Based on historical data of operational risk in Chinese commercial banks between 1994 and 2012, this paper introduces Lévy measure to describe discontinuous jumping behavior of operational losses, uses thinning method to simulate the dynamical process of operational risk losses and adopts Lévy Copula model considering frequency dependence and severity dependence simultaneously. In this paper, a dynamical operational risk model with time-varying parameters and time-varying correlation structure is given as well as the corresponding numerical experimental technology to calculate VaR and CVaR in different confidence levels. The empirical result shows that Lévy Copula model of which setting risk is decreased shows priority in describing the dependence structures between risk cells. In comparison with traditional Copula model, Lévy Copula model depicts dependence structure more explicitly and intensively lowering total risk capital and passes model robustness test as well. Furthermore, the dynamical Copula model displays trends of risk and reduces the estimation bias of risk capital deriving from time-varying parameters.
In consideration of the deficiency of existing research, Mean-DCCA and Mean-MF-DCCA models are proposed by combining fractal research method with traditional portfolio theory in order to meet the actual demand of investors in different transaction cycles. The factors of time scales and different fluctuation ranges are taken into account in these models. The models are used to conduct portfolio strategies in Shanghai & Hong Kong stock markets by means of Shanghai-Hong Kong stock connect program, after which the effects of out-of-sample are tested and analyzed. The empirical results show that, first, the market structure of Shanghai-Hong Kong presents scales effect, long memory and multifractal characteristics; second, compared with the traditional strategies, the Mean-DCCA portfolio strategies are proved to achieve better effects; finally, the Mean-MF-DCCA portfolio strategies, by choosing an appropriate multifractal q-order, will significantly improve the single fractal portfolio strategies, enhance investment project profitability and Sharp Ratios, and create additional utility for investors with different risk preferences. This study is of great practical significance to the optimal allocation of assets, risk measurement and management, as well as the delineation of dependence structure of Shanghai & Hong Kong stock markets.
In order to overcome the bad precision of fitted error, low veracity and other flaws when the power dissipation of whole-straw returning device was optimized by using the regression analysis method, a high precision and high veracity optimization method based on back-propagation (BP) neural network was proposed. Taking 1ZT-210 rice straw whole straw returning device as the research objective, the testing program of three factors, five level was designed by using the orthogonal rotation method, which selected the forward velocity of device, rotate speed of knife roll and established angle of knife as experimental factors and power dissipation as influence index. The field experiment was carried out the Heilongjiang institute of agricultural mechanical engineering science according the testing program and the experimental data was obtained. The BP neural network was used to fit the experimental data, and the mathematic model of power dissipation with influence factors was established. Then, the optimal parameter combination of influence factors could be obtained by the proposed method. The optimum combination as follows:forward velocity of device is 1.39 km/h, rotate speed of knife roll is 210 rpm, and established angle is 55°, the minimum power dissipation is 9.21 kW. Comparing the result with the regression analysis method, it is better than the 10.56 kW obtained by regression analysis method. In order to check the veracity of optimization result based on BP neural network, the confirmatory experiment was carried out which selected the optimization result as testing program. The power dissipation is 9.42 kW, the absolute error is 0.21 kW and the relative error is 2.28% between the experimental result with optimization result. The confirmatory experimental was shown that the experimental result is consistent with optimization result, and the proposed method can obtain better fitting precision, higher practicability and more stable optimization result. It is a stable and feasible optimization method and offers a new method to solve the similar optimization problem in field of agriculture production.