Operational Loss Events Research Articles

The market reaction to operational risk events in the energy sector

AbstractDespite the growing importance of operational risk, little is known about the impact of operational losses on the stock prices of energy firms. We fill this void by examining the market reaction of energy firms to a unique international sample of 452 operational loss events announced between 1996 and 2021, constructed using the SAS OpRisk Global Data database. Our results reveal negative and significant abnormal returns following the announcement of operational losses, while further analysis shows that results vary with event and/or announcing firm characteristics. Our evidence can help inform multiple stakeholders in the energy industry in the pricing, asset allocation, and risk management functions, as well as help regulators design effective mechanisms to mitigate the impact of operational losses on the economy and society.

Modernising operational risk management in financial institutions via data-driven causal factors analysis: A pre-registered study

In an effort to contribute a quantitative, objective and real-time tool to proactively and precisely manage the factors underlying and exacerbating operational risks, this pre-registered study executes the empirical methodology approved in the associated pre-registered report (Cornwell et al., 2023). The application of the Bayesian network-based approach to an Australian insurance company shows that integrating a financial institution's loss and operational data in this way can effectively model the probability of an operational loss event within its interconnected operational risk environment. Further insights and efficiencies are gained by modelling multiple operational loss events together, rather than in isolation. A novel two-module framework derived specifically for causal factors analysis from the resulting operational risk model helps to highlight the relative importance of causal factors, their collective effects and critical thresholds requiring proactivity. These insights derived from the framework are expected to be strategically valuable in helping an organisation design intentional and targeted controls for and monitoring of operational risks. Given existing knowledge of the improvements quantitative risk management tools make to risk management effectiveness and subsequently firm value, the enhanced risk management and the operational efficiencies this tool seeks to afford should ultimately contribute to driving financial performance and firm value.

Modernising operational risk management in financial institutions via data-driven causal factors analysis: A pre-registered report

To enable more proactive management of the underlying sources of operational risks in financial institutions, this pre-registered study seeks to improve traditional qualitative approaches to causal factors analysis. A Bayesian network-based approach is used to leverage both incident and operations data to model the probability of operational loss events. The approach is applied and empirically tested in a case study on an Australian insurance company. The outputs from the model go beyond simply identifying key risk drivers to offer risk managers a deeper understanding of how causal factors influence risk. Insights into the collective effects of causal factors, their relative importance and critical thresholds strategically inform more efficient and effective mitigation decisions, ultimately enhancing firm performance and value.

Reputational risks in banks: A review of research themes, frameworks, methods, and future research directions

AbstractThis study examined the research trends on reputational risk in banks using a systematic literature review and network analysis (SLRNA) approach on 35 research articles published between 2010 and 2020. It was found that only developed countries (i.e., the United States and Europe) have been actively contributing to research on reputational risks in banks, suggesting that reputational risks management of banks has not gained the global attention it deserves. Moreover, we identified five broad research themes from the thematic network analysis, namely, reputational risk of operational losses, reputational effects of media tone, performance implications of reputational risk management, management of reputational risk in banks, sustainability practices and reputational risk of banks in the project finance market. Nonetheless, there still a number of areas that require further attention. These include conceptualization of reputational risk management in banks that explore the dynamic and interdependencies in a system thinking framework, the role of corporate governance, environmental and social issues on reputational risk, how sustainability practices shape reputational risk management, the role of political connections in mitigating reputational effect of operational loss events, and how information and communication technology (ICT) mechanisms impact reputational risk management in banks.

Machine learning for categorization of operational risk events using textual description

This paper provides an overview of how machine learning can help in categorizing textual descriptions of operational loss events into Basel II event types. We apply PYTHON implementations of support vector machine and multinomial naive Bayes algorithms to precategorized Öffentliche Schadenfälle OpRisk (ÖffSchOR) data to demonstrate that operational loss events can be automatically assigned to one of the seven Basel II event types with very few costs and satisfactory accuracy. Our comprehensive case study on ÖffSchOR data, which includes the provision of parsimonious PYTHON code, is also useful for practitioners, who can use this knowledge to improve the cost efficiency and/or reliability of their processes for categorizing operational risk events.

