PAPERS FROM ACTUARIAL JOURNALS WORLDWIDE

2013 Annals of Actuarial Science  
ASTIN Bulletin 42 (1), 2012 ARBENZ, P.; CANESTRABO, D. Estimating copulas for insurance from scarce observations, expert opinion and prior information: A Bayesian approach. 271-290. A prudent assessment of dependence is crucial in many stochastic models for insurance risks. Copulas have become popular to model such dependencies. However, estimation procedures for copulas often lead to large parameter uncertainty when observations are scarce. In this paper, we propose a Bayesian method which
more » ... ines prior information (e.g. from regulators), observations and expert opinion in order to estimate copula parameters and determine the estimation uncertainty. The combination of different sources of information can significantly reduce the parameter uncertainty compared to the use of only one source. The model can also account for uncertainty in the marginal distributions. Furthermore, we describe the methodology for obtaining expert opinion and explain involved psychological effects and popular fallacies. We exemplify the approach in a case study. BÜ HLMANN, H.; CZAPIEWSKI, C.; HAVNING, M.; JOHANSEN, S. Obituary: Paul Johansen, the first Chairman of ASTIN has died. 385-387. Obituary. CHANG, C. W.; CHANG, J. S. K.; GUAN LIM, K. Global warming, extreme weather events, and forecasting tropical cyclones. 77-101. Global warming has more than doubled the likelihood of extreme weather events, e.g. the 2003 European heat wave, the growing intensity of rain and snow in the Northern Hemisphere, and the increasing risk of flooding in the United Kingdom. It has also induced an increasing number of deadly tropical cyclones with a continuing trend. Many individual meteorological dynamic simulations and statistical models are available for forecasting hurricanes but they neither forecast well hurricane intensity nor produce clear-cut consensus. We develop a novel hurricane forecasting model by straddling two seemingly unrelated disciplines -physical science and finance -based on the well known price discovery function of trading in financial markets. Traders of hurricane derivative contracts employ all available forecasting models, public or proprietary, to forecast hurricanes in order to make their pricing and trading decisions. By using transactional price changes of these contracts that continuously clear the market supply and demand as the predictor, and with calibration to extract the embedded hurricane information by developing hurricane futures and futures option pricing models, one can gain a forward-looking market-consensus forecast out of all of the individual forecasting models employed. Our model can forecast when a hurricane will make landfall, how destructive it will be, and how this destructive power will evolve from inception to 306 https://www.cambridge.org/core/terms. https://doi.org/10.1017/S1748499513000055 Downloaded from https://www.cambridge.org/core. IP address: 207.241.231.80, on 26 Jul 2018 at 03:38:26, subject to the Cambridge Core terms of use, available at LU, Y.; ZENG, L. A nonhomogeneous Poisson hidden Markov model for claim counts. 181-202. We propose a nonhomogeneous Poisson hidden Markov model for a time series of claim counts that accounts for both seasonal variations and random fluctuations in the claims intensity. It assumes that the parameters of the intensity function for the nonhomogeneous Poisson distribution vary Papers from Actuarial Journals Worldwide 308 https://www.cambridge.org/core/terms. https://doi.
doi:10.1017/s1748499513000055 fatcat:zq3otwnkxjcpbhqdb5mpjevgam