Dynamic modelling of climate-related shocks in the fund sector.
Date: 2024-01-19
Institutions: European Securities and Markets Authority
Subjects: climate change, financial market, financial risk, financial stability, investment, macroeconomics, risk management, sustainable development

19 December 2023 
ESMA50-524821-3073

ESMA TRV Risk Analysis 
Sustainable Finance 
Dynamic  modelling  of  climate-
related shocks in the fund sector 

0 

ESMA TRV Risk Analysis 

19 December 2023 

2 

ESMA Report on Trends, Risks and Vulnerabilities Risk Analysis 

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ISBN 978-92-95235-03-8, DOI 10.2856/403241, EK-02-23-265-EN-N 

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ESMA TRV Risk Analysis 

19 December 2023 

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Sustainable Finance 

Dynamic  modelling  of  climate-
related shocks in the fund sector 

Contact: alexander.harris@esma.europa.eu1 

Summary 

Identifying vulnerabilities of the investment fund sector in climate stress scenarios is of vital importance 
given the sector's size in the financial system and its crucial role financing the green transition. In line 
with recent ESMA mandates in this regard, this article outlines a first approach to dynamically modelling 
the impact of asset price shocks from adverse scenarios involving climate-related risks. A given set of 
asset price shocks is the core input to the model, which comprises static impacts – the immediate price 
impact on funds’ direct and indirect asset holdings – plus dynamic impacts, such as inflows and outflows 
by investors and portfolio rebalancing by managers. The present analysis focuses on the direction and 
sequencing  of  dynamic  impacts,  showing  that  dynamic  responses  can  exacerbate  falls  in  total  fund 
assets  due  to  outflows  following  an  initial  shock.  Portfolio  rebalancing,  in  contrast,  only  affects  the 
sensitivity of fund valuations to subsequent shocks. The article concludes by discussing the sensitivity 
of the results and possibilities for further research.  

1   This article was written by Adrien Amzallag, Alexander Harris and Paul Reiche. 

 
 
 
 
 
 
 
 
 
ESMA TRV Risk Analysis 

19 December 2023 

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Introduction  
The  investment  fund  sector,  with  assets  under 
management  (AuM)  of  over  EUR 60tn  globally 
and EUR 17tn in the EEA, is a key component of 
the  financial  system. 2  Its  exposure  to  climate-
related  repricing  shocks  has  implications  for 
financial  stability,  especially  as  such  shocks 
could  arise  during  adverse  macroeconomic 
conditions. At the same time, the sector’s crucial 
role in financing economic activity and the green 
transition may be limited by climate shocks. The 
task of identifying vulnerabilities in the investment 
fund sector to climate-related shocks is therefore 
of major importance both from the perspective of 
financial stability and sustainability.3  

the 

financial  system, 

To anticipate the impact of climate-related shocks 
on 
the  European 
Commission  (EC)  has  mandated  the  ESAs  to 
perform  regular  climate  change  stress  tests  or 
scenario  analyses  and  to  develop  methods, 
parameters and scenarios for supervisors to use 
in their own climate stress testing (EC, 2021). In 
addition, as detailed in Textbox 1, the ESAs have 
a mandate  to conduct  a one-off climate change 
stress 
in 
the 
coordination with the ECB and ESRB, reporting 
results by 1Q25 (EC, 2023).  

financial  sector 

test  across 

risk, 

including  simulations 

ECB  and  ESRB  (2023)  present  recent  work  on 
for  managing 
macroprudential 
frameworks 
climate 
the 
for 
investment  fund  sector  by  ESMA  based  on  the 
methodology set out in this  article, and building 
on  previous  reports  (ECB  and  ESRB  2021; 
2022).  Within  the  fund  sector,  Amzallag  (2021) 
provides  climate  risk  scenario  analysis  of  a 
network  of  funds,  indicating  potential  system 
losses  of  over  EUR 400bn 4.  Crisóstomo  (2022) 
estimates investment fund losses due to climate 
transition  risk  of  EUR  17.5bn  in  Spain  alone, 
equivalent  to  over  EUR  800bn  in  losses  if 
extrapolated to the whole European fund sector. 

To support the development of ESMA’s climate-
related  stress  testing  of  the  investment  fund 
sector  in  line  with  these  new  mandates,  this 
article  outlines  a  possible  dynamic  approach  to 
modelling the impact of asset price shocks.  

2   Sources:  Global 

from 

figure 

Investment  Company 
Institute (2023) covers regulated open-ended funds as of 
2Q23. Assumed exchange rate: EUR/USD = 1.05. EEA 
figure  from  European  Fund  and  Asset  Management 
total  Assets  under 
covers 
Association 
Management (AuM) in UCITS and AIFs, excluding those 
domiciled in UK, as of end-2022. 

(2023) 

3   As  explained  in  the  next  section,  these  climate  risks 
include  transition  risks  and physical  risks. The  dynamic 

First,  the  article  explains  how  climate  scenarios 
are  specified  and  used  to  simulate  shocks  to 
asset prices, through modelling work carried out 
by other authorities and international bodies.  

Next,  the  article  describes  ESMA’s  dynamic 
modelling  of  effects  within  the  investment  fund 
sector given a set of asset price shocks, including 
an example calibration.5 The model covers static 
impacts – the immediate price impacts on funds’ 
direct and indirect asset holdings – and dynamic 
impacts,  such  as 
inflows  and  outflows  by 
investors, portfolio rebalancing by managers and 
associated  second-round  price  impacts.  These 
dynamic  effects  are  especially  relevant  among 
funds, which typically offer frequent redemptions 
and have relatively liquid positions compared with 
other  parts  of  the  financial  sector.  Finally,  the 
article presents and discusses the main findings. 

Textbox   1 
Fit-for-55 one off exercise 

In March 2023, the EC issued a request for a one-off 
scenario  analysis  exercise  to  be  conducted  jointly  by 
the ESAs, the ECB and the ESRB, as envisaged by the 
Commission’s Strategy for Financing the Transition to 
a Sustainable Economy (EC, 2021; EC, 2023).  

