Pubblicate Linee guida EBA in materia di supervisione delle filiali significative

News

Nov 10 2017

L’Autorità bancaria europea (EBA) ha pubblicato le linee guida definitive sulla vigilanza delle filiali cosidette significative. A seguito della crescente richiesta di costituzione di filiali in tutta l’Unione europea, tali linee guida sono volte a facilitare la cooperazione e il coordinamento tra le autorità competenti in materia di vigilanza prudenziale delle filiali di maggiori dimensioni e di importanza significativa (filiali cosiddette “significant-plus”). In particolare, le  linee guida permettono di individuare le filiali “significant-plus” attraverso una valutazione comune effettuata dalle autorità competenti dello Stato membro di provenienza e dalle autorità competenti dello Stato membro ospitante.

Comunicato stampa
Linee guida EBA per la supervisione delle filiali significative

Aggiornato l’elenco dei  conglomerati finanziari finanziari al 31 dicembre 2016

News

Nov 10 2017

 Sulla base di quanto previsto dall’Accordo di Coordinamento sottoscritto il 31 marzo 2006, la Banca d’Italia, l’IVASS e la Consob hanno aggiornato l’elenco dei conglomerati finanziari italiani, vale a dire di quei gruppi societari che svolgono attività in misura significativa sia nel settore assicurativo sia in quello bancario e/o dei servizi di investimento.

La vigilanza sui conglomerati finanziari viene esercitata, ai sendi del d.lgs. n. 142/2005 con gli strumenti della vigilanza supplementare che si aggiungono a quelli utilizzati per l’esercizio delle vigilanze settoriali, al fine di monitorare in modo sistematico l’adeguatezza patrimoniale e la rischiositàdel gruppo nel suo complesso, tenendo conto delle interrelazioni fra le attività assicurative e bancario/finanziarie svolte dalle sue diverse componenti.

L’elenco al 31.12.2016 (come riportato nella tabella seguente) comprende i tre conglomerati finanziari sottoposti a vigilanza supplementare già lo scorso anno. La Banca d’Italia mantiene il ruolo di autorità coordinatrive su un conglomerato a prevalente attività bancaria che include istituzioni non qualificabili come “significative”, l’IVASS sui due a prevalente attività assicurativa.

Nell’elenco non sono compresi i conglomerati finanziari che includono istituzioni “significative”, la cui identificazione spetta alla Banca Centrale Europea nell’ambito del Meccanismo di Vigilanza Unico.

What factors affect non-performing loans in Italy?
di Carlo Milani

Articolo

Nov 09 2017
What factors affect non-performing loans in Italy? di Carlo Milani

After the international financial crisis of 2007–08, the consequent economic collapse in the advanced economies and the financial turbulence in the Euro area, non-performing loans (NPLs) reached high levels in Europe. The gross amount of NPLs in the European Union was around €1.0 trillion at the end of 2016. Based on European Banking Authority’s (EBA) data, the gross NPL ratio – that is the ratio between NPLs and total loans (performing and non-performing), where both numerator and denominator are not adjusted for provisions – is close to 50% in Cyprus and Greece in June 2016 (Figure 1). In Portugal and Slovenia, it is close to 20%, while in Italy it is 16.4%. High NPL levels are present also in Ireland, Hungary, Romania and Slovakia. However, due to the larger size of the Italian banking system compared to the other distressed market, 25% of the Euro area NPLs are concentrated in Italy (Italy represented 16% of the Eurozone GDP in 2016). At the end of 2016, the Italian gross NPLs were equal to around €325 billion, of which €200 billion were bad debts (firms represented 77% of bad debts).

The Italian net NPL ratio – defined as the ratio between NPLs plus provisions and total loans plus provisions – showed an increase in the period between 2006 and 2015. It moved from 5.4% to 12.6% based on R&S Mediobanca data (Figure 2). The growth involved bad loans and the other form of NPLs (substandard, past due and restructured loans).

NPLs have relevant implications on the macroeconomic and financial side. A high level of NPLs is a signal of an excessive leveraged non-financial sector, thus also economic growth could be negatively affected. Moreover, banks are less inclined to grant more loans when they have to deal with a high level of NPLs, with a consequence of further damage on the real economy through credit crunch (Bernanke and Gertler 1989, Bernanke et al. 1999). On the financial side, NPLs affect the resilience of the banking sector, increasing the risk of financial instability.

