Ismail, Mohd SabriNoorani, Mohd Salmi MdIsmail, MuniraRazak, Fatimah Abdul...
27页查看更多>>摘要：This study examines persistent homology to detect early warning signals of financial crises in the United States, Singapore, and Malaysia markets. Persistent homology is applied to obtain a L-1-norm time series, which is then associated with critical slowing down indicators (autocorrelation function at lag 1, variance, and mean power spectrum at low frequencies). Mann-Kendall test is used to anticipate the rising trend in the indicators before financial crises. Significance, structural break, and sensitivity tests are added to validate the method's robustness. Further, we compare the L-1-norms with another representative called residual time series. In our findings, three methods, namely mean power spectrums at low frequencies of the L-1-norms, variances of the residuals and mean power spectrums at low frequencies of the residuals consistently provide a period of significant rising trends and breakpoints before the Dotcom crash and Lehman Brothers bankruptcy in all markets. The outcome indicates their potential as an early warning detection tool. However, these methods depend on their parameters. Despite the dependency, we further analyze these methods by determining the threshold to cover entire trading days and record their performance based on two classification scores (probability of successful anticipation and probability of erroneous anticipation). Overall, the mean power spectrums at low frequencies of the residuals is the finest method to detect early warning signals of financial crises in all markets. It is closely followed by the mean power spectrums at low frequencies of the L-1-norms, which has obtained better scores than the variances of the residuals in the US and Singapore, except for Malaysia. Besides the residuals, our study demonstrates that the L-1-norms obtained from persistent homology also is a meaningful representation to detect early warning signals. In general, this study offers a framework to determine early warning signals of financial crises for risk management purposes. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:
NSTL
Elsevier
Huang, Qi-AnZhao, Jun-ChanWu, Xiao-Qun
13页查看更多>>摘要：Stock networks, which are constructed from stock price time series, are useful tools for analyzing complex behaviors in stock markets. Following former researches, the epidemic model has been usually used to detect dynamic characteristics in a stock price complex systems. Recently, multilayer networks have been demonstrated well when working on heterogeneous nodes rather than integrated networks. In this paper, we proposed a two-layer SIR propagation model with an infective medium to analyze the spread of financial shocks. In consideration of strict financial regulation in the A shares, the model assumed that capital cannot flow directly between layers but through the Hong Kong stock market. By applying the model to constituent stocks included in three prominent indices, Standard & Poor 500, Shanghai and Shenzhen 300, and Hang Seng(medium), we established a two-layer Granger networks. Betweenness showed that the Hong Kong stock market had a promoting transition function of financial shocks between the US stock markets and the mainland China stock markets. In addition, with a big basic reproduction number, stock markets system appeared to be vulnerable during extreme financial shock such as the outbreak of COVID-19 epidemic and the meltdown of stock markets. Furthermore, sensitivity analysis and the spreading simulation indicated that the US stock markets were much more robust to financial shocks than the mainland China stock markets. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Rasaizadi, ArashFarzin, ImanHafizi, Fateme
11页查看更多>>摘要：Unbalanced distribution of trips during the time is one of the factors influencing traffic congestion at some hours of a day. Identifying the significant factors on travelers' departure time choice and predicting their behavior helps maintain the balance in the time distribution of trips. For this purpose, this study employs and compares two machine learning and probabilistic approaches to model the departure time choice, including four choices, morning peak, noon peak, evening peak, and non-peak hours. Probabilistic support vector machine (PSVM) and multinomial logit (MNL) models calibrated based on the origin-destination data of Qazvin, and the evaluation and comparison of these two models made based on two applications of identifying the significant factors on the departure time and predicting the departure time. In terms of interpretability, the MNL model results have an indisputable advantage due to the lack of interpretable coefficients and parameters in the PSVM model. On the other hand, machine learning models' predictive power partially covers the disadvantage of not being interpretable. The results show that the PSVM model can predict the departure time with 53.96% accuracy than the 49.98% accuracy of the MNL model. The maximum balanced accuracy for predicting morning peak, noon peak, and non-peak options is 69%, 53%, and 60%, respectively; obtained by the PSVM model and the MNL model predicts the evening peak option with a balanced accuracy of 52% more accurate than PSVM. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Shi, XiaoqiuLong, WeiLi, YanyanDeng, Dingshan...