The one-off exercise will test the resilience of the EU 
financial  system  during  the  implementation  of  the 
Commission’s  ‘Fit  For  55’  package  to  reduce  net 
greenhouse  gas  emissions  by  at  least  55%  by  2030 
from  1990  levels.  The  exercise  aims  to  anticipate 
financial shocks that could threaten financial stability or 
hinder the financing of the green transition, with results 
due by 1Q25. The Commission’s request specifies that 
the exercise should analyse two adverse scenarios: 

i.  A  first  scenario  involves  climate-change  related 
risks  that  materialise  in  the  near  term,  with  asset 
price 
sudden 
reassessment of transition or physical risks.  

triggered  by  a 

corrections 

ii.  A second scenario combines these climate-related 
risks with other, non-climate-related stress factors.  

The  analysis  in  the  present  article  is  not  based  on 
either adverse scenario from the one-off exercise, and 
the  illustrative  results  below  are  separate  from  that 
exercise. Nonetheless, ESMA may adapt the general 
modelling 
to  specific 
framework  presented  here 
mandates and tasks, including the one-off exercise. 

modelling framework presented can be applied to asset 
price shocks arising from either risk source (or both). The 
adverse  scenario  used  to  illustrate the model  considers 
transition risk. 

4  

In that analysis, “brown” funds’ losses are 2-3 times those 
of “green” funds and have greater systemic impact.  

5   For illustration, the article considers an adverse scenario 
over  a  5-year  horizon,  though  longer-term  modelling 
could follow the same approach. 

 
 
 
 
 
 
ESMA TRV Risk Analysis 

19 December 2023 

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Identifying scenarios  
Repricing  shocks  may  arise  in  different  climate 
scenarios,  which  can  be  classified  according  to 
their  level  of  physical  risks  and  transition  risks 
(NGFS,  2020;  BIS,  2021).  Physical  risks  are 
financial  costs  associated  with  changes 
in 
climate.  These changes may be acute, such as 
more frequent or severe adverse weather events, 
or  chronic,  such  as  sea  level  rises.  Transition 
risks involve changes to climate policy, changes 
in  consumer  behaviour  or  changes 
the 
development of technologies that affect the shift 
to a lower-carbon economy (via net emissions or 
in relation to the mitigation of physical risks). 

in 

conditions  may  complicate  efforts  to  achieve  a 
timely,  orderly  transition.  Additionally,  physical 
risks  can  affect  macroeconomic  variables  and 
increase  market  volatility  (e.g.  through  physical 
disruption  of  value  chains,  operational  outages, 
lower agricultural yields, or higher morbidity). 

Chart   1 
NGFS climate scenarios 
Range of climate scenarios 

to 

The Network for Greening the Financial System 
(NGFS)  has  produced  a  range  of  different 
scenarios 
facilitate  comparable  analysis 
across  jurisdictions  (Chart 1).6 For  example,  the 
Current Policies scenario, in which only currently 
implemented  policies  remain  in  place  in  the 
coming  decades,  entails  higher  physical  risks 
than  alternative  scenarios  in  which  additional 
policy action is taken to reduce emissions. In the 
alternative 
are 
implemented that reduce physical risks.  

scenarios, 

policies 

new 

However, while policy action may be carried out 
in an orderly fashion through timely, coordinated 
measures (bottom left quadrant of Chart 1), there 
is  also  a  risk  that  it  could  be  undertaken  in  a 
disorderly  fashion,  delayed  or  uncoordinated 
across  countries  and  economic  sectors  (upper 
quadrants  of  Chart 1).  For 
the 
Below 2°C  scenario  assumes 
that  climate 
policies  become  steadily  more  stringent.  The 
Delayed  Transition  scenario  results  in  similar 
levels of physical risk, but via later, more abrupt 
policy  tightening  that  brings  greater  transition 
risk. 

instance, 

Macroeconomic conditions 

Fully  identifying  a  scenario  for  the  purpose  of 
assessing financial sector vulnerabilities includes 
specifying  macroeconomic  conditions.  For 
example,  a  baseline  scenario  may  assume 
relatively  benign  macroeconomic  variables  in 
conjunction  with  current  climate  policies.  When 
identifying  adverse  scenarios,  it  is  important  to 
consider  the  possibility  that  climate  risks  may 
materialise at a time of existing macroeconomic 
Indeed,  adverse  macroeconomic 
stress.

7

Note: Diagram reproduced from NGFS website, which gives full definitions of the 
scenarios, as updated in 2023 (Phase IV). The positioning of the scenarios (grey 
circles) is approximate. The top left quadrant represents disorderly scenarios with 
relatively high transition risks and low physical risks.   In particular, the ‘Delayed 
Transition’ scenario assumes global annual emissions do not decrease until 2030. 
Strong  policies  are  then  needed  to  limit  warming  to  below  2 °C,  and  negative 
emissions are limited. The top right quadrant represents scenarios where limited 
action  is  taken  late,  entailing  relatively  high  transition  and  physical  risks.  In 
particular,  ‘Fragmented  World’  assumes  a  delayed  and  divergent  climate  policy 
response among countries globally. Those with net zero targets only achieve 80% 
of  the  targets,  while  other  countries  follow  current  policies.  The  bottom  right 
quadrant involves high physical risks but lower transition risks. ‘NDCs’ = Nationally 
Determined  Contributions,  which  includes  all  pledged  policies  even  if  not  yet 
implemented,  such  that  emissions  decline  but  nonetheless  lead  to  2.6 °C  of 
warming.  ‘Current  Policies’  assumes  only  currently  implemented  policies  are 
preserved. The bottom left quadrant involves lower  physical and transition risks. 
‘Below 2 °C’ gradually increases the stringency of climate policies, giving a 67 % 
chance  of  limiting  global  warming  to  below  2 °C.  ‘Net  Zero  2050’  limits  global 
warming to 1.5 °C through stringent climate policies and innovation, reaching net 
zero  CO₂  emissions  around  2050. ‘Low  Demand’  assumes  that  significant 
behavioural  changes,  reducing  energy  demand,  mitigate  the  pressure  on  the 
economic system to reach global net zero CO₂ emissions around 2050. 
Sources: NGFS, ESMA. 

