Operational Risk Model Managing Urban Safety in Real State

2018 ◽  
pp. 199-216
Author(s):  
Indjy M. Shawket
Keyword(s):  
Risk Model ◽  
Operational Risk ◽  
Urban Safety ◽  
Real State

scholarly journals On the Kesten-type inequality for randomly weighted sums with applications to an operational risk model

Filomat ◽  
2021 ◽  
Vol 35 (6) ◽  
pp. 1879-1888
Author(s):  
Yishan Gong ◽  
Yang Yang ◽  
Jiajun Liu
Keyword(s):  
Value At Risk ◽  
Risk Model ◽  
Cell Model ◽  
Type Inequality ◽  
Random Variables ◽  
Operational Risk ◽  
Weighted Sums ◽  
Asymptotic Formulas ◽  
Tail Probabilities ◽  
Randomly Weighted Sums

This paper considers the randomly weighted sums generated by some dependent subexponential primary random variables and some arbitrarily dependent random weights. To study the randomly weighted sums with infinitely many terms, we establish a Kesten-type upper bound for their tail probabilities in presence of subexponential primary random variables and under a certain dependence among them. Our result extends the study of Chen [5] to the dependent case. As applications, we derive some asymptotic formulas for the tail probability and the Value-at-Risk of total aggregate loss in a multivariate operational risk cell model.

scholarly journals Exploring and Validating Container Operational Risk Scale in Container Shipping: The Case of Ethiopian Shipping and Logistics Service Enterprise

2021 ◽  
Vol 13 (16) ◽  
pp. 9248
Author(s):  
Efrah Wozir Abdulahi ◽  
Luo Fan
Keyword(s):  
Risk Factors ◽  
Information Flow ◽  
Quantitative Research ◽  
Risk Model ◽  
Pairwise Comparison ◽  
Operational Risk ◽  
Container Shipping ◽  
Quantitative Research Methods ◽  
Two Factors ◽  
Risk Dimensions

The risk associated with container shipping has been a major concern in recent decades. This study presents three major risk frameworks to systematically and inclusively explore and validate container operational risk scales based on risk factors derived from the extant literature. The three risk frameworks identified are risks related to information flow, risks related to physical flow, and risks related to payment flow. Each risk factor is grouped into sub-factors (dimensions), three factors for information flow, two factors for physical flow, and two factors for payment flow. The study uses Ethiopia as a case study and employed both qualitative and quantitative research methods. An interview survey was conducted to explore additional risk factors and validate the identified risk factors in container shipping, and a questionnaire survey was then accompanied to collect the relevant data. A pairwise comparison chart (PCC) was employed to rank the risk dimensions. The results showed that the container operational risk model is satisfactory by employing exploratory and confirmatory factor analysis. Furthermore, the PCC result indicates that risk of loss or damage of goods/assets, payment delay, and decrease in or total loss of payment were ranked first, second, and third, respectively, and consequently the most significant dimensions of the risk factors. This study provides a reliable and valid scale for measuring container operational risk in container shipping companies. It also unlocks future works for using the identified risk factors as guidelines for researchers and experts to design and develop container operational risk dimensions.

A Composite Risk Model for Railroad Operations Utilizing Positive Train Control (PTC)

2014 ◽  
Author(s):  
Andre’ Abadie ◽  
Damindra Bandara ◽  
Duminda Wijesekera
Keyword(s):  
Cyber Security ◽  
Risk Model ◽  
Risk Mitigation ◽  
Operational Risk ◽  
Security Risk ◽  
Dynamic Configuration ◽  
Train Control ◽  
Novel Approach ◽  
Positive Train Control ◽  
Composite Risk

This paper proposes a novel approach for composite risk management of rail operations incorporating operational risk computed by the rail industry and cyber security risk introduced due to Positive Train Control (PTC). The suggested risk model focuses on a given train location to estimate likelihood of the PTC system failing by considering environmental risk factors (precipitation, vegetation, obstacles) and the inherent vulnerability of the radio frequency — yielding a measure of system impact. For the same location, the risk model considers safety related attributes such as train speed, track curvature, freight type, etc. — offering an estimate of operational consequence if there were an accident due to the failure of PTC. It is this intersection of impact / consequence that separates the proposed model from existing risk calculations of impact / likelihood. This is accomplished by distinguishing the likelihood of a safety accident from the likelihood of a PTC system failure — and incorporating them both. What results is an enhancement of both models; the operational risk model factors potential PTC failure in its risk assessment and gains awareness of possible requirements for operator intervention while the system risk model factors operational risk as its severity metric leading to possible requirements for automated risk mitigation by dynamic configuration change to the PTC radio.

244 Derivation and validation of a clinical risk-model in heart failure based on 6-min walk test performance and NT-proBNP status

2007 ◽  
Vol 6 (1) ◽  
pp. 46-46
Author(s):  
L FRANKENSTEIN ◽  
L INGLE ◽  
A REMPPIS ◽  
D SCHELLBERG ◽  
C SIGG ◽  
...  
Keyword(s):  
Heart Failure ◽  
Test Performance ◽  
Risk Model ◽  
Walk Test ◽  
Clinical Risk
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