Operational Loss Events Research Articles

Sudden crash or long torture: The timing of market reactions to operational loss events

An emerging literature investigating market responses to operational loss announcements concludes that financial markets tend usually to overreact to loss events. This overreaction is commonly interpreted as reputational damage. We revisit this issue by focusing on the timing of markets’ reactions and highlight two variables: the start and the speed of stock markets’ responses. It appears that when operational losses are caused by internal fraud the negative market reaction materializes earlier and faster. Industry sectors and prevailing market conditions influence the timing of market reactions as well. Our empirical findings reveal moreover that a higher initial grading of the company is associated with a later stock market reaction to the announcement. While the relative magnitude and the length of markets’ overreactions is positively correlated to the concomitant downgrading our study shows that overreaction magnitudes are also strongly correlated to our estimate of the total duration of the reaction.

The frequency and severity of operational losses: a cross-country comparison

We examine the proposition that cross-country differences in the factors determining the frequency and severity of operational losses lead to cross-country differences in the distribution and incidence of operational loss events in terms of frequency and severity. For this purpose, we consider 4388 operational loss events covering 11 countries or country groups. The results reveal differences with respect to the type of loss events prevailing in each country or country group as well as differences with respect to the dominance of events of certain type in a particular business line and corporate entity type.

Operational Risk Management, Risk Management Approaches, and Risk Mitigation Techniques: Challenges Faced By Islamic Financial Services

The present study discussed and analyzed a number of issues concerning the operational risk faced by Islamic Financial Services, for example concepts of operational risks, risk management approaches and risk mitigation techniques and standards as these exist in the financial industry, unique operational risks of the Islamic Financial Services industry and the perceptions of Islamic Financial Services about these risks, regulatory concerns with respect to operational risks and their management, and Shari'ah related challenges concerning risk management has been identified and discussed. This study is an exploratory study therefore, based on the literature review relating to operational risk, risk management approaches and risk mitigation techniques. Guidelines on Stress Testing, issued by State Bank of Pakistan including sensitivity analysis and scenario analysis were also studied. The major part of the data consisting of secondary source was collected through research journals, internet and relevant books. Citation and literature discussion have been the major approach of this study. Banks should aim to develop a framework for operational risk management particularly for collecting operational loss data. In respect of designing operational risk stress tests, key indicators, like human errors, frauds, or failure to perform in timely manner, breaching limits, failure of information technology systems or events such as major fires or other disasters may be identified against the business lines. Shocks may be given to these risk events, their frequency and severity of losses. Once the operational loss events are identified, the level of shocks may be designed by looking into both the historical as well as hypothetical level of losses under those risk events.

Estimating operational risk capital: the challenges of truncation, the hazards of maximum likelihood estimation, and the promise of robust statistics

In operational risk measurement, the estimation of severity distribution parameters is the main driver of capital estimates, yet this remains a nontrivial challenge for many reasons. Maximum likelihood estimation (MLE) does not adequately meet this challenge because of its well-documented nonrobustness to modest violations of idealized textbook model assumptions: specifically, that the data is independent and identically distributed (iid), which is clearly violated by operational loss data. Yet, even using iid data, capital estimates based on MLE are biased upward, sometimes dramatically, due to Jensen's inequality. This overstatement of the true risk profile increases as the heaviness of the severity distribution tail increases, so dealing with data collection thresholds by using truncated distributions, which have thicker tails, increases MLE-related capital bias considerably. Truncation also augments correlation between a distribution's parameters, and this exacerbates the nonrobustness of MLE. This paper derives influence functions for MLE for a number of severity distributions, both truncated and not, to analytically demonstrate its nonrobustness and its sometimes counterintuitive behavior under truncation. Empirical influence functions are then used to compare MLE with robust alternatives such as the optimally bias-robust estimator (OBRE) and the Cramér-von Mises estimator. The ultimate focus, however, is on the economic and regulatory capital estimates generated by these three estimators. The mean adjusted single-loss approximation is used to translate these parameter estimates into value-at-risk-based estimates of regulatory and economic capital. The results show that OBREs are very promising alternatives to MLE for use with actual operational loss event data, whether truncated or not, when the ultimate goal is to obtain accurate (nonbiased) and robust capital estimates.

