Macroenvironmental determinants of operational loss severity

Cope, Eric; Piche, Mark T.; Walter, John S.

doi:10.1016/j.jbankfin.2011.11.022

Cited by 57 publications

(69 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, booming economic conditions may also create incentives to commit frauds [Povel et al, 2007], increasing the likelihood of large losses related to fraud events. Overall, our findings are in line with the ones made by Cope et al [2012]. Secondly, a positive regression coefficient related to the VIX implies that increasing values of the VIX are associated with larger values of γ, hence with an increased likelihood of 16 Results for the adLASSO models can be found in the supplementary material.…”

Section: Economic Interpretation Of the Dependence Structuresupporting

confidence: 86%

“…It can be a determinant of fine amounts in lawsuits [Cope et al, 2012], beside measuring also the health of an economy. The data have been adjusted for seasonality.…”

Section: Appendix D Explanatory Variablesmentioning

confidence: 99%

“…More recently, the attention of researchers shifts towards the conditional distribution (i.e. conditional on the economic situation) of operational losses [Chernobai et al, 2011, Cope et al, 2012, Wang and Hsu, 2013, Chavez-Demoulin et al, 2016. However, Chernobai et al [2011] and Wang and Hsu [2013] focus on the frequency process only (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…the distribution of N t ), using Poisson and logistic regressions. Cope et al [2012] study the dependence between the expected values of the loss size and macroeconomic covariates using a lognormal regression approach, but they do not focus on extreme events. Chavez-Demoulin et al [2016] model the severity and frequency distribution of large losses but only using the time and the event type as covariates.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Understanding the economic determinants of the severity of operational losses: A regularized generalized Pareto regression approach

Hambuckers

Groll

Kneib

2018

J of Applied Econometrics

View full text Add to dashboard Cite

We investigate a novel database of 10,217 extreme operational losses from the Italian bank UniCredit. Our goal is to shed light on the dependence between the severity distribution of these losses and a set of a set of macroeconomic, financial and firmspecific factors. To do so, we use Generalized Pareto regression techniques, where both the scale and shape parameters are assumed to be functions of these explanatory variables. We perform the selection of the relevant covariates with a state-of-the-art penalized-likelihood estimation procedure relying on L 1 -penalty terms. A simulation study indicates that this approach efficiently selects covariates of interest and tackles spurious regression issues encountered when dealing with integrated time series. Lastly, we illustrate the impact of different economic scenarios on the requested capital for operational risk. Our results have important implications in terms of risk management and regulatory policy.

show abstract

Section: Economic Interpretation Of the Dependence Structuresupporting

confidence: 86%

“…It can be a determinant of fine amounts in lawsuits [Cope et al, 2012], beside measuring also the health of an economy. The data have been adjusted for seasonality.…”

Section: Appendix D Explanatory Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Understanding the economic determinants of the severity of operational losses: A regularized generalized Pareto regression approach

Hambuckers

Groll

Kneib

2018

J of Applied Econometrics

View full text Add to dashboard Cite

show abstract

“…The literature includes various papers discussing the factors that predict operational loss exposure (e.g., Chernobai et al 2011, Cope et al 2012, Wang and Hsu 2013. However, not much attention has been devoted to the fundamental assumption underlying LDA models: do past losses help predict future exposure, particularly tail exposure?…”

Section: -Introductionmentioning

confidence: 99%

Predicting Operational Loss Exposure Using Past Losses

Curti

Migueis

2016

FEDS

View full text Add to dashboard Cite

Operational risk models, such as the loss distribution approach, frequently use past internal losses to forecast operational loss exposure. However, the ability of past losses to predict exposure, particularly tail exposure, has not been thoroughly examined in the literature. In this paper, we test whether simple metrics derived from past loss experience are predictive of future tail operational loss exposure using quantile regression. We find evidence that past losses are predictive of future exposure, particularly metrics related to loss frequency.

show abstract

An internal fraud model for operational losses in retail banking

Paredes,

Vega

2023

Appl Stoch Models Bus & Ind

View full text Add to dashboard Cite

This article presents a novel dynamic model for internal fraud losses in the retail banking sector, incorporating internal factors such as ethical quality of workers and bank risk controls. The model's parameters are calibrated for each bank in the Operational Riskdata eXchange (ORX) consortium, based only on publicly available exposure indicators. The model generates simulated internal operational losses, exhibiting standard stochastic properties and tail behavior that closely align with actual operational losses. At an aggregate level, the model endeavors to replicate the average frequency and severity of losses observed within the internal fraud—retail banking category. Moreover, we identify macro‐environmental factors that exert influence over the severity and frequency of model‐simulated losses, consistent with findings in the existing literature.

show abstract

Macroenvironmental determinants of operational loss severity

Cited by 57 publications

References 19 publications

Understanding the economic determinants of the severity of operational losses: A regularized generalized Pareto regression approach

Understanding the economic determinants of the severity of operational losses: A regularized generalized Pareto regression approach

Predicting Operational Loss Exposure Using Past Losses

An internal fraud model for operational losses in retail banking

Contact Info

Product

Resources

About