In a recent working paper (Milani, 2017), I evaluate the effect of macroeconomic and bank-specific determinants of NPLs during a very turbulent period for Italy.

I use data from R&S Mediabanca, which includes all banks operating in Italy over the period 2006-2015 with at least €50 million of total assets. My annual based data set includes 482 juridical different commercial banks. Taking into account 2015, these banks represent 85.4% of the Italian credit market and they have more than 28,000 branches (94.8% of the total). This high-quality and high-representative data set allows me to test several factors that theoretical and empirical literature indicate as having a potential impact on NPLs (see Louzis et al., 2012).

I find evidence in favor of the too-big-too-fail (TBTF) hypothesis. In other words, very big banks, those with a market share in term of total assets at least equal to 0.96%, show that leveraging tends to significantly increase net NPLs. Moreover, I find robust evidence in favor of the ‘moral hazard’ and the ‘bad management’ hypotheses, both for TBTF and small-enough-to-fail (SETF) banks. This result signals that bank managers have a relevant role in the increase in net NPLs after the international financial crisis due to their risk-loving approach and their unsatisfactory management quality.

On the other hand, bank size has a relevant effect on the ‘diversification’ and ‘procyclical credit policy’ hypotheses. I find that diversification helps in the management of credit portfolio only when large banks are considered. For the sample of banks small-enough-to-fail it seems that the higher complexity overrides diversification benefits. Furthermore, to avoid losing private information on their customers, small banks seem to renovate and/or renegotiate existing loans to low quality borrowers. However, such a strategy compromises loan quality in the long-run.

Furthermore, I find robust evidence in favor of the ‘relationship lending’ hypothesis, both for the overall sample and for SETF banks. In the middle of financial turmoil, banks that apply a higher level of automatization in the underwriting procedures show worse performance in terms of NPLs.

I also find robust evidence in favor of the ‘cooperative banks’ hypothesis. Banks that are organized as cooperative seem to take fewer risks. Independent cooperative/mutual banks, which based their loan strategy on soft information, anticipating their difficulty to raise funds during a financial crisis, seem to adopt a more risk-averse approach.

Finally, macroeconomic explanatory variables do not seem to have a significant impact on net NPLs in Italy. More specifically, I do not find robust evidence in favor of the ‘sovereign debt’ hypothesis. A high level of public debt does not seem to impact net NPLs when a measure of leveraging is included in the estimations. I interpret this result as evidence that the sovereign risk affects only leveraged banks.

My results have important policy implications. First of all, since the macroeconomic variables for a big economy like Italy have no relevant effect on net NPLs, expected benefits from macro prudential policy are low. ‘Lean against the wind’ (Borio and Lowe 2002) is not enough to prevent financial imbalance. Only bank-based supervision can limit risk-lover behavior and bad management choices (Arpa et al. 2001). Moreover, after the financial crisis, bank supervision has mainly focused on large banks, as also remarked by the European Single Supervisory Mechanism which involves only the 120 biggest banks operating in Europe. If the too-big-to-fail problem is an issue for financial stability, small banks could also bring instability in the long-run, mainly through their procyclical credit policy.

References

  • Arpa, M., Giulini, I., Ittner, A., and Pauer, F. The influence of macroeconomic developments on Austrian banks: implications for banking supervision. BIS Papers, 2001, 1, 91–116.
  • Bernanke, B., Gertler, M. Agency costs, net worth, and business fluctuations. American Economic Review, 1989, 79, 14–31.
  • Bernanke, B., Gertler, M., and Gilchrist, S. The financial accelerator in a quantitative business cycle framework. Handbook of Macroeconomics, 1999, 1, 1341–1393.
  • Borio, C. and Lowe, P. Asset prices, financial and monetary stability: exploring the nexus. BIS Working Papers 114, 2002.
  • Louzis, D. P., Vouldis, A. T. and Metaxas, V. L. Macroeconomic and bank-specific determinants of non-performing loans in Greece: A comparative study of mortgage, business and consumer loan portfolios. Journal of Banking and Finance, 2012, 36, 1012-1027.
  • Milani, Carlo, What Factors Affect Non-Performing Loans During Macroeconomic and Financial Turbulence? Evidence from Italy, 2017. Available at SSRN: https://ssrn.com/abstract=3056189

Cyber security e finanza

Articolo

Nov 06 2017
Cyber security e finanza

 

Il 25 ottobre, alle ore 16.00, presso il Dipartimento di Matematica del Politecnico di Milano, si è tenuto un nuovo incontro della serie Polimi Fintech Journey, dal tema

Cyber security e finanza

Qui di seguito potete trovare il programma e le presentazioni dei relatori.