Quantitative measures of operational risk: an application to funds management

Basel II defines operational risk as the risk of direct or indirect loss resulting from inadequate or failed internal processes, people or systems or from external events. In the past decade, there have appeared a number of quantitative approaches to measuring this risk, approaches that abstract from market risk and reputational risk. The challenge is to develop operational risk measures in an asset management context where there is only limited information available about the incidence and severity of operational loss events. We survey different approaches to this problem and argue that managing this risk through operational due diligence is a source of alpha in this funds management context.

Quantitative Measures of Operational Risk: An Application to Funds Management

Basel II defines operational risk as the risk of direct or indirect loss resulting from inadequate or failed internal processes people or systems or from external events. In the past decade there have appeared a number of quantitative approaches to measuring this risk, approaches that abstract from market risk and reputational risk. The challenge is to develop operational risk measures in an asset management context where there is only limited information available about the incidence and severity of operational loss events. We survey different approaches to this problem and argue that managing this risk through operational due diligence is a source of alpha in this funds management context.

Operational and reputational risk in the European banking industry: The market reaction to operational risk events

In this paper I study the stock market reaction to the announcement of operational losses in European financial companies. Accounting for the effect of the nominal loss amount allows for an examination of the reputational damage caused by operational loss events. The analysis is based on a sample of 136 operational losses stemming from a database of the Association of German Public Sector Banks (Bundesverband öffentlicher Banken, VÖB). All operational loss events affect European financial institutions with settlements reported by the press between January 2000 and December 2009. In line with previous literature, I find a significant negative stock price reaction to the first press announcement of operational losses. Results show that the stock market also reacts negatively to the settlement announcement as losses are confirmed and the loss amount is known. Even after accounting for the nominal loss amount, cumulative abnormal returns are negative following the date of the initial news article and the settlement date indicating damages to the reputation of the firm suffering the operational loss. Multivariate regression results suggest that reputational damages are rather influenced by firm characteristics than characteristics of the operational loss event: companies with a high ratio of liabilities to total assets suffer more severe damages to reputation from operational losses than companies with more equity.

A Bayesian network structure for operational risk modelling in structured finance operations

This paper is concerned with the design of a Bayesian network structure that is suitable for operational risk modelling. The model's structure is designed specifically from the perspective of a business unit operational risk manager whose role is to measure, record, predict, communicate, analyse and control operational risk within their unit. The problem domain modelled is a functioning structured finance operations unit within a major Australian bank. The network model design incorporates a number of existing human factor frameworks to account for human error and operational risk events within the domain. The design also supports a modular structure, allowing for the inclusion of many operational loss event types, making it adaptable to different operational risk environments.

Estimating Operational Risk Capital: The Challenges of Truncation, the Hazards of MLE, and the Promise of Robust Statistics

In operational risk measurement, the estimation of severity distribution parameters is the main driver of capital estimates, yet this remains a non-trivial challenge for many reasons. Maximum likelihood estimation (MLE) does not adequately meet this challenge because of its well-documented non-robustness to modest violations of idealized textbook model assumptions, specifically that the data are independent and identically distributed (i.i.d.), which is clearly violated by operational loss data. Yet even under i.i.d. data, capital estimates based on MLE are biased upwards, sometimes dramatically, due to Jensen’s inequality. This overstatement of the true risk profile increases as the heaviness of the severity distribution tail increases, so dealing with data collection thresholds by using truncated distributions, which have thicker tails, increases MLE-related capital bias considerably. Truncation also augments correlation between a distribution’s parameters, and this exacerbates the non-robustness of MLE. This paper derives influence functions for MLE for a number of severity distributions, both truncated and not, to analytically demonstrate its non-robustness and its sometimes counterintuitive behavior under truncation. Empirical influence functions are then used to compare MLE against robust alternatives such as the Optimally Bias-Robust Estimator (OBRE) and the Cramer-von Mises (CvM) estimator. The ultimate focus, however, is on the economic and regulatory capital estimates generated by these three estimators. The mean adjusted single-loss approximation (SLA) is used to translate these parameter estimates into Value-at-Risk (VaR) based estimates of regulatory and economic capital. The results show that OBRE estimators are very promising alternatives to MLE for use with actual operational loss event data, whether truncated or not, when the ultimate goal is to obtain accurate (non-biased) and robust capital estimates.