Does governance matter? Country-level determinants of operational risk

Abstract The severity and frequency of operational loss events show high variability across the globe. In this paper, we first examine the extent to which the quality of country-level governance measured by the Worldwide Governance Indicators explains cross-country variation of operational losses. We use the comprehensive database of SAS OpRisk Global for the period of 2008–2019 covering 132 countries and 8,144 loss events with a total loss amount of almost 490 billion USD. Our findings indicate that the governance indicators lost their explanatory power over the past decades, which contradicts the academic consensus and calls for new explanatory variables. To find these variables, we hypothesize that the changes are driven by some important megatrends such as economic development and technological advancement, globalization, and sustainability. Accordingly, we propose an extended model where the number of mobile subscribers, the export to GDP ratio, and the poverty headcount ratio were significant for the frequency. For severity, only GDP is a significant and robust explanatory variable. Investors, regulators, and analysts should, therefore, concentrate on these factors if they wish to model, manage, or mitigate operational risks.

CORPORATE GOVERNANCE AND OPERATIONAL RISK: EMPIRICAL EVIDENCE

The aim of this paper is to study the effect of governance mechanisms on the operational risk management of banks. A total of 1176 operational loss events recorded in 14 banks during the period 2006-2013 are analyzed to study the relation between the operational loss events and seven indicators of governance: the board size, the proportion of foreign administrators, the proportion of a government representative on the board, the proportion of institutional directors, the proportion of independent directors, the rotation of the director and the internal rating of the bank. The results show that only six governance mechanisms have significant effects on operational risk management. The size of the board of directors, the presence of independent directors, the presence of institutional directors, the presence of a state representative, the presence of foreign directors on the board of directors are positively and statistically significant with the severity operational losses. The results also state that the internal rating variable is negatively and statistically significant with the severity operational losses. But, the turnover hasn't any impact on the operational risk management.Keywords: operational risk, operational loss events, corporate governance, Basel IIIJEL Classifications: G28, G34, G38DOI: https://doi.org/10.32479/ijefi.9861

The impact of spillover effects from operational risk events: a model from a portfolio perspective

Purpose Financial firms announcing large operational losses have empirically been shown to cause significant negative spillover effects in other non-announcing firms in case of the banking and insurance industry. The purpose of this paper is 1) to model such spillover effects in a network from a portfolio perspective and 2) to holistically assess operational risk, reputational risk and the risk of spillover effects, taking into account the dependencies between these risk types. Design/methodology/approach The authors propose different approaches to model spillover effects with different complexity, including stochasticity and influencing factors within the industry network. They then calibrate the model based on information from previous empirical literature. Findings The results emphasize that spillover effects can represent a considerable (non-diversifiable) risk, especially in portfolios, and that neglecting them may lead to a severe underestimation of the actual impact of single operational loss events. Originality/value This study is relevant not only for a firm’s risk management strategy but also for investors holding a portfolio of firms potentially subject to spillover effects.

Modeling catastrophic operational risk using a compound Neyman–Scott clustering model

Within a loss distribution approach (LDA) framework, we propose to model catastrophic operational risk using a compound Neyman–Scott clustering model. The particularity of this compound model is that it relies on a Neyman–Scott process (the frequency component of the LDA) to model the occurrence behavior of catastrophic operational loss events. The motivation behind this is that catastrophic operational risk may be the manifestation of a two-level risk generation mechanism: on the first level, natural and human-made catastrophes (referred to as operational storms) occur and trigger, on the second level, clusters of catastrophic operational loss events. A graphical analysis based on a historical series of 334 extreme operational loss events supports the clustering structure of the event occurrences. The calibration of the Neyman–Scott process reveals a satisfactory model fitness and underlines the high vulnerability of financial organizations to eventual operational storms.