– Cosimo Comella (Autorità Garante per la protezione dei dati personali) Protezione dei dati personali nell’attività bancaria

– Paolo Colombini (UBI sistemi e servizi) Cyber Security in Banca: Sfide, servizi, competenze

– Antonio Perrotti (Chief Information Officer Aviva) Cyber Security nel mondo assicurativo: Sfide, servizi, competenze

– Stefano Zanero (Politecnico di Milano) Machine learning per l’identificazione automatica di frodi

 

Concorso per Idee

Annunci,Articolo

Nov 04 2017
Concorso per Idee

Impara l’arte e mettila da parte

Concorso di idee su educazione finanziaria

Il laboratorio QFinLab del Dipartimento di Matematica del Politecnico di Milano ha indetto un concorso nazionale di idee a premi su temi di educazione finanziaria rivolto a singoli partecipanti o a gruppi composti al massimo da tre partecipanti di eta’ non superiore ai 35 anni.

Il concorso è volto a far emergere idee interessanti e originali in merito a come comunicare nell’ambito dell’educazione finanziaria contribuendo a superare il gap di conoscenze di larga parte della popolazione rispetto a temi complessi quali la gestione dei propri risparmi o scegliere le modalità di finanziamento per l’acquisto dell’abitazione.

Time line:
Iscrizione: entro 15 gennaio 2018
Consegna opere: entro 22 febbraio 2018

Il giorno 23 marzo 2018 si è riunita la commissione del premio, composta da:

  • Prof. Emilio Barucci (Politecnico di Milano)
  • Prof. Daniele Marazzina (Politecnico di Milano)
  • Dott.ssa Anna Rho (Politecnico di Milano)
  • Dott.ssa Nicoletta Trentinaglia (Politecnico di Milano)
  • Dott.ssa Giulia Bernardi (Politecnico di Milano)
  • Dott. Pietro Cazzaniga (Altroconsumo)

La commissione ha giudicato vincitrice del concorso l’opera letteraria “Come allenarsi per aumentare la ricchezza” di Maria Grazia Lo Cicero (Palermo).

L’autrice dell’opera vincitrice con il suo scritto e il premio, un tablet.

 

CME Group avvierà la negoziazione dei primi futures su bitcoin

News

Nov 03 2017

CME Group, leader mondiale sul mercato dei derivati, ha annunciato l’intenzione di lanciare la negoziazione dei futures su bitcoin nel quarto trimestre del 2017.
Il nuovo contratto sarà liquidato in contanti e basato sul CME CF Bitcoin Reference Rate (BRR) che funge da tasso di riferimento giornaliero per il prezzo della criptovaluta in dollari.

Comunicato stampa

EIOPA: livello di rischio stabile nel settore assicurativo europeo

News

Nov 03 2017

L’EIOPA ha pubblicato il nuovo aggiornamento del Risk Dashboard basato sui dati del secondo trimestre del 2017. Il documento – a cadenza trimestrale – riassume i principali rischi e le vulnerabilità nel settore assicurativo dell’Unione Europea utilizzando un insieme di indicatori suddivisi per classi di rischio.
I risultati mostrano che l’esposizione al rischio del settore assicurativo nell’Unione europea è rimasta stabile e sono stati individuati alcuni sviluppi positivi del mercato dovuti in particolare alla riduzione dei tassi di inflazione attesi e dei livelli di disoccupazione.
Nonostante questi segnali positivi, tuttavia, il perdurare dell’attuale regime di bassi tassi di interesse e l’osservazione che i fondamentali del mercato potrebbero non riflettere correttamente il rischio di credito sottostante, rappresentano preoccupazioni importanti per l’industria assicurativa europea.