Mapping scenarios to asset prices  

For  a  given  scenario,  the  transition  or  physical 
risks  involved  will  directly  affect  the  financial 
position of firms across the economy. This direct 
impact  may  be  greater  in  some  sectors  than 
others.  For  example,  the  profitability  of  firms 
carrying  out  carbon-intensive  activities  such  as 
transportation  or  resource  extraction  may  be 

6   The  NGFS  is  a  group  of  central  banks and  supervisors 
committed  to  sharing  best  practices,  contributing  to  the 
development  of  climate  –and  environment–  related  risk 
management  in  the  financial  sector  and  mobilising 
mainstream  finance  to  support  the  transition  toward  a 
sustainable  economy.  As  of  24  November  2023,  the 
NGFS consisted of 129 members (including ESMA) and 

21 observers. The scenarios discussed in this article are 
from Phase IV of the NGFS framework. 

7   The need to consider vulnerabilities arising from climate-
related  and  non-climate-related 
risks  materialising 
together  is  highlighted  in  the  mandate  for  the  one-off 
scenario testing exercise set out in EC (2022). 

 
 
 
 
 
 
 
 
ESMA TRV Risk Analysis 

19 December 2023 

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especially  sensitive  to  an  increase  in  carbon 
prices  or  availability  of  emission  allowances,  or 
other  constraints 
imposed  by  new  climate 
policies.  An  increase  in  transition  risk  under  a 
given  scenario  versus  a  baseline  will  therefore 
lead  to  a  relatively  large  fall  in  equity  prices  or 
increase  in  bond  yields  for  these  firms,  other 
things equal.  

Additionally, transition or physical risks will affect 
firms’ financial positions through macroeconomic 
channels,  e.g.  by  reducing  economic  growth 
compared  to  the  path  implied  by  the  assumed 
underlying macroeconomic conditions.  

Finally, quantifying the overall impact of transition 
and  physical  risks  in  different  scenarios  is 
complicated  by  interactions  between  direct  and 
feedback 
indirect 
mechanisms.  For  example,  changes  in  asset 
prices may lead to a change in the discount rate, 
which in turn affects asset prices.  

potential 

effects 

and 

Taking  these  effects  into  account  results  in  a 
specification of asset price trajectories over time 
versus  a  baseline.  This  specification  is  the 
starting point for detailed modelling of the impact 
on  a  part  of  the  financial  sector  such  as 
investment funds.  

Illustrative assumed scenarios 

Given  a  scenario  and  a  baseline  that  specify 
asset prices for a given period and frequency, the 
impact  on  the  investment  fund  sector  can  be 
modelled  in  different  ways.  To  illustrate  the 
different possibilities, we consider a scenario and 
baseline over 5 years (2023 to 2027) provided in 
ECB and ESRB (2023), as follows.8 

Baseline scenario: 

–  Macroeconomic  conditions  per  baseline  in 

EBA (2023). 

–  Climate-related variables per NGFS Current 

Policies scenario. 

Adverse scenario:  

–  Macroeconomic  conditions  per  adverse 
scenario in EBA (2023), driving the majority 
of the drop in asset prices. 

–  Climate-related  variables  are  shocked  in 
2023 due to a sudden, disorderly change in 
policy  stance  in  the  context  of  adverse 
macroeconomic  conditions,  as  in  ECB  and 
ESRB (2023). 

The adverse scenario considered for expositional 
purposes in this article  involves an initial shock. 
The  shock  is  a  combination  of  (i)  adverse 
macroeconomic  conditions  that  arise  for  non-
climate-related reasons and (ii) a climate-related 
shock  in  the  form  of  a  disorderly  transition, 
including  a  spike  in  the  carbon  price. 9  The 
adverse scenario aims to be severe but plausible. 
It generates very large asset price changes, with 
equities typically falling in price by 45-70% in the 
first year primarily due to a sudden deterioration 
in  macroeconomic  conditions,  with  the  effect 
exacerbated  as  governments  suddenly  impose 
climate policy  measures in a disorderly fashion. 
In subsequent years, asset prices are assumed 
to  follow  a  gradual  and  partial  recovery.  The 
specification of the scenario is outside the scope 
of the ESMA modelling approach within the fund 
sector.  ECB  and  ESRB  (2023)  provide  an 
accompanying narrative.10 

Modelling  investment  fund 
sector impacts  
To  model  how  the  investment  fund  sector  is 
affected by a given scenario, the starting point is 
a  set  of  asset  prices.  For  simplicity,  this  article 
restricts attention to shocks to equity prices, and 
considers  the  resulting  change  to  the  value  of 
EEA-domiciled investment funds.11 

8   The scenarios in ECB and ESRB (2023) combine shocks 
from  the  2023  EBA  stress  test  and  the  2022  version 
(Phase  III)  of  the  NGFS  scenarios.  Shocked  variables 
vary  across  countries  and  sectors.  The    main  adverse 
scenario  implies  that  the  carbon  price  increases  more 
than 20 times as part of an initial shock, which is modelled 
as  frontloaded  version  of  the  NGFS  Delayed Transition 
scenario.  ECB  and  ESRB  (2023)  also  consider  an 
additional  adverse  shock  in  the  form  of  an  increase  in 
uncertainty and risk premia, which  is excluded from the 
present analysis. 

9   Around 80% of the equity price shock is in fact attributable 
to  the  macroeconomic  conditions.  As  noted  above, 
climate  risks  may  materialise  at  a  time  of  existing 
macroeconomic  stress,  creating  a  vulnerability.  The 
modelling  framework  would  however  apply  equally  to  a 

scenario  with  price  shocks  arising  solely  from  climate-
related risks (whether physical risk, transition risk or both). 

10   A  summary  of  the  possible  narrative  is  as  follows. 
Following  severe  natural  disasters,  increasing  societal 
and political pressure, and global tensions driving energy 
markets away from climate objectives requires a sudden 
change in policy stance, with governments implementing 
stringent regulations and a dramatic tightening of carbon 
pricing policy. This major crystallisation of transition risk 
comes  alongside  a  sudden  and  severe  macroeconomic 
deterioration. 

11   Both  direct  and  indirect  holdings  of  equities  (through 
funds holding shares in other funds with direct exposure 
to  equities)  are  subject  to  shocks  in  the  illustrative 
modelling.  Holdings  of  assets  in  other  classes  such  as 

 
 
 
 
 
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Conceptual framework 

The impact of a price shock to underlying assets 
can be broken down into components, as follows. 

