Anno 4, Numero 6 - Facoltà di Economia

ISSN 1973-0578
All’interno …
ERES Award 2010
Le Universiadi del
trading 2010
Dal Dottorato di ricerca
in Banca e Finanza
Anno 4,
Numero 6
Novembre - Dicembre 2010
Operational risk losses: what do available
databases say?
Sommario
di Giuseppe Galloppo
Editoriale .................. 1
Le Universiadi del
trading 2011 Anteprima ................. 3
Segnalazione ............. 3
B&F on the road ......... 4
Pubblicazioni
internazionali ............ 4
Banking & Finance Lab
Proprietario ed editore: Università
degli Studi di Roma Tor Vergata,
Via Orazio Raimondo, 18, 00173
Roma.
Autorizzazione del Tribunale di
Roma n. 450/2007 del 20/09/2007
Direttore
responsabile:
Alessandro Carretta
Redazione:
Vincenzo Farina
Duccio Martelli
Indirizzo
email
della
redazione:
[email protected]
Hanno collaborato
numero: Giuseppe
Gianluca Mattarocci
a questo
Galloppo,
Numero chiuso il 26/11/2010
Financial institutions have always
been exposed to operational risk –
the risk of loss, resulting from
inadequate or failed internal
processes and information systems,
from misconduct by people or from
unforeseen external events. Both
banking supervision authorities and
banking institutions have showed
their interest in operational risk
measurement and management
techniques. This newfound
prominence is reflected in the Basel
II capital accord, including a formal
capital charge against operational
risk, based on a spectrum of three
increasingly sophisticated
measurement approaches. After the
third and final round of
consultations on operational risk,
from October 2002 to May 2003,
the Operational Risk Subgroup
(AIGOR) of the Basel Committee
Accord Implementation Group
establishes various schemes for
calculating the operational risk charge in a continuum of increasing
sophistication and risk sensitivity Basic Indicator Approach (BIA),
Standardized Approach (TSA), and
Advanced Measurement Approaches
(AMA). Although the application of
AMA is in principle open to any
proprietary model, the most popular
methodology is by far the Loss
Distribution Approach (LDA). Loss
Distribution Approach (LDA) is
based on an annual distribution of
the number and the total loss
amount of operational risk events
and an aggregate loss distribution, by
modelling the loss severity and loss
frequency separately and then
combining them via a Monte Carlo
simulation or other statistical
technique to form an aggregate loss
distribution. Under the Loss
Distribution Approach, the bank
estimates, for each business line/risk
type cell, the probability distribution
functions of the single event impact
and the event frequency for the next
(one) year using its internal data, and
computes the probability
distribution function of the
cumulative operational loss.
Considering data mining process the
LDA methodology consists of three
steps dealing with different types of
technical issues, namely: processing
homogeneous categories of internal
observations to produce a univariate
distribution of operational losses;
integrating external loss data to
refine the fit of the extreme tail of
the distribution; and jointly analyzing
the loss event categories to correct
the aggregate distribution for possible dependence between the
univariate distributions. So firms
must supplement its own internal
data with external data and develop
a meaningful way to link the two.
An important issue in operational
risk is data availability. At a
minimum, Harris, in a previous
work, argues that a database must
contain in which business unit the
loss was recognized, which business
line recognized the loss, and in what
function the loss occurred. He also
notes that accuracy depends on
having those who collect the data
being knowledgeable in the area, on
having reasonable reporting
thresholds, and on verifying the
accuracy and the completeness of
the data through audits. In general,
operational loss data exhibits a large
number of small losses and very few
high impact losses. While it is the tail
behaviour that determines the capital
requirement (since the VaR or TVaR
is usually related to large but unlikely
events which could adversely affect
the operation of the organisation) it
is unfortunately the case that the
best available loss data (i.e from the
organisation itself) relates to the
more frequently observed small
losses. Indeed while many banks
should have adequate data for
modelling high frequency low
severity operational losses, few will
have sufficient internal data to
estimate the tail properties of the
very largest losses. Furthermore
calculating loss distributions for
operational risk by only using
internal data often fails to capture
potential risks and unprecedented
large loss amounts that has huge
impact on the capital. Guidelines
and recommendations for the use of
external loss data is set up in the
Basel II accord and underlines the
importance of using relevant
external data, especially when there
is reason to believe that the
institution is exposed to infrequent,
yet potentially severe, losses. The
absence of reliable internal operational loss data has impeded banks’
progress in measuring and managing
operational risk.
