Forecasting Carbon Dioxide Price Using a Time-Varying High-Order Moment Hybrid Model of NAGARCHSK and Gated Recurrent Unit Network

The carbon market is recognized as the most effective means for reducing global carbon dioxide emissions. Effective carbon price forecasting can help the carbon market to solve environmental problems at a lower economic cost. However, the existing studies focus on the carbon premium explanation from the perspective of return and volatility spillover under the framework of the mean-variance low-order moment. Specifically, the time-varying, high-order moment shock of market asymmetry and extreme policies on carbon price have been ignored. The innovation of this paper is constructing a new hybrid model, NAGARCHSK-GRU, that is consistent with the special characteristics of the carbon market. In the proposed model, the NAGARCHSK model is designed to extract the time-varying, high-order moment parameter characteristics of carbon price, and the multilayer GRU model is used to train the obtained time-varying parameter and improve the forecasting accuracy. The results conclude that the NAGARCHSK-GRU model has better accuracy and robustness for forecasting carbon price. Moreover, the long-term forecasting performance has been proved. This conclusion proves the rationality of incorporating the time-varying impact of asymmetric information and extreme factors into the forecasting model, and contributes to a powerful reference for investors to formulate investment strategies and assist a reduction in carbon emissions.

Reliability Assessment on Pile Foundation Bearing Capacity Based on the First Four Moments in High-Order Moment Method

In this paper, the high-order moment method (HOMM) was developed for estimating pile foundation bearing capacity reliability assessment. Firstly, after the performance function was established, the first four moments (viz. mean, variance, skewness, and kurtosis) were suggested to be determined by a point estimate method based on two-dimensional reduction integrations. Then, the probability distribution of the performance function for the pile foundation bearing capacity was then approximated by a four-parameter cubic normal distribution, in which its distribution parameters are the first four moments. Meanwhile, the quantile of the probability distribution for the performance function and its reliability index was capable to be obtained through this distribution. In order to examine the efficiency of this method in engineering application, four pile foundations with different length-diameter radios were investigated in detail. The results demonstrate that the reliability analysis based on HOMM is greatly improved to the computational efficiency without loss precision compared with Monte Carlo simulation (MCS) and does not require complex partial derivative solving, checking point sought, and large numbers of iteration comparing with first-order reliability method (FORM). Moreover, the probability distribution function (PDF) approximated by the four-parameter cubic normal distribution was found to be consistent with that obtained by MCS. Eventually, the effects of parameter sensitivity for relative soil layer of the certain pile on reliability index were illustrated using the above-mentioned method. It indicated that the HOMM is an effective and simple approach for reliability assessment of the pile foundation bearing capacity.

Research on Risk Identification System Based on Random Forest Algorithm-High-Order Moment Model

With the continuous development of the stock market, designing a reasonable risk identification tool will help to solve the irrational problem of investors. This paper first selects the stocks with the most valuable investment value in the future through the random forest algorithm in the nine-factor model and then analyzes them by using the higher-order moment model to find that different investors’ preferences will make the weight of the portfolio change accordingly, which will eventually make the optimal return and risk set of the composition of the portfolio change. The risk identification system designed in this paper can provide an effective risk identification tool for investors and help them make rational judgments.

Dynamic financial economic fluctuation model based on non-normal distribution

This paper discusses the modeling of financial volatility under the condition of non-normal distribution. In order to solve the problem that the traditional central moment cannot estimate the thick-tailed distribution, the L-moment which is widely used in the hydrological field is introduced, and the autoregressive conditional moment model is used for static and dynamic fitting based on the generalized Pareto distribution. In order to solve the dimension disaster of multidimensional conditional skewness and kurtosis modeling, the multidimensional skewness and kurtosis model based on distribution is established, and the high-order moment model is deduced. Finally, the problems existing in the traditional investment portfolio are discussed, and on this basis, the high-order moment portfolio is further studied. The results show that the key lies in the selection of the model and the assumption of asset probability distribution. Financial risk analysis can be effective only with a large sample. High-frequency data contain more information and can provide rich data resources. The conditional generalized extreme value distribution can well describe the time-varying characteristics of scale parameters and shape parameters and capture the conditional heteroscedasticity in the high-frequency extreme value time series. Better describe the persistence and aggregation of the extreme value of high frequency data as well as the peak and thick tail characteristics of its distribution.