1.  Static effects. A shock to equity prices leads 
to  an  immediate  reduction  in  the  value  of 
funds  directly  holding  equities.  Additionally, 
there is an immediate reduction in the value 
of indirect equities holdings, e.g. where funds 
hold shares in other funds that hold equities.12  

2.  Dynamic effects. Once the value of funds is 
adjusted for the static effects, there may be 
additional  effects  due  to  the  actions  of 
economic  agents.  These  include  (i) inflows 
and  outflows  by  investors  in  response  to 
changes  in  their  financial  positions  and 
rebalancing, 
(ii) portfolio 
expectations; 
whereby fund managers buy or sell assets in 
line  with  their  mandate,  in  response  to  the 
changing financial conditions. Additionally, if 
these dynamic responses lead to large-scale 
buying or selling of certain assets, there may 
be  (iii) knock-on  (second-round)  price 
effects.  For  example,  if  there  is  initially  a 
large  fall  in  equities  prices  for  a  certain 

sector,  asset  managers  may  decide  to  ‘tilt’ 
their portfolios away from the sector. If many 
managers make the same trade, even if only 
a small value of each portfolio is liquidated, 
the selling pressure could push down prices 
of the equities even further. 

first,  as 

the  price  shock 

Modelling  these  effects  requires  them  to  be 
calculated in sequence. The static component is 
calculated 
takes 
immediate  effect.  Investor  flows  and  portfolio 
rebalancing, on the other hand, take place over a 
longer  time  horizon  –  whether  days,  weeks  or 
months. Subsequent second-round price effects 
are  sensitive  to  the  assumed  time  horizon,  as 
rapid  net  sales  or  purchases  of  assets  result  in 
crowded trades, leading to price spikes.  

In principle, this  modelling  process can then be 
iterated  within  a  given  time  period  (Chart 2),  as 
the second-round price changes can be used to 
update the value of portfolio assets, which results 
rebalancing 
in 
according  to  the  assumed  reaction  functions. 
These actions, in turn, then generate third-round 
price changes, and so on.13

flows  and 

investor 

further 

Chart   2 
Overview of ESMA modelling framework 
Sequencing and potential iteration of static and dynamic effects 

Note: Schematic representation of sequencing of modelling stages (numbered boxes). Potential for iteration via knock-on price impacts represented via orange dashed 
lines; such price impacts are not modelled in the present analysis. 
Source: ESMA. 

A  related  modelling  question  is  the  extent  to 
which  any  knock-on  price  impacts  in  one  year 

would  persist  in  subsequent  years.  There  are 
arguments for either approach, as follows.  

bonds,  cash  and  derivatives  (even  those  referencing 
equities) are excluded from the analysis. 

12   Indirect  static  effects  are  calculated  via  iteration,  as 
follows. First, define S1 to be the set of funds that hold 
direct equities only, for which direct static effects are first 
calculated. Then define S2 to be the set of funds that hold 
shares  in  funds  in  S1  and  otherwise  only  directly  hold 
equities. Given the updated valuation of funds in S1, static 
effects in S2 can be calculated, and so on at higher levels. 
Assuming the population of funds is regular as in Gourdel 

and Sydow (2022) – i.e. there is no subset containing only 
funds that are fully owned by each other – then it can be 
partitioned in this way and static effects calculated for all 
funds.  

13   The  precise  conditions  under  which 

this  process 
converges to a stable set of fund portfolios is left to further 
research.  Informally,  however,  convergence  happens  if 
given price impacts lead to smaller investor and manager 
reactions  that  in  turn  yield  smaller  next  round  price 
impacts. 

 
 
 
 
 
 
 
 
 
 
ESMA TRV Risk Analysis 

19 December 2023 

8 

1.  Changes  in  demand  for  assets  may  be 
temporary if flows and rebalancing are a one-
off  phenomenon. 
if  price 
impacts are largely attributable to a burst of 
trading activity resulting in ‘crowded trades’, 
their  effect  will  be  largely  transient.  If  so, 
knock-on price effects will only apply within-
period, not in subsequent periods. 

In  particular, 

2.  To  the  extent  that  the  buying  and  selling  of 
assets is due to lasting shifts in demand from 
fund  investors  and  managers,  the  knock-on 
price impacts may be reflected in equilibrium 
market prices in the longer term. 

In principle, it would be possible to model some 
combination of the two – whereby some but not 
all  knock-on  price  impacts  persist  into  future 
years.  For  simplicity,  however,  we  exclude 
modelling  of  knock-on  price  impacts  from  the 
present analysis. As ESMA work continues in this 
area, future modelling may seek to capture such 
impacts.  The  section  below  on  calibration 
discusses the possibility further. 

Data used 

In addition to the simulated equity price shocks, 
we  use  an  extensive  dataset  of  fund  portfolio 
holdings obtained from Morningstar and enriched 
with further information from Refinitiv Eikon. The 
dataset  represents  a  portfolio  snapshot  as  of 
June 2023 and covers around 19 000 investment 
funds  from  the  EEA  with  EUR  10 trillion  assets 
under management. Around 16 000 of the funds 
in  the  sample  are  UCITS,  700  disclose  under 
Article 9  of  the  Sustainable  Finance  Disclosure 
Regulation  (SDFR)  and  1 400  disclose  under 
SFDR Article 8.14 Equities form the largest asset 
class  (Chart 3).  Around  34%  of  equities  are 
issued  by  companies  from  the  US,  followed  by 
France  with  7%,  the  UK  with  6%,  and  China, 
Germany, and Japan with around 5% each. 

In the illustrative exercise, we consider all funds 
in  the  dataset,  but  only  equity  holdings  are 
subject to  a price shock. Results are presented 
for these equity holdings, taken together. 

The  raw  portfolio  data  have  been  cleaned  by 
removing  duplicates  and  funds  with  reported 
position values older than 2022.15 To account for 
outliers,  e.g.  currency  conversion  or  fat-finger 
errors,  the  largest  and  smallest  0.5%  of  fund 
portfolio positions are removed. 

