school of business and economics discussion on “risk horizon predictors of euro area financial...

School of Business and Economics

Discussion on “Risk horizon Predictors of Euro Area Financial

Stress” by Thomas Lejeune

Stefan Straetmans

Maastricht University (School

of Business and Economics)

Risk forum Paris, 30-31 March 2015


Discussion in a nutshell

• What is the paper about? • Strengths • Weaknesses


What is the paper about?

• Traditionally (e.g. CAPM, APT, FFC 4-factor model)

“market” risk premium exogenously determined

→ sample average ≈ expected market return • Here: asset pricing model for different risk premia • Nonparametric (model free) approach: no utility

function or distributional return assumptions, only knowledge of first 4 moments assumed

• New concept of risk related to “risk horizon”• Identification of model parameters by use of term structure

model of interest rates• Use of risk premia as EWI in logit models for systemic stress


Strengths • Dazzling analytic level• Novel concept of individual asset risk (“risk horizon”) • Take into account heavy tails via higher moments• Nonparametric character of the approach

→ parsimony in terms of model assumptions reduces

model risk as compared to previous approaches


Weaknesses (1) • “Hermetic” write-up: paper should in principle be

understandable for every financial economist

(not necessarily a specialist in asset pricing) • Journal you want to submit?

→ if it is for a specialized math journal, OK

→ if mainstream finance journal, more intuition

explanation necessary

→ three more or less concentrically overlapping papers; fine for a phd thesis but what about a journal article? You refer a lot to previously unpublished research, make one master paper out of this?


Weaknesses (2) • How accurate is the Chebyshev upper bound? Is extreme

value analysis (EVT) more accurate alternative?• Simplifying assumptions need more explanation

- concept of risk horizon

- knowledge of 2nd-4th moment also characterized

by estimation risk

- estimates of the tail index of financial returns suggest:

- α is the number of bounded moments

- in other words: the variance seems to exist but the kurtosis often does not!

43ˆ2


Weaknesses (3) • Accuracy of risk factors? Confidence bands?• Tables 5-6: after adding controls, only credit trend stays

significant as EWI, it seems• Tables 5-6: why does the credit trend coefficient

become so huge (in absolute value) after adding controls? • Logit output

- way too many tables!

- discuss marginal logit effects

- lots of controls, lack of parsimony

- correlation, multicollinearity of control variables?

- without control variables, pseudo-R2 low

- static vs. dynamic logit (lagged dependent variable)


All in all • High potential paper • Should be readable for a wider audience than asset

pricing except if you focus on very specialized journal

school of business and economics discussion on “risk horizon predictors of euro area financial...

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