This article is published in Environmetrics as a special issue on Modern quantitative methods for environmental risk assessment, edited by Lelys Bravo de Guenni, Cómputo Científico y Estadística, Universidad Simón Bolívar, Valle de Sartenejas. Carretera Baruta-Hoyo de La Puerta, Caracas, Miranda 1080-A, Venezuela, and Susan J. Simmons, Mathematics and Statistics, UNCW, 601 South College Road, Wilmington, NC 28403, U.S.A.
Special Issue Paper
Risk management against extremes in a changing environment: a risk-layer approach using copulas†
Version of Record online: 26 NOV 2012
Copyright © 2012 John Wiley & Sons, Ltd.
Special Issue: Modern quantitative methods for environmental risk assessment
Volume 23, Issue 8, pages 663–672, December 2012
How to Cite
Hochrainer-Stigler, S. and Pflug, G. (2012), Risk management against extremes in a changing environment: a risk-layer approach using copulas. Environmetrics, 23: 663–672. doi: 10.1002/env.2182
- Issue online: 25 DEC 2012
- Version of Record online: 26 NOV 2012
- Manuscript Accepted: 17 OCT 2012
- Manuscript Revised: 10 OCT 2012
- Manuscript Received: 17 APR 2012
- risk layer approach;
- risk management;
- climate change
Assessing and managing extreme risk have to be carried out differently compared with frequent event risk. Especially dependencies among risks are important to be incorporated here. Additionally, dynamics that may alter risk in the future are important to be included in modeling approaches so that strategies to reduce risk are sustainable in the long run. However, little is known about changing risks under dynamic conditions and corresponding advantages and limits of options to decrease or hedge risks. We use this knowledge gap as our starting point to lay out an approach how to model dependencies with the use of copulas and how to manage and prevent increases in risk because of dynamic changes over time. The uniqueness of these approaches is that changes in anticipated losses over different spatial scales (or risks) compared with the current situation are separated into different risk layers, and these different risk layers can be expressed simultaneously with loss distributions. Because risk-reduction and risk-transfer measures are suitable for different risk layers, this distinction can serve in determining the most appropriate risk management measures over different scales. We apply our method to flood risk in Hungary, specifically within the Tisza region. The approach may be particularly useful if dissimilar changes in risk at the local level can be expected, for example, an increase in low-probability events in one region and an increase in the frequency of small magnitude events in a neighboring region. Copyright © 2012 John Wiley & Sons, Ltd.