The dataset  used does not distinguish between 
active  and  passive  funds.  Especially  for  the 
modelling  component  on  portfolio  rebalancing, 
updating the dataset to enable such a distinction 
would  be  a  useful  extension  of  the  work. 16 
Nonetheless, as active funds generally represent 
around 85% of UCITS AuM, simply assuming that 
all  funds  follow  the  same  dynamic  process  is  a 
helpful way to illustrate how the model works.17 

Chart   3 
Distribution of fund portfolio holdings by asset class 
Equities are the most-held asset class 

Note: Morningstar portfolio holdings dataset, distribution of asset classes.  
Sources: Morningstar, ESMA. 

Assumptions 
Certain assumptions can be made to simplify the 
modelling. Key assumptions made in the present 
analysis include the following. 

–  Assumption 1. Changes in fund valuations 
come solely from changes in the price of fund 
asset 
assets 

simulated 

from 

and 

14   SFDR sets out how financial market participants have to 
disclose sustainability information. SFDR Article 8 funds 
promote  environmental  or  social  characteristics,  while 
SDFR  Article  9  funds  have  sustainable  investment  as 
their objective. 

15   Short positions have also been removed. Short positions 
in equities represent less than 1% of the total net value of 
equity holdings in the dataset. 

16   A  distinction  relevant  to  modelling  investor  flows  is 
whether each AIF is closed-ended or open-ended (UCITS 
are open-ended. The dataset used in the present exercise 
does not contain this information. 

17   According  to  Refinitiv  Lipper  data,  as  of  2Q23,  86%  of 
AuM among non-ETF equity or bond UCITS was in active 
funds. If ETFs are included the overall population, around 
68% of total AuM is in non-exchanged traded fund (ETF) 
active funds. Future development of the model presented 
in the current article could consider calibrating fund flow 
elasticities  conditioning  on  active  versus  passive  fund 
status.  The  rebalancing  component  could  potentially 
include  some  rebalancing  for  passive  funds  if  index 
composition is assumed to be updated intra-year. 

42%17%11%10%9%11%EquitiesCorporate bondsGovt. & supranational debtCash & cash equivalentsFunds Other 
 
 
 
 
 
 
 
ESMA TRV Risk Analysis 

19 December 2023 

9 

sales/purchases.  Neither 
fund  dividend 
distribution  policy  nor  investor  reinvestment 
are modelled.  

–  Assumption  2. 

Investor 

inflows  are 
proportional  to  positive  fund  performance 
(see calibration section below). Outflows are 
proportional  to  negative  performance  up  to 
the point that a fund’s net assets are zero, in 
which case its positions are thereafter set to 
zero. 
financially 
unconstrained.  Flows  are  frictionless  and 
independent across funds. 

investors  are 

Implicitly, 

–  Assumption  3.  Liquidity  shocks  are  not 
modelled; there is always sufficient demand 
to  meet  asset  sales  at  (post-shock)  market 
prices. 18.  Prices  are  not  adjusted  based  on 
the  dynamic  component  of  the  model,  as 
discussed in the previous section. 

Calibration  

Investor inflows and outflows  

As  investor  inflows  are  proportional  to  positive 
fund performance while outflows are proportional 
to  negative  performance  (Assumption  2),  flows 
are  a  piecewise  linear  function  of  returns.  The 
strength  of 
is 
conditioned on whether a fund is categorised as 
ESG or non-ESG, using the coefficients found by 
Renneboog  et  al.  (2011)  and  summarised  in 
Table 1.19  

inflow/outflow  responses 

the 

Table   1 
Calibration of flow-return elasticities 
Negative performance leads to outflows 

Flow-return elasticities 

Positive return 

Negative return 

ESG funds 

Non-ESG funds 

1.014 

1.014 

0.121 

0.285 

Note: Flow-return elasticity defined as flows as a % of the fund’s value at the start 
of a time period given % performance during that time period. Positive coefficients 
therefore imply inflows in the case of positive performance and outflows in the case 
of negative performance (Renneboog et al. (2011). 

18   This  is a major simplification and limitation, as in reality 

certain assets may risk being stranded. 

19   The  coefficients  are  for  all  types  of  fund.  If  estimating 
coefficients directly  as  part  of a  future  calibration of  the 
model, it would be  useful to  condition also on the asset 
type of the fund (e.g. equity funds vs bond funds). 

20   Another  consideration  when  modelling  investor  flows  in 
response to a shock is that funds could in fact use their 
cash buffer (around 10% of fund holdings by value in the 
dataset, as shown in Chart 3) rather than liquidate other 
assets.  However, it seems likely that  while cash  buffers 
would  be  used  to  meet redemptions up  front,  in a  one-

In the present scenario analysis, the returns are 
obtained by comparing the pre-shock fund value 
to  its  post  shock  value.  If  for  instance  a  fund’s 
value  falls  by  EUR 10mn,  there  are  outflows  of 
fund),  or 
EUR 1.21mn 
EUR 2.85mn (if it is non-ESG). If the fund  gains 
in  value  by  EUR 10mn,  in  contrast,  there  are 
inflows of EUR 10.14mn.  

is  an  ESG 

(if 

it 

One limitation of the fund flows calibration is that 
it assumes linearity in the flow-return relationship. 
In the extreme market conditions of the adverse 
scenario, however, it is possible that the linearity 
could  break  down,  e.g.  if  investors  regard  the 
market as undergoing a paradigm shift.20 

Portfolio rebalancing 

Following the shock to asset prices and resulting 
net investor flows via the elasticities in Table 1, a 
fund’s manager then acts in two steps, as follows. 

–  Divestment and new asset purchases. The 
manager  divests  entirely  from  the  20%  of 
worst-performing assets (i.e. equities, in the 
simple  case  we  are  considering)  in  the 
portfolio. The proceeds are used to purchase 
the  20%  best-
new  assets,  which  are 
performing  equities  among  those  held  by  a 
‘peer group’ of funds.21  

– 

Intra-portfolio  rebalancing:  The  manager 
then  reallocates  capital  within  the  resulting 
set of assets within the portfolio (i.e. including 
newly  purchased  assets  and  excluding 
assets from which the fund has divested). For 
fund 𝑖, the total proportion 𝑃𝑖 of its value 𝑉𝑖 to 
be reallocated equals the sum of (i) 1% of the 
fall in the value of the fund based on the static 
effects, and (ii) 25% of the absolute value of 
the net investor flows. For the large shocks to 
asset prices in the illustrative scenario, which 
are  typically  falls  of  over  50%,  𝑃𝑖  is  a  little 
under  1%,  and  slightly  larger  for  non-ESG 
funds  than  ESG  funds.  The  amount 𝑃𝑖𝑉𝑖  is 
then redistributed among assets in proportion 
to  their  relative  performance. 22  Textbox  2 
gives an example. 

two  steps  modelled 

the  portfolio 
The 
rebalancing  stage  are  motivated  by  different 

in 

year  period  that  serves  as  the  basis  of  the  present 
analysis, funds might be expected to replenish their cash 
buffers back to their pre-shock levels. 