12页查看更多>>摘要：A supply chain system can be considered as an interdependent supply chain network (ISCN) which consists of an undirected cyber-network and a directed physical-network. To survive against disruptions, an ISCN needs to maintain operations and connectedness, referred to as robustness. Studies on the robustness of ISCNs when considering both functional and structural cascading failures are still scarce. In this paper, we first propose a cascading failure model which considers these two cascading failures simultaneously. We also present a model to generate ISCNs with different network types and interconnecting patterns. Using the transition threshold based on the proposed all-type connected sub-network, we can evaluate the robustness of ISCNs more properly. We then conduct numerical simulations to investigate how some parameters (e.g., network type, interconnecting pattern, the distribution of different types of nodes, etc.) affect the robustness of ISCNs under random and targeted disruptions. The results mainly show that the robustness of ISCNs can be affected seriously by different network types, interconnecting patterns, and disruption types; and the distribution of different types of nodes is more uniform, the corresponding ISCN is more robust, no matter what the disruption type is. Our results may provide help for building robust ISCNs. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Kim, MinjungKim, Beom Jun
8页查看更多>>摘要："Too-big-to-fail (TBTF)"is a controversial approach to reducing the risk of cascading failures in the financial systems. In the TBTF defense strategy, most financial supports are provided to very big companies in order to avoid the complete breakdown of the entire system. We also consider "too-small-to-fail (TSTF)"as a comparative defense strategy, in which financial supports are more focused on small companies instead. We use two types of model network and a real network based on inter-industry Input-Output Table as underlying structures for cascading failures, and examine the validity of both defense strategies with two types of bailout policy, indirect (node capacity is increased) and direct (node load is decreased). We evaluate and compare the performances of TBTF and TSTF strategies in preventing cascading failures, and demonstrate that TSTF performs better when the node capacity is increased whereas TBTF works better when the node load is decreased. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Liu, ZhizhenChen, HongLiu, EnzeHu, Wanyu...
21页查看更多>>摘要：Urban space for new transportation facilities cannot meet the increasing traffic demand. Afterward, scholars gradually increased attention to the resilience evaluation of urban road networks. Therefore, we proposed a resilience assessment framework of the urban road networks, including the resilience performance index, the robustness index, and the recovery index. Then we simulated the cascading failure based on a nonlinear load-capacity model with two capacity control parameters: alpha and beta. Results show that the intersection-based attack has the most significant impact on resilience, and resilience is positively correlated with the node degree of the attacked intersection. The increase of alpha and beta could enhance the resilience, and the urban road network achieves the best resilience performance when alpha = 0.3, beta = 0.5. Compared with the deliberate attack strategy, the resilience performance under the random attack strategy is more robust. This research can provide the foundation for optimizing urban road networks and multi-mode urban public transit networks. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Kim, Jin MinLee, Sang Bub
12页查看更多>>摘要：We study by Monte Carlo simulation the absorbing phase transition of the contact process (CP) on a four-dimensional hypercubic lattice with quenched impurity. The critical behavior of CP with quenched impurity has been predicted by an application of the Harris criterion established in equilibrium spin system to nonequilibrium absorbing phase transition and a mapping of disordered CP onto random quantum magnets. Harris criterion suggested that the pure fixed point is unstable if dv(perpendicular to)< 2, implying that any amount of impurity added to the system changes the critical behavior, where d and v(perpendicular to) are, respectively, the substrate dimensionality and correlation-length exponent of a pure system. On the other hand, in the random transverse-field magnets the critical behavior is controlled by the infinite randomness fixed point in any dimensions, suggesting that CP with randomness follows the same scenario in any dimensions. For d < 4, both expectations are valid, and the critical behavior of disordered CP is known to exhibit activated scaling. However, in four dimensions, dv(perpendicular to) = 2, and the stability of pure fixed point suggests that the disorder is irrelevant according to the Harris criterion. Our simulation results in four dimensions showed that the CP with quenched impurity exhibited the same critical behavior as the clean CP as long as the density of impurity sites x < x(c), where x(c) is the critical density above which pure lattice sites cannot form an infinite cluster. At x(c), the unusual nonuniversal power-law behavior was observed in the subcritical region and the activated scaling was found at the critical point. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
