Presenter: Xuemiao Hao, University of Manitoba Author #2: Xinyi Zhu, University of Manitoba Abstract: We give a fast and relatively simple method to valuate credit value adjustment (CVA) in an interest rate swap via a new structural default model. In the new model the asset value process of a company has infinite jumps but finite variation. One important advantage of our work is that we are able to assume a random recovery rate which depends on the asset value at default. Compared with the case with recovery rate fixed, we show that the effect on CVA by a random recovery rate is significant. Thursday, August 6, 10:20AM to 12 Noon Session SM1 – Statistical Methods V211 10:45 AM Title: Rating Endorsements using Generalized Linear Models Presenting Author: Gee Y. Lee, University of Wisconsin-Madison Author #2: Edward W. Frees, University of Wisconsin-Madison Abstract: Insurance policies often contain optional insurance coverages known as endorsements. Because these additional coverages are typically inexpensive relative to primary coverages and data can be sparse (coverages are optional), rating of endorsements is often done in ad hoc manner after a primary analysis has been conducted. This presentation will introduce a study of the Wisconsin Local Government Property Insurance Fund where it is desirable to have a formal mechanism for rating endorsements. The goal of the study is to provide prediction algorithms that are transparent and that promote equity among policyholders by determining rates that reflect the appropriate level and amount of uncertainty of each risk. To accommodate potentially conflicting goals of data complexity and algorithmic transparency, we utilize shrinkage techniques to moderate the effects of endorsements with penalized likelihoods. We find that the rating algorithms using shrinkage techniques have a predictive accuracy that are comparable to unbiased generalized linear model techniques and provide relativities for endorsements that are consistent with sound economic, risk management, and actuarial practice. Thursday, August 6, 10:20AM to 12 Noon Session SM1 – Statistical Methods V211 11:10 AM Author and Presenter: Roel Verbelen, KU Leuven Author #2: Katrien Antonio, K, U Leuven and University of Amsterdam Author #3: Gerda Claeskens, KU Leuven Title: Modeling dependent losses under censoring and truncation using multivariate mixtures of Erlangs Abstract: We study the estimation and use of multivariate mixtures of Erlangs (MME) to model dependent multivariate censored and truncated data. MME form a highly flexible class of distributions as they are dense in the space of positive continuous multivariate distributions. Moreover, the class is analytically tractable. Many quantities of interest such as the joint density and distribution function, the Laplace transform, moments, Kendall's tau and Spearman's rho have a closed form. Moreover, the class enjoys appealing closure properties such as the facts that any unior multivariate marginal or conditional distribution is a uni-or multivariate Erlang mixture, the distribution of the sum of the component random variables is a univariate Erlang mixture and the distribution of the excess-of-losses is a again a multivariate Erlang mixture. The use of MME should be regarded as semiparametric density estimation technique to model the dependence directly and hence forms a suitable alternative to the use of copulas. We present an estimation technique for fitting MME using the EM algorithm to data that can be censored and/or truncated, which is often the case with claim severity data in actuarial science due to policy modifications such as deductibles and policy limits. We demonstrate the effectiveness of the proposed algorithm and the practical use of MME on simulated data as well as on real-world data sets. Thursday, August 6, 10:20AM to 12 Noon Session SM1 – Statistical Methods V211 11:35 AM Title: Modeling Severity and Measuring Tail Risk of Norwegian Fire Claims Author and Presenter: Vytaras Brazauskas, University of Wisconsin-Milwaukee Author #2: Andreas Kleefeld, Brandenburg University of Technology Abstract: The probabilistic behavior of the claim severity variable plays a fundamental role in calculation of deductibles, layers, loss elimination ratios, effects of inflation, and other quantities arising in insurance. Among several alternatives for modeling severity, the parametric approach continues to maintain the leading position, which is primarily due to its parsimony and flexibility. In this paper, several parametric families are employed to model severity of Norwegian fire claims for the years 1981 through 1992. The probability distributions we consider include: generalized Pareto, lognormal-Pareto (two versions), Weibull-Pareto (two versions), and folded-t. Except for the generalized Pareto distribution, the other five models are fairly new proposals that recently appeared in the actuarial literature. We use the maximum likelihood procedure to fit the models, and assess the quality of their fits using basic graphical tools (quantile-quantile plots), two goodness-of-fit statistics (Kolmogorov-Smirnov and Anderson-Darling), and two information criteria (AIC and BIC). In addition, we estimate the tail risk of 'ground up' Norwegian fire claims using the valueat-risk and tail-conditional median measures. We monitor the tail risk levels over time, for the period 1981 to 1992, and analyze predictive performances of the six probability models. In particular, we compute the next-year probability for a few upper tail events using the fitted models and compare them with the actual probabilities. Thursday, August 6, 10:20AM to 12 Noon Session ERM1 – Enterprise Risk Management – N113 10:20 AM Title: Risk Transfer: Why Insurance Is Not an Option Author and Presenter: Tom Edwalds, DePaul University Abstract: Financial derivatives are often described as "insurance" because they can be used as a risk transfer mechanism. However, the mechanics of risk transfer using financial derivatives are fundamentally different from the mechanics of risk transfer using insurance. Consequently, certain risk exposures are more appropriately transferred with one mechanism over the other. This distinction is important for actuaries and other risk professionals to keep in mind as risks emerge or evolve. 10:45 AM Title: State Transition Financial Projections for Traditional Insurance Products Author and Presenter: Nick Jacobi, SOA Abstract: Creating a financial projection of future premiums, claim payments, and reserve changes based on inforce block knowledge and sales assumptions for traditional insurance products. Discussion focuses on the creation of state variables that drive interrelated numerical components. Feedback loops within the model approximate real business dynamics of insurance products and help to explain future changes in GAAP financials. 11:10 AM Title: Down but Not Out: A Cost of Capital Approach to Fair Value Risk Margins Author and Presenter: John Manistre, GGY Axis Abstract: The Market Cost of Capital approach is emerging as a standard for estimating margins for non-hedgeable risk on an insurer’s fair value balance sheet. This paper develops a conceptually rigorous formulation of the cost of capital method for estimating margins for mortality, lapse, expense and other forms of underwriting risk. For any risk situation we develop a three step modeling approach which starts with i) a best estimate model and then adds ii) a static margin for contagion risk (the risk that current experience differs from the best estima