21   This ‘peer group’ is all funds within the same Morningstar 
Global Category (e.g. Energy Sector Equity, or US Equity 
Mid-Cap) and with the same Morningstar ESG rating. 

22   Formally,  suppose 

following 
divestment and new purchases, with values 𝑤𝑘 > 0 such 
𝑁
that  ∑ 𝑤𝑘 = 𝑉𝑖
the  simple  average 
𝑘

fund  𝑖  has  𝑁  assets, 

.  Denoting 

 
 
 
 
 
 
 
 
 
ESMA TRV Risk Analysis 

19 December 2023 

10 

considerations.  Step  (1)  –  selling  20%  of  fund 
assets and spending the proceeds on new assets 
–  involves  an  extensive  and  sudden  change  in 
fund’s portfolio composition. The 20% figure used 
in this part of the calibration is intended to reflect 
the  nature  of  the  adverse  scenario,  where 
financial market participants have made a major 
and  sudden  reassessment  of  asset  valuations, 
leading  to  an  extreme  and  abrupt  fall  in  equity 
valuations.  Setting  the  percentage  of  divested 
assets to 20% within each fund is intended to be 
keeping with this narrative, though can readily be 
varied in future applications of the model.  

However, the lack of a clear precedent that could 
be used to estimate the parameter empirically is 
an important limitation in the modelling approach.  

Textbox  2 
Example of intra-portfolio rebalancing 

To  give  an  example  of  the  second  step:  if  the  static 
effects yield a 50% reduction in the value of an ESG 
fund  𝑖 ,  this  will  generate  outflows  of  0.121 × 50% =
6.05%  of  the  fund’s  pre-shock  value  𝑉𝑖 .  The  total 
amount of capital to be reallocated will then equal 𝑃𝑖𝑉𝑖, 
where: 

disproportionately  buying  stocks  that  recently 
outperformed 
that 
underperformed.  Over  a  year  around  0.5%  of 
total  fund  value  was  reallocated,  broadly  in  line 
with our calibration.23  

selling 

those 

and 

Knock-on price effects 

As discussed above, the current model does not 
calculate price effects arising from investor flows 
or portfolio rebalancing. However, sizeable price 
effects could be expected in  reality given large-
scale market orders from an EU fund sector with 
ex-ante assets in excess of EUR 10tn.24  

Price effect calculations could be calibrated at a 
general level by, for instance, using the Amihud 
illiquidity  ratio  measure  for  equities  specified  in 
EBA  (2013).  Alternatively,  estimated  Amihud 
illiquidity  measures  on  an  asset-by-asset  basis 
from  different  sources. 
may  be  available 
Regardless  of 
for 
fundamental 
remains 
calibration, 
question discussed above as to what extent the 
price effects are permanent. 

the  data  source  used 

there 

the 

𝑃𝑖 = (1% × 50%) + (25% × 6.05%) 

Illustrative results 

= 0.61% 

For  a  non-ESG  fund 𝑗 also  suffering  a  50%  drop  in 
value from static effects, outflows specified by Table 1 
would be higher, leading to 𝑃𝑗 = 0.86%.  

(2)  of 

rebalancing  – 
the  portfolio 
Step 
adjustments within the portfolio that results from 
step  (1)  –  is  somewhat  similar  to  momentum-
following  strategies  popular  among 
fund 
managers during more normal market conditions. 
Grinblatt,  Titman  and  Werners (1995)  find  that 
over  three-quarters  of  fund  managers  carry  out 
investing, 
some 

form  of  momentum-based 

performance due to the price shock as 𝑟, the change in 
the value invested in asset 𝑘, ∆𝑤𝑘, is: 
(𝑟𝑘 − 𝑟)
𝑠𝑡. 𝑑𝑒𝑣(𝑟)

∆𝑤𝑘 =

𝑃𝑖
𝑁

×

𝑘

𝑁
𝑘

 is proportional to (∑ 𝑟𝑘)𝑁

Note that ∑ ∆𝑤𝑘
− 𝑛𝑟 = 0, i.e. 
the  changes  in  weights  across  assets  sum  to  zero, 
consistent with the notion of within-portfolio rebalancing. 
23   On  average,  the  money  reallocated  to  each  stock 
multiplied by its excess returns was 0.74% of fund value 
per quarter, or around 3% annualised. Stocks bought had 
average  returns  around  6%  above  those  sold,  implying 
around 0.5% of fund value was reallocated.  

24   Sydow  et  al  (2021)  show  that  interactions  between  the 
fund  and  banking  sectors  may  be  expected  to  amplify 
such  price  effects.  By  way  of  comparison,  Mirza  et  al 
(2020) simulate a ‘fire sale’ of assets held by banks and 
funds.  Based  on  a  dataset  covering  equity  holdings  of 
around  EUR 5 tn,  the  authors  estimate  knock-on  price 
effects  of  the  order  of  1%.  In  contrast,  the  present 

The model is applied to the dataset from 2023 to 
2027,  with  annual  frequency.  The  total  value  of 
equity  holdings  declines  up  to  70%  under  the 
adverse scenario versus the baseline (Chart 4).25  

In the model, investor flows amplify the shock in 
the  adverse  scenario,  while  portfolio  balancing 
has  no  immediate  effect. 26 This  is  because  the 
scenario  has  a  single,  initial  shock,  whereas 
rebalancing  only  affects  subsequent 
fund 
performance, after the shock has hit, which is a 
general feature of the modelling approach. 

scenario analysis does not consider a fire sale, as funds 
do not face liquidity constraints under Assumption 3. As 
rebalancing is assumed to take place in an orderly fashion 
over  several  months  (i.e.  within  the  yearly  modelling 
frequency), the price impact may be more moderate. On 
the other hand, the far larger total value of equities held 
by the fund sector and  the scale of the rebalancing are 
likely to push price effects higher. 

