According to the theory of unit root test, Lee-Carter model and generalized linear model, which are widely used in mortality projection, impose key implicit assumptions respectively which are inconsistent with each other. Log mortality rate (the force of mortality or the central mortality rate) is described as a unit root process in Lee-Carter model, while it is modeled as a deterministic trend process in generalized linear model. We use panel LM unit-root tests with level shifts to test the assumptions in above models, based on mortality data of the 7 most developed countries(G7) and Nordic countries(Denmark, Finland, Norway, Sweden). The test results show that a mortality projection model, whatever it is Lee-Carter model or generalized linear model, is not always appropriate to predict dynamic mortality rates of different countries. Further, we explain period effect and cohort effect of dynamic mortality according to the results of structural break test. Based on the empirical results, we extend Lee-Carter model, which includes a special case of generalized linear model. To check the performance of the extended model, we use it to forecast USA and Sweden mortality and we find that the extended Lee-Carter model works better than the original Lee-Carter model.

The short history and market segmentation characteristic of China stock markets not surprisingly make the market indicators behave in certain way. In this paper, we tabulate the belief that the regulatory and instrumental policy changes in China structurally break the market indices. This is proven and break points are detected with a focus on Shanghai Stock Exchange in the first part of this paper. Whereas, the stochastic trend nature of the market remains even when the structural breakpoints are detected and after it is tested against various kinds of deterministic trends. It, to some extent, implies the efficiency of Shanghai market with regards to unpredictability. The second part of this paper dedicates to analyzing the interaction between A and B share markets. As a contrast to the past literature, the change in trading volume of B share market is found to be a much more sensitive leading indicator to the change in A share market, in the sense of Granger causality with a VAR fashion. This finding may further reveal the unbalanced investor structure in A and B share markets.