Operational risk: a forgotten case study

In 2002, a number of US financial regulators and agencies, led by the Securities and Exchange Commission (SEC), imposed the largest fines up to that time on ten investment banks and broker/dealers for publishing misleading investor information. The fines and subsequent changes to these industry structures were named the Global Analysts Settlement (GAS), and the regulators’ remedies aimed to ensure that Chinese walls within investment banks were strictly enforced. This paper is a historical case study of the GAS scandal and the first to analyze it from the perspective of operational risk. In retrospect, the GAS case can be seen as an example of an operational risk loss event (ORLE) and, in particular, “conduct risk” (as it later became known). Subsequent events, such as the manipulation of the London interbank offered rate (Libor) and the mis-selling of mortgages and payment protection insurance (PPI), demonstrated that GAS was the precursor of much larger scandals. However, at the time of GAS, the thinking on operational risk management and capital was still being developed by the Basel Committee on Banking Supervision, and the implications of this particular scandal went largely unnoticed. Clearly, an opportunity to incorporate the lessons learned from the GAS case into wider thinking on operational risk was missed. Using Turner’s case study approach, this paper considers the GAS case from the perspective of operational risk, with a view to identifying the lessons to be learned from the scandal and then applied to future, large-scale operational risk events.

Operational risk and its impact on North American and British banks

ABSTRACTThis study examines the financial impact of operational risk and its loss announcements on the value of publicly traded banks in United States, UK and Canada. There are three main findings: (1) on average, the market reacts more negatively to a loss’s initial announcement than its settlement news; (2) operational loss events cause strong reputational damage to the announcing banks and (3) the market is particularly sensitive to the announcement of losses caused by internal fraud, of a large magnitude, resulted in the order of restitution, and that are the rulings of a regulatory investigation.

Systemic operational risk

PurposeThe aim of this paper is to study the information content of operational loss events occurring at European financial institutions with respect to the announcing bank’s industry rivals from an equity investor’s perspective.Design/methodology/approachThe authors conduct an event study to identify spillover effects of operational loss events using the Carhart (1997) four-factor model as a benchmark model. In addition, they conduct multiple regression analyses to investigate the extent to which firm-specific factors or the market environment affect abnormal returns.FindingsThey observe significant negative abnormal returns following operational loss announcements exceeding € 50 million for both the announcing firms and their competitors. In addition, they find that stock market reactions occur only within a very small event window around the announcement date, indicating a high degree of market efficiency. Finally, abnormal returns tend to be insignificant for smaller loss amounts.Originality/valueWhile operational risk is often believed to be strictly firm-specific, the results show that large operational risk events are not purely idiosyncratic; rather, they are systemic in the sense that they have contagious effects on non-event banks. Thus, the authors shed new light on how operational risk affects equity investors’ investment behaviour in an opaque and highly interconnected banking market.

Operational risk capital charges (Basel II): factoring in external loss data to the internal datasets

Purpose – This paper aims to discuss ideas of factoring in external loss data to the internal loss data sets to obtain a true picture of operational losses for non-bank financial services firms, focusing on a case study of the interdealer brokers business and a specific Basel II category of the operational risk capital charges. As it transpires, financial services firms are increasingly required by regulators to merge external loss data with their internal data sets when using a loss distribution approach. However, there is a significant constrain on the availability and completeness of the external data for non-bank financial services firms. Design/methodology/approach – Embarking on a modified Kaplan-Meier method is a clever way of factoring in external loss data into the internal data set. It allows non-bank financial firms to choose which fragments of the data constitute “the best fit”. In choosing the external data, this paper posits that such firms need to rely on loss-type events that display similar patterns in probabilities of occurrence. This method eliminates over-reliance on the external data that are specific for a different entity. One of the most important assumption underpinning the method presented in this paper is the fact that constant time intervals between the recorded operational loss events are assumed. Hereto, reaching a certain level of loss is used as the event of interest in both groups. For simplification purposes and to eliminate the noise and capture significant losses, we set this level as a multiplicity of the interdealer broker’s loss threshold. Findings – Obtaining external loss data is difficult for the non-bank financial services firms. Furthermore, institutions operating as interdealer brokers are exposed to different levels of operational risk that affect their own Advanced Measurement Approach to capital charges under Basel II. The existing consortium data sets are not suitable for non-bank financial institutions. With this in mind, the non-bank firms should select only the parts of the external data that fit their business environment. Originality/value – This paper should be of interest to any financial services firms that is required by regulators to merge its internal loss data sets with external loss data. Furthermore, this paper makes strong recommendations for regulators who should understand that the contemporary operational risk consortium data sets are not suitable for non-bank financial services firms.

