A bankruptcy probability model for assessing credit risk on corporate loans with automated variable selection

We propose an econometric model for predicting the share of bank debt held by bankrupt firms by combining a novel set of firm-level financial variables and macroeconomic indicators. Our firm-level data include payment remarks in the form of debt collections from private agencies and attachments from private and public agencies and cover all Norwegian limited liability companies for the period 2010–2021. We use logistic Lasso regressions to select bankruptcy predictors from a large set of potential predictors, comparing a highly sparse variable selection criterion (“the one standard error rule”) with the minimum cross validation error (CVE) criterion. Moreover, we examine the implications of using debt shares as weights in the estimation and find that weighting has a large impact on variable selection and predictions and, generally, leads to lower out-of-sample prediction errors than alternative approaches. Debt weighting combined with sparse variable selection gives the best predictions of the risk of bankruptcy in firms holding high shares of the bank debt.publishedVersio

Is the Pareto-Lévy law a good representation of income distributions?

Mandelbrot (1960) proposed using the so-called Pareto-Lévy class of distributions as a framework for representing income distributions. We argue in this paper that the Pareto-Lévy distribution is an interesting candidate for representing income distribution because its parameters are easy to interpret and it satisfies a specific invariance-under-aggregation property. We also demonstrate that the Gini coefficient can be expressed as a simple formula of the parameters of the Pareto-Lévy distribution. We subsequently use wage and income data for Norway and seven other OECD countries to fit the Pareto- Lévy distribution as well as the Generalized Beta type II (GB2) distribution. The results show that the Pareto-Lévy distribution fits the data better than the GB2 distribution for most countries, despite the fact that GB2 distribution has four parameters whereas the Pareto-Lévy distribution has only three

Pensjonsreformen og individuell risiko

Pensjonsreformen ble gjennomført for å bidra til at fremtidens pensjoner skulle bli mer økonomisk bærekraftige. For den enkelte betyr det økt sannsynlighet for at utbetalingene blir som lovet. Reformen gir sterke insentiver til å stå lenger i jobb. De som går av tidlig, vil få en vesentlig lavere utbetalt pensjon enn dagens pensjonister. De som planlegger for tidlig pensjon, kan møte inntektsfallet med økt sparing i yrkesaktiv alder. Alle andre løper en viss risiko for uventet og ufrivillig tidligpensjon. De som har privat tjenestepensjon, må dessuten selv bære markedsrisikoen på sin pensjonsavkastning. Et uventet fall i inntekt ved overgang til pensjon kan få konsekvenser for husholdningenes gjeldsbetjeningsevne og redusere konsumet i makro. Fremover blir det viktig å følge med på arbeidstakernes evne til å tilpasse seg reformen gjennom å stå lenger i arbeid.publishedVersio

Is the Distribution of Income Compatible with a Stable Distribution?

Mandelbrot (1961) proposed to apply the class of Pareto-Levy distributions - which belong to the Stable distributions - as a framework for modelling income distributions. He also presented theoretic arguments in favor of the Pareto-Levy distributions. In this paper we provide additional theoretical justification for this class of distributions. We also use micro data on individual market income to estimate the parameters of a Pareto-Levy distribution. Several estimation methods have been applied. The estimated Pareto-Levy distribution appears to fit the data well.Stable distributions; Pareto-Levy distributions; Income distributions

Estimates of banks' losses on loans to the corporate sector

Loans to non-financial enterprises are the main source of banks’ losses. Analyses of banks’ losses on corporate loans are therefore important in the assessment of financial stability. This paper presents Norges Bank’s framework for estimating losses on corporate loans built up from microdata for each firm and loan in each bank. Losses are estimated using a stepwise process. First, we estimate revenue developments at industry level and simulate the effect on firms’ future financial statements. This is then used to project firms’ bankruptcy probabilities using Norges Bank’s bankruptcy probability model (KOSMO). Finally, the bankruptcy probabilities are linked to data on banks’ exposures and credit losses are estimated. The estimates will be included in Norges Bank’s assessment of vulnerabilities and risks in the Norwegian banking system. In addition to being included in a general risk assessment, the framework can be used in stress testing and in the assessment of new areas of risk, such as climate risk.publishedVersio

Government support schemes during the Covid-19 pandemic have had a dampening effect on corporate credit risk

After the Covid-19 pandemic broke out, the authorities have introduced a number of measures aimed at the business sector. Support has largely been given to the sectors hardest hit by the pandemic and measures to contain it. A considerable share of banks’ loan customers in these sectors have received support from one or more of these schemes. This has likely had a dampening effect on banks’ credit loss risk

Myndighetenes støtteordninger under koronapandemien har dempet kredittrisikoen i foretakene

Etter at koronapandemien brøt ut, har myndighetene innført en rekke tiltak rettet mot næringslivet. Støtten har i stor grad blitt gitt til de næringene som er hardest rammet av pandemien og smitteverntiltakene. En betydelig andel av bankenes lånekunder i disse næringene har mottatt støtte fra en eller flere ordninger. Det har trolig bidratt til å dempe risikoen for utlånstap i bankene

Pensjonsreformen og individuell risiko

Pensjonsreformen ble gjennomført for å bidra til at fremtidens pensjoner skulle bli mer økonomisk bærekraftige. For den enkelte betyr det økt sannsynlighet for at utbetalingene blir som lovet. Reformen gir sterke insentiver til å stå lenger i jobb. De som går av tidlig, vil få en vesentlig lavere utbetalt pensjon enn dagens pensjonister. De som planlegger for tidlig pensjon, kan møte inntektsfallet med økt sparing i yrkesaktiv alder. Alle andre løper en viss risiko for uventet og ufrivillig tidligpensjon. De som har privat tjenestepensjon, må dessuten selv bære markedsrisikoen på sin pensjonsavkastning. Et uventet fall i inntekt ved overgang til pensjon kan få konsekvenser for husholdningenes gjeldsbetjeningsevne og redusere konsumet i makro. Fremover blir det viktig å følge med på arbeidstakernes evne til å tilpasse seg reformen gjennom å stå lenger i arbeid

Myndighetenes støtteordninger under koronapandemien har dempet kredittrisikoen i foretakene

Etter at koronapandemien brøt ut, har myndighetene innført en rekke tiltak rettet mot næringslivet. Støtten har i stor grad blitt gitt til de næringene som er hardest rammet av pandemien og smitteverntiltakene. En betydelig andel av bankenes lånekunder i disse næringene har mottatt støtte fra en eller flere ordninger. Det har trolig bidratt til å dempe risikoen for utlånstap i bankene.publishedVersio

A bankruptcy probability model for assessing credit risk on corporate loans with automated variable selection

We propose an econometric model for predicting the share of bank debt held by bankrupt firms by combining a novel set of firm-level financial variables and macroeconomic indicators. Our firm-level data include payment remarks in the form of debt collections from private agencies and attachments from private and public agencies and cover all Norwegian limited liability companies for the period 2010–2021. We use logistic Lasso regressions to select bankruptcy predictors from a large set of potential predictors, comparing a highly sparse variable selection criterion (“the one standard error rule”) with the minimum cross validation error (CVE) criterion. Moreover, we examine the implications of using debt shares as weights in the estimation and find that weighting has a large impact on variable selection and predictions and, generally, leads to lower out-of-sample prediction errors than alternative approaches. Debt weighting combined with sparse variable selection gives the best predictions of the risk of bankruptcy in firms holding high shares of the bank debt