Comunicato stampa
Risk Dashboard EIOPA 2Q 2017

Secondo Pilastro: consultazione EBA sul rafforzamento del framework regolamentare

News

Nov 03 2017

L’Autorità Bancaria Europea (EBA) ha avviato una consultazione pubblica avente ad oggetto la modifica dell’assetto dei requisiti di Secondo Pilastro. In particolare, la consultazione include:

  • un aggiornamento delle linee guida sulle procedure e metodologie comuni per il processo SREP (linee guida SREP);
  • un aggiornamento delle linee guida sulla gestione del rischio di tasso di interesse nel banking book (linee guida IRRBB); e
  • un aggiornamento delle linee guida in materia di stress test.

La consultazione avrà termine il 31 gennaio 2018.

Comunicato stampa
Documento di consultazione su linee guida SREP
Documento di consultazione su linee guida IRRBB
Documento di consultazione su linee guida in materia di stress test

Comitato di Basilea: pubblicate linee guida definitive su rischio di step-in

News

Nov 03 2017

Il Comitato di Basilea per la vigilanza bancaria ha pubblicato le linee guida definitive sull’identificazione e la gestione del rischio di step-in, ovverosia del rischio, riscontrabile soprattutto nelle relazioni tra banche e soggetti appartenenti al sistema bancario ombra, che nasce dalla possibilità che una banca fornisca un sostegno finanziario ad entità che versano in difficoltà finanziaria in mancanza, o al di fuori, di precostituiti obblighi contrattuali.
Nell’ambito dell’iniziativa del G20 per rafforzare la vigilanza e la regolamentazione del sistema bancario ombra, le linee guida contribuiscono a mitigare il rischio che le potenziali difficoltà incontrate da soggetti bancari ombra possano influire sulla situazione finanziaria delle banche.
Le linee guida, che dovrebbero essere attuate entro il 2020, fanno seguito a due processi di consultazione condotti dal Comitato nel dicembre 2015 e nel marzo 2017 e mirano ad attenuare il rischio significativo di step-in attraverso un processo di supervisione basato sulla segnalazione e sull’applicazione di misure prudenziali esistenti.

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Linee guida definitive su rischio di step-in

Latest from EIOPA: RDB 10.2017, IM projects and SF review
di Silvia Dell’Acqua

Articolo

Nov 03 2017
Latest from EIOPA: RDB 10.2017, IM projects and SF review di Silvia Dell’Acqua

This article focuses on the latest news by EIOPA:

  • risk dashboard updated at October 2017
  • updates on Internal Model consistency projects
  • SCR Standard Formula review

The risk dashboard (RDB) is published on a quarterly basis, showing the level of risk for 8 (=7+1) risk categories. This is the outcome at October 2017:

Some comments

1. Macro risks [high, stable]

This is an overarching category affecting the whole economy, which considers economic growth, monetary policies, consumer price indices and fiscal balances.

The data show an economic environment that remains fragile because of the enduring low-yields. The GDP forecast stays around 2% expected real growth; the inflation rates forecast is decreasing, inverting the positive trend observed till March 2017, the fiscal deficit is similar to the previous quarter and the unemployment rates continue to decrease.

2. Credit risks [medium, stable]

This category measures the vulnerability to the credit risk by looking at the relevant credit asset classes exposures combined with the associated metrics (e.g. government securities and credit spread on sovereigns). The credit risk is still not to be properly reflected in the market prices, where the observed spreads are close to their historical minimum. The credit quality of the investments remains stable

3. Market risks [medium, stable]

This vulnerability of the insurance sector to adverse developments is evaluated based on the investment exposures, while the current level of riskiness is evaluated based on the volatility of the yields together with the difference between the investment returns and the guaranteed interest rates. The slight increase of the Bond volatilities is counterbalance by the reduction of the Equity Vol.

4. Liquidity and funding risk [medium, decreasing]

The vulnerability to liquidity shocked is monitored measuring the lapse rate, the holding in cash and the issuance of catastrophe bonds (low volumes or high spreads correspond to a reduction in the demand which could forma a risk). The overall assessment shows that liquidity is not a major issue for the insurance industry, despite an increased challenge for the insurers to raise debt funding and an increase issuance of CAT bonds following the hurricane season.