Macroenvironmental determinants of operational loss severity

We investigate the relationships between the severity of operational loss events reported in the banking sector and various regulatory, legal, geographical, and economic indicators. Based on a data sample of over 57,000 losses incurred in more than 130 countries reported by the Operational Riskdata eXchange (ORX) consortium, we identify the most relevant exposure indicators for losses in four Basel II event categories: Internal Fraud; External Fraud; Employment Practices and Workplace Safety; and Clients, Products and Business Practices. We find evidence of significant correlations between Internal Fraud and constraints on executive power and the prevalence of insider trading. Clients, Products and Business Practices losses are significantly related to securities and shareholder protection laws, restrictions on banking activity, supervisory power, and the prevalence of insider trading. Other event types are sensitive to per-capita GDP and a governance index.

Operational Risk - Scenario Analysis

Operational risk management and measurement has been paid an increasing attention in last years. The main two reasons are the Basel II requirements that were to be complied with by all international active financial institutions by the end of 2006 and recent severe operational risk loss events. This paper focuses on operational risk measurement techniques and on economic capital estimation methods. A data sample of operational losses provided by an anonymous Central European bank is analyzed using several approaches. Multiple statistical concepts such as the Loss Distribution Approach or the Extreme Value Theory are considered. One of the methods used for operational risk management is a scenario analysis. Under this method, custom plausible loss events defined in a particular scenario are merged with the original data sample and their impact on capital estimates and on the financial institution as a whole is evaluated. Two main problems are assessed in this paper – what is the most appropriate statistical method to measure and model operational loss data distribution and what is the impact of hypothetical plausible events on the financial institution. The g&h distribution was evaluated to be the most suitable one for operational risk modeling because its results are consistent even while using a scenario analysis method. The method based on the combination of historical loss events modeling and scenario analysis provides reasonable capital estimates for the financial institution and allows to measure impact of very extreme events and also to mitigate operational risk exposure.

Sudden Crash or Long Torture: The Timing of Market Reactions to Operational Loss Events

Sudden Crash or Long Torture: The Timing of Market Reactions to Operational Loss Events

An empirical analysis of the operational losses of Australian banks

AbstractThis study provides an analysis of 54 operational loss events experienced by eight Australian banks during the period 1990–2007. The results of an event study show that the announcement of operational losses has an adverse effect on the stock price and market value of the announcing bank. Further empirical work reveals no systematic relation between losses and bank characteristics such as size and leverage. The results also show that while the frequency of an event of a certain type is independent from the underlying business line, there is an association between the loss amount and the business line. The decline in market value relative to the loss amount is found to be independent of the type of the underlying loss event.

Operational Risk – Scenario Analysis

Operational risk management and measurement has been paid an increasing attention in last years. The main two reasons are the Basel II requirements that were to be complied with by all international active financial institutions by the end of 2006 and recent severe operational risk loss events. This paper focuses on operational risk measurement techniques and on economic capital estimation methods. A data sample of operational losses provided by an anonymous Central European bank is analyzed using several approaches. Multiple statistical concepts such as the Loss Distribution Approach or the Extreme Value Theory are considered. One of the methods used for operational risk management is a scenario analysis. Under this method, custom plausible loss events defined in a particular scenario are merged with the original data sample and their impact on capital estimates and on the financial institution as a whole is evaluated. Two main problems are assessed in this paper – what is the most appropriate statistical method to measure and model operational loss data distribution and what is the impact of hypothetical plausible events on the financial institution. The g&h distribution was evaluated to be the most suitable one for operational risk modeling because its results are consistent even while using a scenario analysis method. The method based on the combination of historical loss events modeling and scenario analysis provides reasonable capital estimates for the financial institution and allows to measure impact of very extreme events and also to mitigate operational risk exposure.