25   The magnitude of this decline differs significantly from the 
estimate  of  a  fall  in  AuM  of  -6%  in  Crisóstomo  (2022), 
mainly due to the inclusion of macroeconomic stress, the 
focus on equity holdings and the dynamic fund adjustment 
mechanism. 

26   The  result  that  investor  outflows  exacerbate  the  shock 
(i.e.  the  direction  of  the  effect)  follows  from  the  shock 
specification and the flows calibration. More generally, if 
the  calibration  has  non-zero  coefficients  with  the  same 
signs  as  in  Table  1, and  if  in  a given period  a  scenario 
leads  on  average  to  negative  returns  among  non-ESG 
funds as well as among ESG funds, there will be negative 
net outflows in that period.  

 
 
 
 
 
 
 
 
ESMA TRV Risk Analysis 

19 December 2023 

11 

Chart   4 
Main results from scenario modelling 
Dynamic  impacts  exacerbate  declines  in  
value of funds’ equity holdings 

fare  significantly  worse  under 
scenario. 

the  adverse 

Chart   6 
Largest relative falls in equity holdings values by sector 
Very large falls for high-emitting sectors 

Note:  Simulated  proportional  change  in  total  value  of  equity  holdings  among  a 
sample  of EEA-domiciled investment funds, based on  main adverse scenario in 
ECB  and  ESRB  (2023).  Static-only  calculations  plotted  alongside  cumulative 
effects  of  static  and  dynamic  components.  In  the  scenario  taken  as  an  input, 
around  four-fifths  of  the  static  impact  on  equity  prices  in  the  first  year  is  from 
adverse macroeconomic conditions, with the remainder attributable to materialised 
transition risk. 
Sources: ESRB, NGFS, Morningstar, Refiniv Eikon, ESMA. 

The  results  can  be  broken  down  by  economic 
sector (Chart 5). Equities from only three sectors 
(financial  & 
insurance;  manufacturing;  and 
information  &  communication)  account  for  over 
80% of the decline in value. However, the result 
reflects  those  sectors’  dominance  in  equity 
markets and thus fund holdings. 

Chart   5 
Sectoral breakdown of total decline in AuM 
Aggregate  fall  in  value  driven  by  financial 
and manufacturing sectors 

Note:  Average  percentage  changes  in  value  of  equity  holdings  losses  by  the 
general industrial classification of economic activities within the European Union 
(NACE)  level  1  sector  under  the  adverse  scenario.  "Waste  man."  =  Waste 
management.  "Admin."  =  Administrative.  "Insur."  =  Insurance.  "Manuf."  = 
Manufacturing.   The letters in brackets after sector names are classification codes. 
Sources: ESRB, NGFS,  Morningstar, Refinitiv Eikon, ESMA. 

The contributions of the dynamic components of 
the  model  to  the  results  can  be  compared 
(Charts 7, 8).  

Chart   7 
Impact of investor flows on fund assets 
Most investor outflows from 7-18% 

Note: Distribution of asset market value in %  (y-axis)  by proportional change  in 
initial  holding  due  to  investor  flows  in  the  model  (x-axis;  bracket  width  5  basis 
points. 
Sources: ESRB, NGFS,  Morningstar, Refinitiv Eikon, ESMA. 

The  model  calibration  yields  larger  changes  in 
funds’  asset  positions  due 
rebalancing 
(Chart 8) than fund flows (Chart 7). Outflows due 
to  investor  redemptions  mostly  range  up  to 
around  18%,  whereas  most  positions  are 
rebalanced to a greater extent. 

to 

Note:  Share  of  total  fall  in  total  assets  under  management  for  sample  of  EEA-
domiciled  investment  funds  by  the  general  industrial  classification  of  economic 
activities  within  the  European  Union  (NACE)  level  1  sector  under  the  adverse 
scenario. The letters in brackets after sector names are classification codes. 
Sources: ESRB, NGFS, Morningstar, Refiniv Eikon, ESMA. 

the  most 
When  analysing 
adversely 
impacted  sectors  (Chart 6),  other 
economic activities such as mining and quarrying 

losses  among 

-100%-80%-60%-40%-20%0%Mining &quarrying(B)Water &waste man.(E)Agriculture,forestry &fishing (A)Admin. &supportservices (N)Financial &insur. (K)Electricity,gas &steam (D)Manuf. (C) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ESMA TRV Risk Analysis 

19 December 2023 

12 

Chart   8 
Net rebalancing by asset price shock 
Extensive rebalancing in the model 

Note: Distribution of asset market value in % (y-axis)  by proportional change  in 
initial  holding  due  to  investor  flows  in  the  model  (x-axis;  bracket  width  5  basis 
points). 
Sources: ESRB, NGFS,  Morningstar, Refinitiv Eikon, ESMA. 

insight 

into  how 

fund  managers 
To  gain 
collectively  respond 
to  asset  price  shocks, 
Chart 9  plots  the  density  of  assets  by  total  net 
buying/selling by managers (y-axis) and by price 
shock in the adverse scenario (x-axis). 

Chart   9 
Net rebalancing by asset price shock 
Rebalancing correlates with price shocks 

Note:  Density  of  asset  value  as  %  of  total  value  of  all  assets,  by  level  of  net 
purchases  in  rebalancing  phase  as  a  proportion  of  initial  value  held  (y-axis; 
truncated at -0.99 and +0.99) and initial price shock in adverse scenario (x-axis). 
Darker colours indicate higher density.  
Sources: ESRB, NGFS, Morningstar, Refinitiv Eikon, ESMA. 

As expected, there appears to be some positive 
relationship  between  these  two  variables,  with 
total  net  purchases  by  managers  only  among 
those assets with a higher performance ranking.  