5. Profitability and solvency [medium, increasing]

The solvency level is measured via SCR and quality of OF, while the profitability via return on investments / combined ratio for the life / non-life sectors. The SCR ratio has slightly increased for both groups (193, +10%) and solo life undertakings (170%, +10%), while it is stable for non-life solo (220%). The net combined ratio and profitability have improved. The quality of OF is still high.

6. Interlinkages and imbalances [medium, stable]

Interlinkages are assessed between primary insurers and reinsurers, insurance and banking sector and among the derivative holdings. The exposure towards domestic sovereign debt is considered as well. Data show stable investment exposures to the different financial services.

7. Insurance (underwriting) risk [low, stable]

Indicators for insurance risks are gross written premia, claims and losses due to natural catastrophes. Concerns rise from the potential impact of the recent natural catastrophic events observed in the US and some EU countries not yet reflected in the metrics.

8. Market perception [medium, stable]

The quantities assessed are relative stock market performances (insurance stock keeps on performing worse than the Stoxx 600), price to earnings ratio (slight improved), CDS spreads and external rating outlooks (unchanged). The market perception has improved, driven by the outperformances of the insurance stocks and the reduction of the CDS spreads. Ratings and their outlooks remain stable.

The last 25 October 2017, EIOPA published an update on Internal Model (IM) Consistency Projects. The activities are part of the follow up on the “Opinion on the preparation for IM application” issued last 14.04.2015, where EIOPA addressed to the National Competent Authorities (NCAs) an opinion on the preferred option to adopt in three areas where different approaches could have harmed the convergence across Member States and jeopardized the decision making process on IM. At that time EIOPA commented on the modelling of Sovereign Exposures, on how to carry out the calculations in absence of some formal decisions and on the use of comparative studies as a complementary tool. Against this background, EIOPA has been running three related IM consistent projects. The update summarizes the work carried out, the current status and the next steps:

1. Market and Credit Risk Benchmarking Study

NCAs are currently reviewing the data of their undertakings (IM approved that include market and credit risk); EIOPA will publish a comparative study in the first quarter 2018. The project collected information on the methodologies for modelling the risks and on their calibrations. Usually, the market risk is modelled using a Monte Carlo approach and the SCR is defined as the 99.50% (1 in 100 years) one-year VaR of the resulting basic OF. EIOPA plans to carry out comparative studies to monitoring market and credit risk on an annual basis. The future exercises will have a shorter time span, producing results before the next year end; the next study will be based on end-2017 calibrations and is expected to be launched by the end of the year.

2. Modelling of Sovereign Exposures

NCAs are currently reviewing the data of their undertakings (both qualitative and quantitative). It seems that, when modelled, sovereign exposures have been treated similarly to non-sovereign instruments. The follow up on the quantitative observations will be included in 1.

3. Modelling of Dynamic Volatility Adjustment

Shall be held constant or shall it vary in line with the credit spreads that are stressed in the one year forecast?

At the beginning of 2017 EIOPA also launched a project dedicated to the review of the SCR calculation under the Standard Formula. The project was aimed at ensuring a proportionate and technically consistent supervisory regime and at looking for possible simplifications of the SCR calculation.

A draft technical advice was published for public consultation last July, the items were split in two sets and a technical advice for the items in the first set was expected by end-October 2017. The final technical advice (both sets 1 and 2) should be ready by February 2018.

Among the items quoted in the consultation paper, two of particular relevance are:

  • the reduction in the resilience to external credit ratings
  • interest rate risk sub-module

The latter, in particular, assesses the appropriateness of the interest rate risk calibration: indeed the interest rates have dropped significantly, till becoming negative. The current approach, defined in 2009, does not represent the real 1 in 200 years shock event: due to the relative calculation of the shocks, the absolute one becomes smaller with decreasing rates and negative rates are not even stressed, while in reality they can fall further. EIOPA measured the underestimation in a back testing exercise. The underestimation would remain even if the relative stress factors were calibrated using more recent data, negative rates were stressed as well and a minimum downward shock of 1% (i.e. -1%) was introduced. Given these evidences, EIOPA has proposed some alternative mathematical approaches to derive the stressed risk free curves (additive stress, interest intensity approach, combination of relative and absolute stresses).