With the impetus of a new Basel
proposal to require capital for
operational risk, banks and vendors
have begun collecting data from
public sources, and have constructed
databases spanning a large crosssection of banks. Over the past 10
years to face operational risk and to
try to better understand the
dynamics have seen the light of
some structures consortium for the
construction of databases on
operational risk. One of these
groups is morexchange
(Multinational Operational Risk
Exchange), a database originally
introduced in November 2000 by
major financial institutions like JP
Morgan, CIBC and Royal Bank of
Canada and operated by a software
company that collects data on
associated banks' operating losses
analyzed and standardized them to
make them as comparable as
possible between banks of different
sizes, and then returned them, on an
aggregate basis to members of the
consortium. Other interbanking
consortiums are XOR (Operational
R i s k
E x c h a n g e
Association) resident in Zurich and
managed by companies
OpVantage (the Fitch group) and
PWC Switzerland and GOLD
(continua a pagina 2)
Banking & Finance Lab
Anno 4, Numero 6
Operational risk losses: what do available databases say?
(Global Operational Loss Database)
operated by British banks come to
host thousands of events of loss,
provided by some thirty participants
(in the United Kingdom and other
countries). Finally the DIOPA (or
DIPO) database (Database Italian
operating losses), run by the Italian
Banking Association with thirty
members.
Among the other prominent
examples of proprietary operational
risk loss event database we can
include the Operational Risk
Insurance Consortium (ORIC) by
the Association of British Insurers
(ABI), OpBase by Aon Corporation,
OpRisk Analytics’ OpRisk Global
Data and OpVantage’s OpVar
database (OpVantage, a division of
Fitch Risk Management, has
recently acquired the IC2 database
of operational loss events).
Recurring to the consortium
databases can contribute to create in
a relatively short time, a database far
more broadly than could be
achieved, inside a single bank, on
the other hand, despite the
standardization activities carried out
by the system manager, it is possible
that the values placed by different
intermediaries reflect, to some
extent, business practices and
different sensitivities, so as not to be
totally consistent among the full
dataset. Most of available databases
are populated mostly by high frequency low severity events, and by a
few large losses. In addition,
different banks had different levels
of completeness in reporting. In
fact, the difficulty of compiling
these data illustrates the enormous
difficulty in performing one key
asp ec t of op erati o nal ri sk
management – collecting data on
relatively infrequent and
unpredictable events. But what
available databases says?
Since 2001, the Risk Management
Group (RMG) of the Basel
Committee has been performing
specific surveys of banks’operational
loss data, with the main purpose of
obtaining information on the
industry’s operational risk
experience, to be used for the
refinement of the capital framework
and for the calibration of the
regulatory coefficients.
The RMG performed two studies –
QIS2—Tranche 1 focused on
internal capital allocation data for
operational risk and information
about other exposure indicators. In
QIS2—Tranche 2, the RMG
gathered data on individual
operational risk loss examples.
The second loss data collection Operational Risk Loss Data
Collection Exercise (LDCE) - was
launched by the RMG of the Basel
Committee on Banking Supervision
in June 2002: the LDCE data
include 47,269 operational loss
events reported by 89 banks from
19 countries in Europe, North and
South America, Asia, and
Australasia, grouped by eight
standardised Business Lines (BLs)
and seven Event Types (ETs). The
RMG collected the number of loss
events and gross loss amounts for
eight business lines: 1) corporate
finance, 2) trading and sales, 3) retail
banking, 4) commercial banking, 5)
payment and settlement, 6) agency
and custody services, 7) asset
ma nag ement, and 8) retail
brokerage. They considered seven
different types of loss events: a)
internal fraud, b) external fraud, c)
employment practices and workplace safety, d) clients, products, and
business services, e) damages to
physical assets, f) business
disruption and system failures, g)
execution delivery.
The LDCE asked participating
banks to provide information on
individual operational losses
exceeding €10,000 during 2001,
among various other data items.
Banks were also asked to indicate
whether their loss data were complete. Feedback on the data collected, which focuses on the
description of the range of
individual gross loss amounts and of
the distribution of these losses
across the BLs/ETs categories, was
returned to industry in March 2003.
Most of the events and the largest
Euro value of the losses were in
retail banking (67% and 39% of all
events and losses respectively) and
commercial banking (13%, and 23%
respectively), which may reflect
where the sample firms do most of
their business. The most likely and
the most costly events were in
external fraud and in execution,
delivery and process management.
Most of the loss events were
relatively small – only one percent
of the sample were events with
losses of one million Euros or more.
However, the large loss events
dominated the total value of the
losses. Events with losses over one
million Euros accounted for almost
three-fourths of the total losses.