POT model for operational risk: Experience with the analysis of the data collected from Chinese commercial banks

This paper takes 533 operational risk loss events publicly announced by Chinese commercial banks in the period of 1995-2012 as the sample, using Peaks over Threshold (POT) model to quantify the operational risk. The statistical data classification indicates the internal fraud is the main type of operational risk in Chinese commercial banks. This paper explains its causes from the perspective of behavioral finance. The results are as follows: first, Chinese commercial banks' operational risk loss events show an upward trend, then downward trend beginning in 2003 and currently an upward trend again; second, through the empirical analysis, this paper simulates the extreme value distribution function, finds the optimal threshold, and calculates the VaR and ES of the operational risk of Chinese commercial banks and compare them at different confidence levels; and third, in view of behavioral finance theory, overconfidence and loss aversion contribute to high internal fraud incidence.

Operational risk, the legal system and governance indicators: a country-level analysis

A total of 4388 operational loss events recorded over three decades in 53 countries are analysed on a country level in terms of the size of the economy, the standard of living, the legal system, the regional factor and six governance indicators. The results show that the average severity of the operational losses incurred by firms located in a particular country is positively related to the size of the economy and the standard of living. The results also show that loss of severity is negatively related to governance indicators, particularly regulatory quality. The frequency of operational loss events is also positively related to the size of the economy.

Operational risk modelling and organizational learning in structured finance operations: a Bayesian network approach

This paper describes the development of a tool, based on a Bayesian network model, that provides posteriori predictions of operational risk events, aggregate operational loss distributions, and Operational Value-at-Risk, for a structured finance operations unit located within one of Australia's major banks. The Bayesian network, based on a previously developed causal framework, has been designed to model the smaller and more frequent, attritional operational loss events. Given the limited availability of risk factor event information and operational loss data, we rely on the elicitation of subjective probabilities, sourced from domain experts. Parameter sensitivity analysis is performed to validate and check the model's robustness against the beliefs of risk management and operational staff. To ensure that the domain's evolving risk profile is captured through time, a formal approach to organizational learning is investigated that employs the automatic parameter adaption features of the Bayesian network model. A hypothetical case study is then described to demonstrate model adaption and the application of the tool to operational loss forecasting by a business unit risk manager.

Estimating operational risk capital with greater accuracy, precision and robustness

The largest US banks and systemically important financial institutions are required by regulatory mandate to estimate the operational risk capital they must hold using an advanced measurement approach as defined by the Basel II/III Accords. Most of these institutions use the loss distribution approach (LDA),which defines the aggregate loss distribution as the convolution of a frequency distribution and a severity distribution representing the number and magnitude of losses, respectively. Capital is a value-at-risk (VaR) estimate of this annual loss distribution (ie, the quantile corresponding to the 99.9th percentile, representing a one-in-a-thousand-year loss, on average). In practice, the severity distribution drives the capital estimate, which is essentially a very large quantile of the estimated severity distribution. Unfortunately, when using LDA with any of the widely used severity distributions (ie, heavy-tailed, skewed distributions), all unbiased estimators of severity distribution parameters generate biased capital estimates apparently due to Jensen's inequality: VaR always appears to be a convex function of these severities' parameter estimates because the (severity) quantile being estimated is so large and the severities are heavy-tailed. The resulting bias means that capital requirements always will be overstated, and this inflation is sometimes enormous (sometimes even billions of US dollars at the unit-of-measure level). In this paper an estimator of capital is presented that essentially eliminates this upward bias when used with any commonly used severity parameter estimator. The reduced-bias capital estimator (RCE), consequently, is more consistent with regulatory intent regarding the responsible implementation of the LDA framework than other implementations that fail to mitigate, if not eliminate this bias. The RCE also notably increases the precision of the capital estimate and consistently increases its robustness to violations of the independent and identically distributed data presumption (which are endemic to operational risk loss event data). So with greater capital accuracy, precision and robustness, the RCE lowers capital requirements at both the unit-of-measure and enterprise levels, increases capital stability from quarter to quarter, ceteris paribus, and does both while more accurately and precisely reflecting regulatory intent. The RCE is straightforward to explain, understand and implement using any major statistical software package.