From a modelling perspective, a limitation of the 
fund-level approach taken is that prices are fixed 
throughout the  rebalancing process. 27 In reality, 
however,  widespread  divestment  of  an  asset 
would  generate  an  additional  discount  in  its 
market price, creating arbitrage opportunities. An 

27   In  some  cases,  all  fund  positions  in  one  asset  are 
liquidated  while  managers  collectively  make  large  net 
purchases  in  other  assets  with  similar  performance. To 
avoid  this  unrealistic  outcome,  the  model  could  in 
principle  be  extended  by  making  prices  endogenous  to 

informal check of the model’s robustness in this 
regard is whether assets with similar price shocks 
are rebalanced by a similar amount. The results 
are  encouraging,  as  much  of  the  mass  of  the 
distribution follows a narrow range (darker cells in 
Chart 9).  Nonetheless,  for  a  given  price  shock, 
rebalancing outcomes often vary significantly. 

Conclusion 
Identifying  vulnerabilities  in  the  investment  fund 
sector  under  climate  stress  scenarios  is  vitally 
important given its systemic place in the financial 
system and its crucial role in financing the green 
transition. In line with ESMA’s recent mandates in 
this regard, this article outlines a first approach to 
modelling the impact of asset price shocks. As set 
out in the first section, various climate scenarios 
can be used to generate shocks to asset prices, 
based  on  modelling  work  carried  out  by  other 
authorities and international groups. 

ESMA’s  first  approach  to  dynamic  modelling  of 
investor sector impacts takes a set of asset price 
shocks  as  the  core  input.  For  illustration,  the 
model  is  applied  to  a  severe  but  plausible 
scenario  involving  a  large  equity  price  shock 
driven  primarily  by  adverse  macroeconomic 
conditions  but  also  from  the  materialisation  of 
climate transition risk.  

The  dynamic  component  of  the  model  gives 
insight  into  the  expected  directional  effects  of 
reactions  by  investors  and  managers,  though 
involves  some  strong  simplifying  assumptions, 
including abstracting away from liquidity effects. 
Further  ESMA  work  on  this  topic  may  involve 
refining 
the  model  and 
incorporating second-round price impacts.  

the  calibration  of 

The  analysis  focuses  on  understanding  how 
dynamic effects  can amplify or mitigate climate-
related  vulnerabilities  in  the  fund  sector.  It 
suggests that the investment fund sector may be 
less  resilient  to  climate-related  shocks  than  a 
simple  static  analysis  would  suggest,  due  to 
outflows  by  investors.  Portfolio  rebalancing,  on 
the  other  hand,  is  unlikely  to  make  a  large 
difference  to  acute,  near-term  vulnerabilities. 
Overall, 
therefore,  dynamic  effects  may 
exacerbate short-term falls in asset values due to 
climate-related  risks  (such  as  a  disorderly 
transition), which may in turn impede the sector’s 
capacity to finance the green transition.  

the rebalancing process and making managers base their 
trading decisions on these prices. This extension would 
be an ambitious undertaking, however. 

 
 
 
 
 
 
 
 
 
 
 
 
 
ESMA TRV Risk Analysis 

19 December 2023 

13 

Related reading 
Amzallag,  A.  (2021),  ‘Fund  portfolio  networks:  a  climate  risk  perspective’,  ESMA  Report  on  Trends, 
Risks  and  Vulnerabilities,  No 1,  https://www.esma.europa.eu/sites/default/files/trv_2021_1-
fund_portfolio_networks_a_climate_risk_perspective.pdf. 

Bank of International Settlements (BIS) (2021), Climate-related Risk Drivers and Their Transmission 

Channels, https://www.bis.org/bcbs/publ/d517.pdf. 

Crisóstomo, 2022, “Measuring Transition Risk in Investment Funds”, Comisión Nacional del Mercado 

de Valores (CNMV) working paper no. 81. 

European Banking Authority (EBA) (2013), ‘Report on appropriate uniform definitions of extremely high 
quality liquid assets (extremely HQLA) and high quality liquid assets (HQLA) and on operational 
requirements for liquid assets under Article 509(3) and (5) CRR’, 
https://www.eba.europa.eu/sites/default/documents/files/documents/10180/16145/cebf6837-
2d13-43a7-8528-
55647f1b20bb/EBA%20BS%202013%20413%20Report%20on%20definition%20of%20HQLA.p
df. 

European 

Banking 

Authority 

(2023), 
https://www.eba.europa.eu/sites/default/documents/files/document_library/Risk%20Analysis%2
0and%20Data/EU-wide%20Stress%20Testing/2023/Results/1061374/2023-EU-wide-stress-
test-Results.pdfECB and ESRB, 2021, “Climate-related risk and financial stability”. 

‘EU-wide 

(EBA) 

stress 

test: 

results’, 

European Central Bank (ECB) and European Systemic Risk Board (ESRB) (2021), ‘Climate-related 

risk and financial stability’, 
https://www.ecb.europa.eu/pub/pdf/other/ecb.climateriskfinancialstability202107~87822fae81.en
.pdf. 

European Central Bank (ECB) and European Systemic Risk Board (ESRB) (2022), ‘The 

macroprudential challenge of climate change’, 
https://www.esrb.europa.eu/pub/pdf/reports/esrb.ecb.climate_report202207~622b791878.en.pdf 

European Central Bank (ECB) and European Systemic Risk Board (ESRB) (2023), ‘Toward 

macroprudential frameworks for managing climate risk’. 
https://www.esrb.europa.eu/pub/pdf/reports/esrb.report202312~d7881028b8.en.pdf?bc89d982a
c0f87466507bbe82686f64a  

European Commission (2021), Communication from the Commission to the European Parliament, the 

Council, the European Economic and Social Committee and the Committee of the Regions – 
Strategy for financing the transition to a sustainable economy, COM(2021) 390, 6 July, 
https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52021DC0390. 

European Commission (2023), ‘Request for a one-off scenario analysis exercise to be conducted 

jointly by the European supervisory authorities, the ECB and the ESRB in accordance with the 
Communication from the Commission of 6 July 2021 “Strategy for Financing the Transition to a 
Sustainable Economy”’, Ares(2023)1699255, 8 March, 
https://www.esma.europa.eu/sites/default/files/library/Mandate_for_the_FF55_one-
off_exercise.pdf. 

European Fund and Asset Management Association (2023), Fact Book 2023: Trends in European 
investment funds, 21st edition, https://efama.vcpgraphics.online/efama-fact-book-2023. 

European Securities and Markets Authority (ESMA) (2023), ‘Costs and performance of EU retail 

investment products 2023’, ESMA50-165-2357, 
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