For what concern operational loss
data provided by two vendors,
OpRisk Analytics and OpVantage,
both vendors gather information on
operational losses exceeding $1
million. These vendors collect data
from public sources such as news
reports, court fillings, and SEC
fillings.
Fontnouvelle in a recent article find
that the two databases are
remarkably similar at the aggregate
level. In fact, the 50th percentile loss
($6 million) and the 75th percentile
loss ($17million) are the same in
both databases. Even the 95th
percentile losses are very close,
equalling $88 million in the OpRisk
Analytics database and $93 million
in the OpVantage database. The two
databases are also quite similar at the
business line level. The business line
with the most observations in both
databases is retail banking, which
accounts for 38% of all OpRisk
losses and 39% of all OpVantage
losses. While retail banking has the
largest number of losses, these
losses tend to be smaller than in
other business lines. Similarly, the
other two business lines with a large
number of losses in both databases,
commercial banking and retail
brokerage, also have smaller losses
than other business lines at the 95th
percentile. In contrast, trading and
sales has fewer observations, but has
the largest loss by business line at
the 95th percentile. These results
suggest how important it will be to
capture both frequency and severity
in determining appropriate capital
by business line. By observing
reports descriptive statistics for
losses that occurred outside the
United States, the most striking
result is that non-U.S. losses are
significantly larger than U.S. losses.
At both the aggregate and business
line level, the reported percentiles
for the non-U.S. losses are
approximately double the equivalent
percentiles for U.S. losses. Another
striking result is that the two
databases are less similar with
respect to non-U.S. losses than with
respect to U.S. losses. In retail
banking, for example, the 95th
percentile loss is $272 million for
the OpVantage data but only $101
million for the OpRisk Analytics
data. Author conclude that data
collection processes may differ for
U.S. versus non-U.S. losses, and that
the underlying loss distributions may
also differ.
The event types with the most
losses are Internal Fraud and
Clients, Products, and Business
Practices. Those with the fewest
losses are Damage to Physical
Assets and Business Disruption and
System Failures. The infrequency of
these loss types may be an accurate
reflection of their rarity. However, it
could be that these types of losses
are rarely disclosed, that loss
amounts are not disclosed even if
the event is, or that they are often
misclassified under different loss
types. Summarize Fontnouvelle
discussions with banks suggest that
a typical large internationally active
bank experiences an average of 50
to 80 losses above $1 million per
year. Smaller banks and banks
specializing in less risky business
lines may encounter significantly
fewer losses in excess of $1million,
and extremely large banks weighted
towards more risky business lines
could encounter more large losses.
Summarizing, the analysis indicates
that supplementing internal data
with external data on extremely large
rare events may significantly improve banks’ models of operational risk.
Additionally, we stress the evidence
that the data of public databases will
suffer from a selection bias which
overestimates the probability of very
high impact events and that properly
accounting for this bias significantly
reduces the estimated operational
risk capital requirement. If the
probability that an operational loss
is reported increases as the loss
amount increases, there will be a
disproportionate number of very
large losses relative to smaller losses
appearing in the external databases.
Failure to account for this issue
could result in sample selection bias.
Also because the external databases
include losses experienced by a wide
range of financial institutions, most
of severity estimates should apply to
the “typical” large internationally
active bank. Banks with better than
average risk controls may have a
thinner-tailed severity distribution,
which would reduce the likelihood
of very large losses. Similarly, banks
with worse than average controls
may have an increased exposure to
large losses.
Nella foto:
Giuseppe Galloppo,
CEIS Economy
Foundation, University
of Rome Tor Vergata
[email protected]
Pagina 2
Banking & Finance Lab
ERES AWARD 2010
Journal of Property
Investment & Finance
Prize for the Best Paper in
Real Estate Finance
Claudio Giannotti, Lucia
Gibilaro e Gianluca
Mattarocci hanno ottenuto il
riconoscimento grazie al contributo "Liquidity risk exposure
for specialised and unspecialised real
estate banks: evidence from the
Italian market".
Il premio verrà consegnato
durante il convegno ERES
2011 che si terrà
ad
Eindhoven (Olanda) dal 15 al
18 Giugno 2011.
Per maggiori informazioni
www.eres2011.com
ASSOCIAZIONE
ITALIANA di SCIENZE
REGIONALI (A.I.S.Re.)
Elezione del Presidente
Riccardo Cappellin è stato
e l e t t o P r e s id e n t e d e l l '
Associazione Italiana di
Scienze Regionali (A.I.S.Re.)
per il periodo 2010-2013.
L'A.I.S.Re. è una associazione
scientifica che si propone di
sviluppare attività di ricerca,
formazione e divulgazione
nelle scienze regionali, quali gli
studi che trattano del ruolo
dello spazio e del territorio
nell'economia e nella società a
scala internazionale,
europea, nazionale e regionale.
