Riding Down the Bay: Space-Time Clustering of Ecological Trends

The Chesapeake Bay Program, initiated in 1983, is a regional partnership between several state governments, federal agencies, and advisory groups that is involved in the cleanup and restoration of the Bay. To study the ecological trends in the area, we propose a new data‐driven procedure for optimal selection of tuning parameters in dynamic clustering algorithms, using the notion of a stability probe. We refer to the new procedure as Downhill Riding (DR) because of the dynamics of the clustering stability probe. We study the finite sample performance of DR when clustering benchmark Iris data and synthetic times series, and illustrate the methods using data on water quality in the Chesapeake Bay

The Use of Dynamic Financial Analysis to Determine Whether an Optimal Growth Rate Exists for a Property-Liability Insurer

Prior research on the aging phenomenon has demonstrated that new business for property-liability (P-L) insurers generates high loss ratios that gradually decline as a book of business goes through successive renewal cycles. Although the experience on new business is initially unprofitable, the renewal book of business eventually becomes profitable over time. Within this context, insurers need to manage their exposure growth in order to maximize long run profitability. Dynamic financial analysis (DFA), a relatively new tool for P-L insurers, utilizes Monte Carlo simulation to generate the overall financial results for an insurer under a large number of scenarios. This article uses a publicly available DFA model-along with the estimated market value of an insurer, based on 1990-2001 data for stock P-L insurers and underlying financial variables-to determine optimal growth rates of a P-L insurer based on mean-variance analysis, stochastic dominance, and constraints on leverage. Copyright The Journal of Risk and Insurance, 2004.