As earthquakes can result in multi-dimensional negative consequences such as human loss and building damage, the ability to make accurate economic loss estimations immediately after the occurrence is crucial. Unfortunately, in many earthquake-stricken countries such as Iran, governments are often unable to quickly or accurately assess post-earthquake losses. The aim of this paper, therefore, is to extend the model developed by Chan et al. (Nat Hazards 17:269–283, 1998) to two independent variables to develop an earthquake economic loss estimation method based on the economic and socio-economic indices gross domestic product (GDP) and disposable personal income (DPI) and a seismic hazard probability function. A global cell map is also considered to assess the GDP and DPI based on the population in each cell affected by the earthquake. In the final stage, using the Modified Mercalli Intensity Scale, 18 earthquake damaged areas in Iran are taken as case study to estimate the economic losses using the new model presented in this paper.
The safety-first principle is a natural motivational factor in decision making, and is closely related to certain popular heuristics such as satisficing. We provide a systematic analysis of optimal portfolio choice under Roy’s safety-first principle by examining and comparing the behavior patterns of three popular investment strategies: the optimal constant-rebalanced portfolio, dynamic-rebalanced portfolio and buy-and-hold strategies. Our results indicate the importance of a match between the investment strategy, the investment goal, and the investment horizon. We also develop a geometric approach to investigate the relationships among the safety-first, expected utility, and mean-variance models and offer an explanation for the long-standing debate concerning different patterns of time-diversification effects. 相似文献
In this paper a class of nonparametric transfer function models is proposed to model nonlinear relationships between ‘input’ and ‘output’ time series. The transfer function is smooth with unknown functional forms, and the noise is assumed to be a stationary autoregressive-moving average (ARMA) process. The nonparametric transfer function is estimated jointly with the ARMA parameters. By modeling the correlation in the noise, the transfer function can be estimated more efficiently. The parsimonious ARMA structure improves the estimation efficiency in finite samples. The asymptotic properties of the estimators are investigated. The finite-sample properties are illustrated through simulations and one empirical example. 相似文献