Ad essa partecipano ricercatori
universitari
di diverse
discipline, del CNR, degli
Istituti Regionali di Ricerca e
di altre istituzioni.
Per maggiori informazioni:
www.aisre.it
Anno 4, Numero 6
Le Universiadi del trading 2011 Anteprima
Si sono concluse lo scorso 30
settembre, dopo oltre sei mesi di
competizione, le Universiadi del
trading 2010, primo
campionato di trading online con
denaro reale, promosso da
Directa, al quale hanno
partecipato oltre 150 studenti
delle lauree magistrali
provenienti da trenta università,
per un totale di 44 squadre in
gara. La premiazione, avvenuta a
Milano presso Palazzo
Mezzanotte il giorno 29 ottobre
2010 nel corso del TOL Expo, ha
decretato la squadra Luiss Blue
Team (Università Luiss)
coordinata da Emilio
Barone, team vincitore del
campionato, con una
performance complessiva pari al
27,31%. Segue a breve
distanza il team Mgei Bocconi
(Università Bocconi), il cui
docente di riferimento è Laura
Zanetti, che ha realizzato un
+19,41%, incalzato dagli Alpha
Brothers (Università Roma Tor
Vergata),
coordinati da Ugo
Pomante, che si sono
posizionati al terzo posto con un
risultato positivo di 16 punti
percentuali. E’ stata inoltre
premiata la squadra della facoltà
di Scienze Politiche dell’
Università di Firenze, guidata da
Luciano Segreto,
che si è
aggiudicata il “Premio della
critica”, assegnato al team che nel
corso delle settimane di gioco ha
registrato con una certa
regolarità performance superiori
alle altre squadre.
Visto il successo riscosso dall’
iniziativa, si terrà in data 29
novembre 2010 presso la facoltà
di economia dell’Università Tor
Vergata la presentazione in
anteprima del campionato 2011.
Maggiori informazioni sull’
iniziativa al sito: www.directaworld.it
Nella foto:
Duccio Martelli, dottore
di ricerca in Banca e
Finanza dell’Università
Tor Vergata
NOTIZIE DAL
DOTTORATO DI
RICERCA IN
BANCA E FINANZA
D-DAY (autunno)
Università di Roma
“Tor Vergata”
30 Novembre - 1 Dicembre 2010
Nei giorni 30 novembre e 1
dicembre si terrà presso
l’Università degli Studi di
Roma ”Tor Vergata” il D-Day
autunnale, nel corso del quale i
dottorandi presenteranno lo
stato di avanzamento dei loro
lavori. In particolare, i partecipanti al XXIV ciclo presenteranno i loro autumn paper,
quelli del XXV illustreranno le
proposte di ricerca definitive,
mentre i nuovi ammessi del
XXVI ciclo si presenteranno
alla comunità del dottorato
esponendo una tematica di
loro interesse nel corso dei
personal workshop.
[email protected]
Segnalazione: “Il risk management nei fondi immobiliari.
Analisi, valutazione e cultura della compliance.”
Lo
scenario
macroeconomico
globale e i fragili
equilibri venuti a
crearsi a seguito
della crisi rendono
più complesso il
ruolo del risk
management aziendale.
E’ cresciuta l’attenzione degli
operatori e delle autorità
di
vigilanza verso il controllo dei rischi
delle SGR e dei fondi immobiliari
da esse gestiti. La crisi ha
sottolineato l’incertezza nella stima
dei flussi finanziari attesi dagli
investimenti immobiliari,
spingendo, tra l’altro, gli istituti di
credito ad adottare criteri più
selettivi nella concessione dei
finanziamenti. In tale contesto
diventa essenziale disporre di un
sistema efficace di risk management
per poter influenzare i processi
decisionali aziendali e consentire ai
manager di compiere scelte
razionali. Il volume ha l’obiettivo di
illustrare i diversi aspetti del risk
management nell’ambito dei fondi
immobiliari in termi ni di:
individuazione e misurazione dei
fattori di rischio; analisi del rischio
finanziario; costituzione e gestione
di un portafoglio immobiliare;
definizione di modelli di analisi dei
fattori rilevanti del rischio
immobiliare; illustrazione della
funzione e analisi di alcuni esempi
significativi di risk management.
Sono descritti, infine, i benefici
attesi di una gestione efficace del
rischio, focalizzando l’attenzione
anche sulle aspettative e sulle
esigenze degli operatori
professionali, nonché sulle criticità
da gestire e sulle sfide aperte per il
settore, nell’attuale quadro
economico e normativo di
riferimento.