Modeling and measuring multivariate operational risk with Lévy copulas

Simultaneous modeling of operational risks occurring in different event type/business line cells poses a serious challenge for operational risk quantification. Here we invoke the new concept of Lévy copulas to model the dependence structure of operational loss events. We explain the consequences of this dependence concept for frequencies and severities of operational risk in detail. For important examples of the Lévy copula and heavy-tailed generalized Pareto distributed tail severities we derive firstorder approximations for multivariate operational value-at-risk.

The market value impact of operational loss events for US banks and insurers

This paper conducts an event study analysis of the impact of operational loss events on the market values of banks and insurance companies, using the OpVar database. We focus on financial institutions because of the increased market and regulatory scrutiny of operational losses in these industries. The analysis covers all publicly reported banking and insurance operational risk events affecting publicly traded US institutions from 1978 to 2003 that caused operational losses of at least $10 million – a total of 403 bank events and 89 insurance company events. The results reveal a strong, statistically significant negative stock price reaction to announcements of operational loss events. On average, the market value response is larger for insurers than for banks. Moreover, the market value loss significantly exceeds the amount of the operational loss reported, implying that such losses convey adverse implications about future cash flows. Losses are proportionately larger for institutions with higher Tobin’s Q ratios, implying that operational loss events are more costly in market value terms for firms with strong growth prospects.

Measuring Reputational Risk: The Market Reaction to Operational Loss Announcements

We measure reputational losses by examining a firm's stock price reaction to the announcement of a major operational loss event. If the firm's market value declines by more than the announced loss amount, this is interpreted as a reputational loss. We find that market values fall one-for-one with losses caused by external events, but fall by over twice the loss percentage in cases involving internal fraud. We find that for firms with weak shareholder rights, there is not a significant difference between internal fraud and non-internal fraud events on market returns; however, for firms with strong shareholder rights, while we do not find evidence that the market reacts more than one-to-one for non-internal fraud announcements, we find strong and robust evidence that the market does fall more than one-to-one for internal fraud announcements. These results are consistent with there being a reputational impact for losses due to internal fraud while externally-caused losses have no reputational impact.

The Market Value Impact of Operational Risk Events for U.S. Banks and Insurers

This paper conducts an event study analysis of the impact of operational risk events on the market values of banks and insurance companies, using the OpVar database. We focus on financial institutions because of the increased market and regulatory scrutiny of operational losses in these industries. The analysis covers all publicly reported banking and insurance operational risk events affecting publicly traded U.S. institutions from 1978-2003 that caused operational losses of at least $10 million - a total of 403 bank events and 89 insurance company events. The results reveal a strong, statistically significant negative stock price reaction to announcements of operational loss events. On average, the market value response is larger for insurers than for banks. Moreover, the market value loss significantly exceeds the amount of the operational loss reported, implying that such losses convey adverse implications about future cash flows. Losses are proportionately larger for institutions with higher Tobin's Q ratios, implying that operational loss events are more costly in market value terms for firms with strong growth prospects.

Operational Risk: Measurement and Management Techniques

In the recent period, there is a dramatic change in the management approach to operational risks. Capturing past event loss is an important hurdle in the implementation phase. It is, of course, possible to use industry data for operational loss event and loss impact can be modified to fit into the size of an organisation. However, using standard industry data may not be justified because of specific inefficiency and advantages of a particular organisation as compared to the industry standard. However, a manufacturing organisation can easily use this method to identify loss provision and risk capital need as well as impact of catastrophic loss. This is in particular applicable to after sale services and product warranty agreement and in transactions with dealers. Non-financial service industry like hotel, transportation can also apply this model.