scholarly journals The impact of changes in the base and precious metals prices on credit risk factors

2020 ◽  
Vol 17 (2) ◽  
pp. 45-64
Author(s):  
Vladimir Živanović
Keyword(s):  
Risk Factors ◽  
Credit Risk ◽  
Risk Model ◽  
Precious Metals ◽  
Multivariate Regression Analysis ◽  
Metal Exchange ◽  
Analysis Model ◽  
Proposed Model ◽  
Credit Risk Model ◽  
The Impact

The changes in the prices of base and precious metals on the global metal market have a significant impact on credit risk factors. The link between these factors has been neglected over the years by traditional credit risk models. The inclusion of correlation coefficients within the set credit risk model will show the impact of these changes on other variables of credit risk over the years under review and the impact of these changes on the probability of default and the recovery rate. Changes in base metals prices on the London Metal Exchange (LME) for lead and zinc and the London Bullion Metal Association (LBMA) for gold and silver as precious metals were used in the proposed credit risk model for the period of ten years. The research was done by using the multivariate regression analysis model and based on the statistical model evaluation,the significant impact of all observed independent variables on the dependent variable of the proposed model was proved. The construction of the proposed model with proven predictability gives a scientific significance to the research that includes variables of models from different markets, which have a significant impact on the variables from the financial market.

scholarly journals Developing a Risk Model for Assessment and Control of the Spread of COVID-19

Risks ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 38
Author(s):  
Usama H. Issa ◽  
Ashraf Balabel ◽  
Mohammed Abdelhakeem ◽  
Medhat M. A. Osman
Keyword(s):  
Risk Factors ◽  
Risk Analysis ◽  
Risk Model ◽  
Risk Groups ◽  
Personal Hygiene ◽  
Exact Nature ◽  
Analysis Model ◽  
Factors Affecting ◽  
Probability Of Occurrence ◽  
The Impact

Coronavirus disease 2019 (COVID-19) continues to spread rapidly all over the world challenging nearly all governments. The exact nature of COVID-19’s spread and risk factors for such a rapid spread are still imprecise as available data depend on confirmed cases only. This may result in an asymmetrically distributed burden among countries. There is an urgent need for developing a new technique or model to identify and analyze risk factors affecting such a spread. Fuzzy logic appears to be suitable for dealing with multi-risk groups with undefined data. The main purpose of this research was to develop a risk analysis model for COVID-19’s spread evaluation. Other objectives included identifying such risk factors aiming to find out reasons for such a fast spread. Nine risk groups were identified and 46 risk factors were categorized under these groups. The methodology in this study depended on identifying each risk factor by its probability of occurrence and its impact on viruses spreading. Many logical rules were used to support the proposed risk analysis model and represented the relation between probabilities and impacts as well as to connect other risk factors. The model was verified and applied in Saudi Arabia with further probable use in similar conditions. Based on the model results, it was found that (daily activities) and (home isolation) are considered groups with highest risk. On the other hand, many risk factors were categorized with high severity such as (poor social distance), (crowdedness) and (poor personal hygiene practices). It was demonstrated that the impact of COVID-19’s spread was found with a positive correlation with the risk factors’ impact, while there was no association between probability of occurrence and impact of the risk factors on COVID-19’s spread. Saudi Arabia’s quick actions have greatly reduced the impact of the risks affecting COVID-19’s spread. Finally, the new model can be applied easily in most countries to help decision makers in evaluating and controlling COVID-19’s spread.

Scenario based evaluation of a cost risk model through sensitivity analysis

2015 ◽  
Vol 22 (4) ◽  
pp. 403-423 ◽  
Author(s):  
Önder Ökmen ◽  
Ahmet Öztaş
Keyword(s):  
Risk Factors ◽  
Risk Analysis ◽  
Risk Model ◽  
Practical Method ◽  
Cost Evaluation ◽  
Sensitivity Analyses ◽  
Analysis Model ◽  
Content Type ◽  
Detailed Evaluation ◽  
Cost Risk

Purpose – Actual costs frequently deviate from the estimated costs in either favorable or adverse direction in construction projects. Conventional cost evaluation methods do not take the uncertainty and correlation effects into account. In this regard, a simulation-based cost risk analysis model, the Correlated Cost Risk Analysis Model, previously has been proposed to evaluate the uncertainty effect on construction costs in case of correlated costs and correlated risk-factors. The purpose of this paper is to introduce the detailed evaluation of the Cost Risk Analysis Model through scenario and sensitivity analyses. Design/methodology/approach – The evaluation process consists of three scenarios with three sensitivity analyses in each and 28 simulations in total. During applications, the model’s important parameter called the mean proportion coefficient is modified and the user-dependent variables like the risk-factor influence degrees are changed to observe the response of the model to these modifications and to examine the indirect, two-sided and qualitative correlation capturing algorithm of the model. Monte Carlo Simulation is also applied on the same data to compare the results. Findings – The findings have shown that the Correlated Cost Risk Analysis Model is capable of capturing the correlation between the costs and between the risk-factors, and operates in accordance with the theoretical expectancies. Originality/value – Correlated Cost Risk Analysis Model can be preferred as a reliable and practical method by the professionals of the construction sector thanks to its detailed evaluation introduced in this paper.

Replication of Contingent Claims in a Reduced-Form Credit Risk Model with Discontinuous Asset Prices

2006 ◽  
Vol 22 (4) ◽  
pp. 661-687 ◽  
Author(s):  
Tomasz R. Bielecki ◽  
Monique Jeanblanc ◽  
Marek Rutkowski
Keyword(s):  
Credit Risk ◽  
Asset Prices ◽  
Risk Model ◽  
Contingent Claims ◽  
Reduced Form ◽  
Credit Risk Model

scholarly journals The impact of risk factors on the financial performance of the commercial banking sector in Barbados

2018 ◽  
Vol 7 (1) ◽  
pp. 76-93 ◽  
Author(s):  
Anthony Wood ◽  
Shanise McConney
Keyword(s):  
Risk Factors ◽  
Credit Risk ◽  
Financial Performance ◽  
Banking Sector ◽  
Negative Impact ◽  
Interest Rate Risk ◽  
Commercial Banking ◽  
Country Risk ◽  
Risk Variable ◽  
The Impact

The objective of this paper is to determine the impact of risk factors on the financial performance of the commercial banking sector in Barbados using quarterly data for the period 2000 to 2015. The empirical results indicate that Capital Risk, Credit Risk, Liquidity Risk, Interest Rate Risk and Operational Risk have statistically significant impacts on financial performance. The only risk variable which does not derive this result is Country Risk. In addition, of those variables which proxy external factors, only GDP Growth has a statistically insignificant influence on financial performance. Credit risk exerted a negative impact on the banks’ financial performance, thus the banks must ensure they adopt appropriate measures to minimise the impact of this risk. Higher levels of capital impacted positively on the banking sector’s profitability. This paper is the first effort employing such an extensive dataset based on Barbados’ commercial banking sector and shows the main factors that influence commercial banks’ financial performance in this developing economy.

A HMM Intensity-Based Credit Risk Model and Filtering

2013 ◽  
pp. 169-184 ◽  
Author(s):  
Robert J. Elliott ◽  
Tak Kuen Siu
Keyword(s):  
Credit Risk ◽  
Risk Model ◽  
Credit Risk Model
Sign in / Sign up

Export Citation Format

Share Document