Il volume, nato dalla collaborazione
tra Beni Stabili Gestioni SGR e il
Laboratorio di Finanza Immobiliare
del Dottorato di ricerca in Banca e
Finanza dell’Università di Roma
Tor Vergata, coniuga gli aspetti
metodologici e scientifici con quelli
prettamente operativi.
Claudio Giannotti,
Straordinario di
Economia degli
Intermediari Finanziari
presso l’Università LUM
Jean Monnet.
.
[email protected]
Il risk management nei
fondi immobiliari.
Claudio Giannotti (a cura di),
Bancaria Editrice, 2010, pp.
220, €30.00).
Pagina 3
Banking & Finance Lab
Anno 4, Numero 6
Le prossime conferenze
internazionali
A cura di Gianluca Mattarocci, [email protected]
CEIS E UNIVERSITÀ DI ROMA "TOR VERGATA"
XIX INTERNATIONAL TOR VERGATA CONFERENCE ON
BANKING AND FINANCE:
NEW FRONTIERS OF BANKING AND FINANCE AFTER THE
GLOBAL CRISIS
Pubblicazioni internazionali
Schwizer P., Farina V., Stefanelli V., "Dimension, Structure and
Skill Mix in European Boards: Are They Convergin towards a common
model of corporate governance?" in Corporate Ownership and
Control Journal, Vol. 8/2010.
B&F on the road
Roma, 13-17 Dicembre 2010
www.economia.uniroma2.it/mbf/index.php?p=11
CONVEGNO CONFAPI LUM UNICREDIT
THE UNIVERISTY OF NEW SUTH WALES
Presentazione dell’indagine congiunturale II semestre 2010
23RD AUSTRALASIAN FINANCE AND BANKING
CONFERENCE
Sydney (Australia), 15-17 Dicembre 2010
www.banking.unsw.edu.au
IBFR
WINTER GLOBAL CONFERENCE ON
BUSINESS AND FINANCE
Las Vegas (USA), 2-5 Gennaio 2011
www.theibfr.com/call-us.htm
THE AMERICAN FINANCE ASSOCIATION
2011 ANNUAL MEETING
Denver (Colorado, USA): 7-9 Gennaio 2011
www.afajof.org
AMERICAN SOCIETY OF BUSINESS
AND BEHAVIORAL SCIENCES
18TH ANNUAL CONFERENCE Las Vegas (Nevada, USA), 24-27 Febbraio 2011
www.asbbs.org
THE
THE
DUKE UNIVERSITY AND
UNIVERSITY OF NORTH CAROLINA
Casamassima, 14 ottobre 2010
Claudio Giannotti
10TH EUROPEAN CORPORATE GOVERNANCE
CONFERENCE
Brussels, 6-7 Dicembre 2010
Schwizer Paola, Casiraghi Rosalba, Stefanelli Valeria,
Enhancing Board Effectiveness: What about Induction and
Training Programs for Directors?
23RD AUSTRALASIAN FINANCE AND BANKING
CONFERENCE
Sydney (Australia), 15-17 Dicembre 2010
Cotugno Matteo, Stefanelli Valeria, Torluccio Giuseppe
Bank Intermediation Models and Portfolio Default Rates
What's the Relation?
ASBBS 18TH ANNUAL CONFERENCE Las Vegas (USA), 24-27 Febbraio 2011
Massimo Caratelli
The Impact of financial Education and Transparency on Borrowing
Decisions. The Case of Consumer Credit.
Call for paper
DUKE-UNC CORPORATE FINANCE CONFERENCE
Durham (North Carolina, USA): 11-12 Marzo 2011
Deadline: December 20, 2010
http://faculty.fuqua.duke.edu/corpfinance
EUROPEAN FINANCIAL MANAGEMENT ASSOCIATION
2011 ANNUAL MEETING
Braga (Portogallo): 22-25 Giugno 2011
Deadline: 15 Gennaio 2011
www.efmaefm.org
Dalla redazione di B&F Lab…
...tanti auguri a Federica Sist e Silvio
per la nascita di Edoardo !!!
La sede di Terni della Facoltà di Economia dell’
Università degli Studi di Perugia organizza per la
prima settimana del mese di maggio 2011 un
convegno dal titolo: “Banche, mercati e territorio”
in memoria del Prof Sergio Corallini. A tale scopo, la
Facoltà bandisce un call for paper con scadenza 28
febbraio 2011. Maggiori dettagli saranno divulgati nel
corso delle prossime settimane.
Dalla redazione di B&F Lab…
...tanti auguri a Valeria e Cristiano !!!
Pagina 4