1. No category

Diris-Keijsers-Kole
Peltonen-Rancan-Sarlin
Session on “Macro Risk”
Discussion
Olivier Loisel
crest
8th Financial Risks International Forum
“Scenarios, Stress, and Forecasts in Finance”
Paris, March 31, 2015
Olivier Loisel, Crest
Discussion on “Macro Risk”
Paris, March 31, 2015
1 / 10
Diris-Keijsers-Kole
Peltonen-Rancan-Sarlin
Two papers on “macro risk”
Two nice papers, with two different perspectives on “macro risk”:
1
Diris-Keijsers-Kole: correlation between the business cycle and
the default rate on bank loans
the loss given default on bank loans
2
Peltonen-Rancan-Sarlin: crisis-predicting properties of
country-level macro, financial, and banking indicators
within-country and cross-country financial linkages
Olivier Loisel, Crest
Discussion on “Macro Risk”
Paris, March 31, 2015
2 / 10
Diris-Keijsers-Kole
Peltonen-Rancan-Sarlin
Data and model
Data sources:
bank-loan variables: Pan-European Credit Database Consortium
macro variables: OECD
Data content:
bank-loan variables: DR, LGD, seniority, security, asset class, industry
macro variables: GDP, IP, UR
Model overview:
LGD: mixture of two normal distributions (good and bad losses)
DR: Bernoulli distribution
The default and bad-loss probabilities depend on
the loan characteristics
the same latent factor αt
The model is estimated with the Expectation Maximization algorithm
Olivier Loisel, Crest
Discussion on “Macro Risk”
Paris, March 31, 2015
3 / 10
Diris-Keijsers-Kole
Peltonen-Rancan-Sarlin
Main results
1
DR and LGD respond to αt in the same direction
2
αt is related to the business cycle:
negatively to GDP and IP (in the same quarter)
positively to UR (three or four quarters later)
3
Observable bank-loan characteristics matter
4
Most of the variation is due to changes in the probability of a bad loss
Olivier Loisel, Crest
Discussion on “Macro Risk”
Paris, March 31, 2015
4 / 10
Diris-Keijsers-Kole
Peltonen-Rancan-Sarlin
Comments I: data
Are the OECD and PECDC datasets matched at the European level?
If so, is there evidence that all or most borrowers are European?
And that borrowers’ nationality is representative of Europe?
Yearly bank-loan observations are affected to Q3 (other Qs missing):
is the third quarter representative of the year (e.g., in 2008)?
why not instead transform quarterly data into yearly data?
Why treat non-default observations as missing values?
Why not consider information on
the maturity of loans?
the period at which default occurs over the loan lifespan?
What about distinguishing crisis from non-crisis periods?
Olivier Loisel, Crest
Discussion on “Macro Risk”
Paris, March 31, 2015
5 / 10
Diris-Keijsers-Kole
Peltonen-Rancan-Sarlin
Comments II: implications
Implications for banks: should they conduct a similar analysis using
more data (price, quality, clauses, etc) on their own loans?
How would the results be affected?
Implications for micro-/macro-prudential authorities (Basel III):
should they combine credit data with macro forecasts?
But then what about the Lucas critique?
Olivier Loisel, Crest
Discussion on “Macro Risk”
Paris, March 31, 2015
6 / 10
Diris-Keijsers-Kole
Peltonen-Rancan-Sarlin
Data and measure of banking-sector centrality
Data sources:
banking crises: ESCB
macro, financial, banking indicators: ECB-BSI
within-country financial linkages: ECB-EAA
cross-country financial linkages: ECB-BSI
Measure of banking-sector centrality:
four candidate measures: degree-in, degree-out, betweenness, closeness
four candidate instruments: loans, deposits, securities, shares
principal-component analysis (PCA) on the 4 × 4 = 16 variables
Olivier Loisel, Crest
Discussion on “Macro Risk”
Paris, March 31, 2015
7 / 10
Diris-Keijsers-Kole
Peltonen-Rancan-Sarlin
Model and main results
Main features of the model:
logit analysis
threshold values for probabilities
goodness-of-fit measure called “usefulness”
Main results:
considering cross-border linkages increases usefulness
considering all linkages increases usefulness further
loans and securities are the most important instruments for usefulness
Robustness analysis with respect to
definition of usefulness
forecast horizon
probability thresholds
real-time analysis
Olivier Loisel, Crest
Discussion on “Macro Risk”
Paris, March 31, 2015
8 / 10
Diris-Keijsers-Kole
Peltonen-Rancan-Sarlin
Comments I: data
What is the definition of a banking crisis?
Data over 2000Q1-2012Q1 for 14 European countries:
how many banking-crisis observations all in all?
how many over 2000Q1-2005Q2 (in-sample analysis)?
how correlated are they over time and across countries?
What are the main estimated PCA components?
Olivier Loisel, Crest
Discussion on “Macro Risk”
Paris, March 31, 2015
9 / 10
Diris-Keijsers-Kole
Peltonen-Rancan-Sarlin
Comments II: cross-border transmission of crises
Could networks affect both crisis occurrence and crisis severity?
Why not mix national indicators with cross-border linkages?
use national indicators to compute “domestic-crisis” probabilities
use cross-border linkages to infer “domestic- or imported-crisis” prob.
Why not consider the joint distribution of national banking crises?
What implications for the ESRB and national prudential authorities?
Olivier Loisel, Crest
Discussion on “Macro Risk”
Paris, March 31, 2015
10 / 10