Evidence, estimates and extreme values from Austria

We explore a new data source of operational loss events, the Austrian Loss Data Collection, featuring more than 42 000 observations. We provide statistical characteristics per event type and business line and analyze the cross-time and crosssection of the data. Subsequently, we make use of the data to address a central question of operational risk research: which approach is best suited to model the severity distribution of a single loss? Scientific attention to this question resulted in three major candidates: the generalized Pareto distribution (GPD), the central distribution of extreme value theory; the g-and-h distribution, an exotic relative to the lognormal; and the recently proposed semiparametric approach based on the modified Champernowne transformation. To evaluate the performance of each approach, we apply cross-validation. First, we find that capital requirements for operational risk are the best proxy for total losses of banks (among the considered indicators) and that, while frequency is highly correlated with size, mean losses are not. Second, we confirm the results of prior research that the GPD provides a very good fit in the cross-validation exercise. Additionally, the g-and-h distribution was found to rank second.

An operational risk profile: the experience of British firms

This study provides an analysis of 163 operational loss events experienced by a variety of British firms over the period 1999–2008. 10 different hypotheses are tested to examine the distribution of loss severity and frequency with respect to business line, event type and corporate entity type. We also test hypotheses on the relation between loss severity and the decline in the market value of the announcing firm and whether or not the decline in market value is greater if the loss results from internal fraud. The results indicate that loss severity does not depend on firm size, that the decline in market value bears no stable relation to the loss amount and that they decline in market value relative to the loss amount is positively related to firm size.

Estimating Operational Risk Capital with Greater Accuracy, Precision, and Robustness -- or -- How to Prevent Jensen's Inequality from Inflating Your OpRisk Capital Estimates

The largest US banks and Systemically Important Financial Institutions are required by regulatory mandate to estimate the operational risk capital they must hold using an Advanced Measurement Approach (AMA) as defined by the Basel II/III Accords. Most of these institutions use the Loss Distribution Approach (LDA) which defines the aggregate loss distribution as the convolution of a frequency distribution and a severity distribution representing the number and magnitude of losses, respectively. Capital is a Value-at-Risk estimate of this annual loss distribution (i.e. the quantile corresponding to the 99.9%tile, representing a one-in-a-thousand-year loss, on average). In practice, the severity distribution drives the capital estimate, which is essentially a very large quantile of the estimated severity distribution. Unfortunately, when using LDA with any of the widely used severity distributions (i.e. heavy-tailed, skewed distributions), all unbiased estimators of severity distribution parameters appear to generate biased capital estimates due to Jensen’s Inequality: VaR always appears to be a convex function of these severities’ parameter estimates because the (severity) quantile being estimated is so large and the severities are heavy-tailed. The resulting bias means that capital requirements always will be overstated, and this inflation is sometimes enormous (sometimes even billions of dollars at the unit-of-measure level). Herein I present an estimator of capital that essentially eliminates this upward bias when used with any commonly used severity parameter estimator. The Reduced-bias Capital Estimator (RCE), consequently, is more consistent with regulatory intent regarding the responsible implementation of the LDA framework than other implementations that fail to mitigate, if not eliminate this bias. RCE also notably increases the precision of the capital estimate and consistently increases its robustness to violations of the i.i.d. data presumption (which are endemic to operational risk loss event data). So with greater capital accuracy, precision, and robustness, RCE lowers capital requirements at both the unit-of-measure and enterprise levels, increases capital stability from quarter to quarter, ceteris paribus, and does both while more accurately and precisely reflecting regulatory intent. RCE is straightforward to explain, understand, and implement using any major statistical software package.