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In many practical situations, more than one failure mechanism may contribute to product failure. Many studies assume independence between the different competing risks of failure. Nevertheless, the assumption of independence is not always justified in various practical applications. When the competing risks are assumed dependent, it is important to identify models that describe their dependence structure. Copulas are considered a powerful tool to model such dependence structures. This paper addresses the problem of developing Bayesian life test acceptance criteria through two-sample prediction of future observations based on another independent Weibull progressively Type-II censored sample with binomial random removals. It is assumed that unit failure occurs due to only one of two competing risks. Dependence among the competing risks of failure is modeled using Archimedean copulas with nonconjugate prior distributions. A Metropolis–Hastings Markov chain Monte Carlo algorithm is implemented to derive the prediction intervals that define the proposed acceptance criteria. The derived acceptance criteria enable manufacturers to conform to the required quality specifications and help their clients to properly set their quality expectations. A real data example is provided to illustrate the proposed life test acceptance criteria.

Dynamic wind loads on tall buildings can be decomposed into three components, i.e. two translational components and one torsional component. When one component reaches its maximum, the other two components have low probability to take their maximum values. It is common to use combination coefficients for estimating the mean extremes of linearly combined wind loads. The traditional design practice for determining wind load combinations relies partly on some approximate combination rules and lacks a systematic and reliable method. Based on the high frequency force balance (HFFB) testing results, wind loads can be acquired in terms of time history data, which provides necessary information for the more rigorous determination of combination coefficients by probabilistic methods. In this paper, a 3D copula-based approach is proposed for determining the combination coefficients for three stochastic wind loads associated with a specific exceedance probability and a set of 3D realizable equivalent static wind loads (ESWLs) on tall buildings. Using the measured base moment and torque data by the HFFB wind tunnel test, a case study is presented to illustrate the effectiveness of the proposed framework to determine the dynamic wind load combinations and associated 3D realizable ESWLs on a full-scale 60-story building.

The fact that the relationships among the returns of financial assets tend to be nonlinear and time-varying has important implications for asset allocation. To describe these two features, this paper first combines a copula function with the Markov switching technique to model the dependence structure across assets and then builds on this Markov Switching Copula model to present a procedure for the timing of portfolio adjustments. Our empirical evidence confirms that the dependence structure between high-risk and low-risk stocks in the Shanghai and Shenzhen markets is not static but switches between regimes over the course of the sample horizon considered in this paper. More importantly, as a result of such regime-switching characteristics of their dependence structure, our analysis of the out-of-sample asset allocation performance indicates that employing the procedure proposed in this paper to identify regime changes and decide when to adjust portfolio weights allows investors with the Constant Relative Risk Aversion utility to achieve both higher realized returns and higher certainty equivalent rate of returns than does the use of strategies based on static models.

In credit risk modeling, factor models, either static or dynamic, are often used to account for correlated defaults among a set of financial assets. Within the realm of factor models, default dependence is due to a set of common systematic risk factors. By coupling with a copula function, e.g., the normal, t-, Clayton, Frank, and Gumbel copula functions, an analytic formulation of the joint distribution of assets’ default times can be derived. On the other hand, factor models fail to account for the contagion mechanism of defaults in which a firm’s default risk increases due to their commercial or financial counterparties’ defaults. This study considers the dynamic factor model of Duffee (1999) coupling with a Hawkes process, a class of counting processes allowing intensities to depend on the timing of previous events (Hawkes, 1971) for the contagious effect. Using the factor- contagious-effect model, Monte Carlo simulation is performed to generate default times of two hypothesized firms. It is demonstrated that as the contagious effect increases, the goodness of fit of the joint distribution of assets’ default times based on copula functions decreases, which highlights the deficiency of the copula function approach.