Extreme Value Analysis and Risk Assessment: A Case of Pakistan Stock Market

Sheraz, Muhammad; Imran, Nasir; Dedu, Silvia

doi:10.24818/18423264/55.3.21.01

Cited by 3 publications

(2 citation statements)

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“…The borrower's personal characteristics mainly include the borrower's age, gender, educational background, marital status and other information, which can reflect the borrower's character traits to a certain extent, which in turn affects the borrower's default risk. It is generally believed that older borrowers have a lower risk of default than younger borrowers; female borrowers have a lower risk of default; married borrowers have a lower risk of default than unmarried borrowers; borrowers with higher education have a lower risk of default than those with lower education, and the influence of these personal characteristics on default risk has been confirmed in previous relevant studies [2,33,34].…”

Section: Default Influencing Factorsmentioning

confidence: 66%

Default Prediction of Internet Finance Users Based on Imbalance-XGBoost

2023

Teh. vjesn.

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Fast and accurate identification of financial fraud is a challenge in Internet finance. Based on the characteristics of imbalanced distribution of Internet financial data, this paper integrates machine learning methods and Internet financial data to propose a prediction model for loan defaults, and proves its effectiveness and generalizability through empirical research. In this paper, we introduce a processing method (link processing method) for imbalance data based on the traditional early warning model. In this paper, we conduct experiments using the financial dataset of Lending Club platform and prove that our model is superior to XGBoost, NGBoost, Ada Boost, and GBDT in the prediction of default risk.

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Section: Default Influencing Factorsmentioning

confidence: 66%

Default Prediction of Internet Finance Users Based on Imbalance-XGBoost

2023

Teh. vjesn.

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“…The probability and consequences of an event are often typically used to describe risk. For the overall system risk, the occurrence of consecutive events leads to dangerous accidents [32]. The calculation of the system risk value is presented in Eq.…”

Section:  mentioning

confidence: 99%

Refined Oil Loading and Unloading Process Risk Assessment using Stochastic Colored Petri Nets Integrated with Risk Factors

2024

Teh. vjesn.

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This paper presents a novel risk assessment method using Stochastic Colored Petri Nets (SCPN) specifically designed for the loading and unloading process of refined oil. The method incorporates a comprehensive analysis of risk factors by employing event trees and fault trees. Based on the real logistics operation process of an enterprise, four key risk factors and their corresponding evolution processes are identified, including equipment quality, improper operations, wrong instructions, and illegal operations. Subsequently, an SCPN model is constructed to integrate these risk factors and evaluate the system's performance using isomorphic Markov chain analysis. The overall risk assessment of the system is determined based on a risk function, which captures the system's risk level considering the influence of the identified risk factors. The results reveal that personnel engaging in illegal operation behaviors pose a high-risk factor, demanding preventive measures and increased attention. This research provides valuable insights for risk management in the refined oil loading and unloading process, emphasizing the significance of addressing risk factors and enhancing safety measures.

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