Computing value-at-risk and expected shortfall in operational risk

Operational Risk has become more popular in the past fifteen years. The Basel committee realized its importance and banks have to allocate more capital charge, yet this is still not enough. With these new rules, banks have put in place new procedures to compute their risk measures and allocate enough capital charge to avoid bankruptcy. The Basel committee under Basel II has proposed different approaches to compute risk measures for Operational Risk, namely the Basic Indicator Approach, the Advanced Measurement Approach and the Standardized Approach. In our research, we will study the case of Loss Distribution Approach, which has been discussed before, and will contribute to the field by using a heavy-tailed distributed severity: g-and-h distributed. Then, we will analyze and test some methods to compute the value-at-risk( VaR) and conditional value-at-risk or expected shortfall (CVaR).

Computing value-at-risk and expected shortfall in operational risk

Operational Risk has become more popular in the past fifteen years. The Basel committee realized its importance and banks have to allocate more capital charge, yet this is still not enough. With these new rules, banks have put in place new procedures to compute their risk measures and allocate enough capital charge to avoid bankruptcy. The Basel committee under Basel II has proposed different approaches to compute risk measures for Operational Risk, namely the Basic Indicator Approach, the Advanced Measurement Approach and the Standardized Approach. In our research, we will study the case of Loss Distribution Approach, which has been discussed before, and will contribute to the field by using a heavy-tailed distributed severity: g-and-h distributed. Then, we will analyze and test some methods to compute the value-at-risk( VaR) and conditional value-at-risk or expected shortfall (CVaR).

The Bayesian Approach to Capital Allocation at Operational Risk: A Combination of Statistical Data and Expert Opinion

Operational risk management remains a major concern for financial institutions. Indeed, institutions are bound to manage their own funds to hedge this risk. In this paper, we propose an approach to allocate one’s own funds based on a combination of historical data and expert opinion using the loss distribution approach (LDA) and Bayesian logic. The results show that internal models are of great importance in the process of allocating one’s own funds, and the use of the Delphi method for modelling expert opinion is very useful in ensuring the reliability of estimates.

LDA-Based Model for Defect Management in Residential Buildings

This study systematically analyzes various defect patterns that occur during the warranty period of residential buildings using the loss distribution approach (LDA). This paper examines 16,108 defects from 133 residential buildings where defect disputes occurred between 2008 and 2018 in South Korea. The analysis results showed that the defect losses were relatively high in reinforcement concrete (RC) work (3/5/10 years), waterproof work (5 years), and finish work (2 years). It is shown that RC work has a high frequency of defects, such as cracks in concrete in public spaces affected by external factors. In addition, it was analyzed that the type of defect needed high repair cost because the area where the defect—such as incorrect installation and missing task—occurred, needed construction again. According to the level of frequency and severity, losses were divided within four zones to provide detailed strategies (by period). This will effectively contribute to minimizing unnecessary losses from defects as quantifying the losses of defects.

Evaluating the Impact of Defect Risks in Residential Buildings at the Occupancy Phase

This study investigated defect risks in residential buildings using the Loss Distribution Approach (LDA), a method of identifying and quantifying operational risks in economic terms. Analysis was performed on 7554 defects in 48 residential buildings where defect disputes occurred between 2008 and 2017. Defects were classified into eight types: affected functionality, broken items, corrosion, detachment, incorrect installation, missing task, surface appearance, and water problems. Work types were classified into seven groups: reinforced concrete (RC), masonry, finish, mechanical, electrical, and plumbing (MEP), door and windows, furniture, and miscellaneous. Using a risk matrix from these categories, the frequency distribution and severity distribution for each matrix cell was used to calculate loss distributions; these were combined to find the total loss distribution. The defect risks centered on RC and MEP. For RC, broken items and water leaks due to cracks or damage represented the most severe defects. For MEP, severe defects occurred owing to malfunctions in products and installation problems. Loss distributions can be used to create scenarios and corresponding response plans; thus, when a defect dispute occurs, the cost can be assessed. Furthermore, residential buildings’ loss distributions for each cell can be used to evaluate the types of work where defects occur and to verify relevant subcontractor’s abilities.