We consider the fluctuation of eigenvalues in factor models and propose a new method for testing the model.Based on the characteristics of eigenvalues,variables of unknown distribution are transformed into statistics ...We consider the fluctuation of eigenvalues in factor models and propose a new method for testing the model.Based on the characteristics of eigenvalues,variables of unknown distribution are transformed into statistics of known distribution through randomization.The test statistic checks for breaks in the structure of factor models,including changes in factor loadings and increases in the number of factors.We give the results of simulation experiments and test the factor structure of the stock return data of China’s and U.S.stock markets from January 1,2017,to December 31,2019.Our method performs well in both simulations and real data.展开更多
Portfolio theory has been extensively studied and applied in finance.To determine the optimal portfolio weight under the global minimum variance strategy,it is necessary to estimate both the covariance matrix and its ...Portfolio theory has been extensively studied and applied in finance.To determine the optimal portfolio weight under the global minimum variance strategy,it is necessary to estimate both the covariance matrix and its inverse.However,the high dimensionality and heavy-tailed nature of financial data pose significant challenges to this estimation.In this study,we propose a method to estimate the Gini covariance matrix by introducing a low-rank and sparse correlation structure,as an alternative to the traditional sample covariance matrix.Our approach employs a factor model to capture the low-rank structure,combined with thresholding rules to achieve the final estimation.We demonstrate the consistency of our estimators and validate our approach through simulation experiments and empirical portfolio analyses.Simulation results show that our method is highly applicable across a variety of distributional scenarios.Furthermore,empirical portfolio analysis indicates that our method can construct portfolios with superior performance.展开更多
In complex systems,functional dependency and physical dependency may have a coupling effect.In this paper,the reliability of a k-out-of-n system is analyzed considering load-sharing effect and failure mechanism(FM)pro...In complex systems,functional dependency and physical dependency may have a coupling effect.In this paper,the reliability of a k-out-of-n system is analyzed considering load-sharing effect and failure mechanism(FM)propagation.Three types of FMs are considered and an accumulative damage model is proposed to illustrate the system behavior of the k-out-of-n system and the coupling effect between load-sharing effect and FM propagation effect.A combinational algorithm based on Binary decision diagram(BDD)and Monte-Carlo simulation is presented to evaluate the complex system behavior and reliability of the k-out-of-n system.A current stabilizing system that consists of a 3-out-of-6 subsystem with FM propagation effect is presented as a case to illustrate the complex behavior and to verify the applicability of the proposed method.Due to the coupling effect change,the main mechanism and failure mode will be changed,and the system lifetime is shortened.Reasons are analyzed and results show that different sensitivity factors of three different FMs lead to the change of the development rate,thus changing the failure scenario.Neglecting the coupling effect may lead to an incomplete and ineffective measuring and monitoring plan.Design strategies must be adopted to make the FM propagation insensitive to load-sharing effect.展开更多
基金supported by the National Natural Science Foundation of China(12001517,72091212)the USTC Research Funds of the Double First-Class Initiative(YD2040002005)the Fundamental Research Funds for the Central Universities(WK2040000026,WK2040000027)。
文摘We consider the fluctuation of eigenvalues in factor models and propose a new method for testing the model.Based on the characteristics of eigenvalues,variables of unknown distribution are transformed into statistics of known distribution through randomization.The test statistic checks for breaks in the structure of factor models,including changes in factor loadings and increases in the number of factors.We give the results of simulation experiments and test the factor structure of the stock return data of China’s and U.S.stock markets from January 1,2017,to December 31,2019.Our method performs well in both simulations and real data.
基金supported by the Postdoctoral Fellowship Program of CPSF(GZC20241651)the National Natural Science Foundation of China(12501391)the Natural Science Foundation of Anhui Province(2408085QA005).
文摘Portfolio theory has been extensively studied and applied in finance.To determine the optimal portfolio weight under the global minimum variance strategy,it is necessary to estimate both the covariance matrix and its inverse.However,the high dimensionality and heavy-tailed nature of financial data pose significant challenges to this estimation.In this study,we propose a method to estimate the Gini covariance matrix by introducing a low-rank and sparse correlation structure,as an alternative to the traditional sample covariance matrix.Our approach employs a factor model to capture the low-rank structure,combined with thresholding rules to achieve the final estimation.We demonstrate the consistency of our estimators and validate our approach through simulation experiments and empirical portfolio analyses.Simulation results show that our method is highly applicable across a variety of distributional scenarios.Furthermore,empirical portfolio analysis indicates that our method can construct portfolios with superior performance.
基金This work was supported by the National Natural Science Foundation of China(61503014).
文摘In complex systems,functional dependency and physical dependency may have a coupling effect.In this paper,the reliability of a k-out-of-n system is analyzed considering load-sharing effect and failure mechanism(FM)propagation.Three types of FMs are considered and an accumulative damage model is proposed to illustrate the system behavior of the k-out-of-n system and the coupling effect between load-sharing effect and FM propagation effect.A combinational algorithm based on Binary decision diagram(BDD)and Monte-Carlo simulation is presented to evaluate the complex system behavior and reliability of the k-out-of-n system.A current stabilizing system that consists of a 3-out-of-6 subsystem with FM propagation effect is presented as a case to illustrate the complex behavior and to verify the applicability of the proposed method.Due to the coupling effect change,the main mechanism and failure mode will be changed,and the system lifetime is shortened.Reasons are analyzed and results show that different sensitivity factors of three different FMs lead to the change of the development rate,thus changing the failure scenario.Neglecting the coupling effect may lead to an incomplete and ineffective measuring and monitoring plan.Design strategies must be adopted to make the FM propagation insensitive to load-sharing effect.
