Feng Shen

This paper develops a Factor-Copula methodology for constructing high-dimensional dynamic tail risk networks based on the conditional expected shortfall (CoES) in order to overcome the limitations of traditional quantile regression and copula models. We backtest the CoES using cumulative joint violations and conditional coverage tests. The proposed factor‑copula‑CoES model reduces the rejection rates in 10‑th order conditional backtests by 56.37%, 47.72%, and 1.96% relative to the static‑copula‑CoES, time‑varying‑copula‑CoES, and factor‑copula‑CoVaR models, respectively, with all reductions being statistically significant. The dynamic analysis of the tail risk network of Chinese listed financial institutions indicates that the network topology characteristics align with real market risk events. Finally, the quantitative analyses show different effects of institutional and market factors on network-measured risk spillovers and contagion among Chinese financial institutions.

Credit risk evaluation has gained substantial attention within financial institutions, serving as a pivotal tool to predict borrower repayment behavior and provide precise credit risk estimations. Traditional credit risk approaches discarded rejected applicants and were built only on accepted applicants, which posed sample selection bias issue. Previous reject inference methods solved the bias issue by incorporating information of rejected applicants. However, these methods assumed that the accepted and rejected samples had identical dimensions. In practical financial scenarios, financial institutions often encounter situations where the dimensions of accepted samples were larger than those of the rejected samples. Therefore, the additional features in accepted samples might not be fully utilized in the previous reject inference. In this study, we proposed a discriminative dual stack sparse auto-encoder (DD-SSAE) reject inference method that was suitable for the real scenarios. The proposed DD-SSAE has the following characteristics: (1) rejected samples were filtered based on our selection mechanism; (2) a stack sparse auto-encoder (SSAE), within a self-taught learning framework, was carried out to incorporate information of the selected rejected samples into the common features of accepted samples; and (3) a data fusion module, consisting of another SSAE network and a data fusion layer, was introduced to combine extra features with common features for accepted samples. The proposed method was verified on a Chinese consumer dataset and the findings illustrated its superiority over four conventional credit scoring models and five previous reject inference models.

In this study, the traditional lag structure selection method in the Mixed Data Sampling (MIDAS) regression model for forecasting GDP was replaced with a machine learning approach using the particle swarm optimization algorithm (PSO). The introduction of PSO aimed to automatically optimize the MIDAS model’s mixed-frequency lag structures, improving forecast accuracy and resolving the "forecast accuracy" and "forecast cost" weighting problem. The Diebold–Mariano test results based on U.S. macroeconomic data show that when the forecast horizon is large, the forecast accuracy of the PSO-MIDAS model is significantly better than other benchmark models. Empirical results show that, compared to the benchmark MIDAS model, the forecast accuracy of both univariate and multivariate PSO-MIDAS models improves by an average of 10% when the forecast horizon exceeds 2 quarters, and the optimization effect is greater compared to other benchmark models. The innovative use of the PSO algorithm addresses the limitations of traditional lag structure selection methods and enhances the predictive potential of the MIDAS model.

The original CamShift moving target tracking algorithm will appear local maximization when the target is occluded or the background color is close to the target color, resulting in target loss. In order to solve this problem, the CamShift algorithm is improved in this paper, and the particle filter and Kalman filter are fused together. A joint video tracking algorithm (CPKF) based on the fusion of improved CamShift algorithm, Particle Filter and Kalman Filter is proposed. The color feature and texture feature are integrated in CamShift to improve its anti-jamming ability in complex scenes. By improved Camshift algorithm to extract target feature and predict the location of the object, Camshift candidate area was calculated by the Bhattacharyya distance and the similarity of the target area is to determine whether the target obscured, if obscured, using particle filter to track local area, and passing the optimal state estimation to kalman filter to track the global area. If not occluded, Continue tracking with improved Kalman filtering. Experimental results show that the proposed algorithm can effectively track moving targets in complex environments in real time, which is significantly better than the traditional target tracking algorithm.

Multiple criteria decision making (MCDM) frameworks assist people in assessing alternatives and making reasonable decisions, with the ELECTRE II MCDM method in particular being widely applied to many diverse fields. As it is not always possible to assess qualitative attributes or accurately evaluate alternatives using precise values, this paper proposes a new approach that combines the ELECTRE II method with probabilistic linguistic term sets (PLTS) to allow decision makers to state their qualitative preferences using corresponding probabilities. To demonstrate the viability of the PTLS-ELECTRE II method and assess its practicability, the proposed method was applied to a typical MCDM venture capital project evaluation problem, for which a comprehensive venture capital project evaluation index system was constructed that included multiple qualitative and quantitative indicators, such as industry background, marketing, product technology, team management and financial data. The reasonable evaluation sequence of alternatives was then determined using the PTLS-ELECTRE II method which can provide more accurate MCDM decisions.