Presentation of Reacfin’s Economic Scenario Generator (ESG) online demo

Reacfin Online ESG solution and user guide
© Reacfin, Place de l’Université 25, 1348 Louvain-La-Neuve, Belgium - M: info@reacfin.com – T: +32 (0)10 84 07 50
© Reacfin (2016)
Place de l’Université 25, 1348 Louvain-La-Neuve, Belgium
M: info@reacfin.com – T: +32 (0)10 84 07 50
Tailored Economic Scenario Generators (“ESG”) solutions
Presentation
Please read the important disclaimer at the end of this presentation

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Table of content
SECTIONS
CONTENT
 What is an ESG
 Reacfin’s tailored solutions
 High-level model architecture
 Integration in full balance-sheet projection
frameworks
 Model granularity
• Reacfin’s ESG solutions
• On-line demo
• About Reacfin

What is an ESG ?
• An Economic Scenario Generator (“ESG”) is a model used to projects the value of economic parameters (e.g., stock returns,
interest rates, corporate bond spreads, property values) into the future through Monte-Carlo simulation techniques
• It creates numerous possible scenarios for the evolution of such macro economic and market variables. The output of an
ESG are thus time-series of variables for different possible evolutions of world (“scenario’s”)
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• Risk Drivers are the fundamental parameters (the building blocks) which determine the risk & performance profile of a
specific asset or liability
• E.g. in the market risk model a mapping is needed to picture the evolution of the asset prices through the evolution of a
limited number of risk drivers.
ESG’s simulate risk drivers, not prices, values or balance-sheets items
Equity Price Indices
Credit Spreads
Yield Curve
Inflation
Corporate Bonds
Shares
Inflation Linked Bond
Market Instruments Risk drivers
Illustration

• The solutions proposed by Reacfin consists in both Real-World and Risk Neutral ESG solution available on- &
off-line that are tailored to the specific requirements of our clients. We further offer our clients full access to our
ESG Code.
• The solution proposed by Reacfin is not a software but rather a methodology supported by a set of tools
(preprogrammed elements in R, Matlab or VBA and including some embedded sub-routines in C++ to ensure
adequate calculation speed) . These are then assembled & tailored along the specific requirements of client to
ensure the delivered results exactly fit our client purpose.
• Through a very user-friendly graphical interface (e.g. browser-based*), our ESG solutions can be operated and
calibrated by users having no particular programming skills nor advanced knowledge in stochastic finance or
calibration of financial ESG’s
• Finally, at the single click of a button, all results can be obtained in various format (e.g. as CSV files) allowing
further use in most other systems (e.g. Excel, R, Matlab, SAS, etc.)
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Reacfin’s tailored open-source ESG solutions
We propose tailored ESG
solutions thoroughly owned
by our client’s staff, which
can periodically be remotely
calibrated by our personal
and further easily operated &
interpreted by a large range
of our client’s staff.
(*) Compatible with most standard internet browsers incl. MS-Internet Explorer, Apple’s Safari, Google Chrome, Firefox, MS-Edge, Opera, Wyzo, etc.

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Typical high-level Model Architecture
Input assessmentConfiguration Files
Calibration parameters
Inputs Downloadable Output
CSV file
CSV files containing:
• Historical time series
• Modeling assumptions
• A –priori calibration
assumptions
ESG Calibration Tool
ESG Scenarios CSV file
Graphical interface to
check sanity of inputs &
selection models
assumptions
Automated calibration
engine allowing also
manual expert judgement
corrections
Single processes parameters
Expert corrections
Correlations
ESG Simulation Tool
Projects Risk Drivers over
user-defined time
horizons and frequencies
Results assessment &
Testing tools
Visualizing simulation results and performing
adequacy tests
ESG Tool

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Designed to be integrated in full balance-sheet projection frameworks
ESG Tool
ESG Scenarios
Instrument Tool
Instrument Scenarios
Balance Sheet Tool
Balance Sheet Scenarios
Calibration and simulation of risk drivers (e.g. interest
rates, credit spreads)
Projection of individual instruments (e.g. FV, duration, SCR
consumptions, RWA, IFRS impairments, defaults & recovery
values…) based on ESG Scenarios & assets characteristics
Projection of balance sheet (FV, Incomes, duration,
Available & Required Capital, …) considering reinvestment
rules, business plan and allocations to the individual
instrument scenarios.
Limits set on Solvency Ratios, Liquidity requirements, P&L
volatility, Leverage Ratio , etc.
Results Analysis Tool
Production of comprehensive analysis reports &
dashboards, optimization tools for SAA pruposes, etc.
Our ESG
solution is
further
designed to
integrate into
Reacfin’s set
of tools for
portfolio- and
balance-sheet
projections.

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Key dimensions of risk drivers modeling granularity
Fixed incomes
Split per type of
assets or liabilities*
Split per
geographies
Split per maturity
bucket
Split per rating
Other
Split per type
(e.g. RE, Equities, HF, etc.)
Split per
geographies
Possible level of granularity currently foreseen
Split per Sectors
Granularity may further be increased considering bespoke developments
(*) e.g. Sovereign bonds, Quasi-sovereigns, Covered Bonds, Corporate Bonds (possibly slit per sectors), Structured credit instruments, Inflation linked instruments, etc.

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Intergation in Reacfin’s SAA suite of tools
Overall architecture
ESG & Total
Return
Optimization
Instrument
tool
Balance-Sheet
tool
Portfolio
selection tool
What it does • Generates scenario’s
for Risk drivers (Rates,
Spreads, Eq. & RE
returns, etc.)
• Performs initial total
return optimization (to
assess calibration &
identify some relevant
portfolio’s)
• Under each scenario’s
generates at all time
all possible single
instruments (for fair
value, Cash-flows,
durations, ratings,
etc.)
• Projects balance sheet
and calculates relevant
indicators (incl. FV,
Incomes, Duration,
Solv. Ratio’s, limits
breach, Liquidity
requirements, etc.)
Projection tools Optimization tools
• SAA optimization given
wide sets of optimization
criteria’s and constraints

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Table of content
SECTIONS
CONTENT
 Where to find Reacfin’s on-line ESG – Demo
 Defining the types of simulations
 Visualizing the results
• On-line demo
• About Reacfin

Where to find Reacfin’s on-line ESG - Demo
Simplified version available for demo purposes on http://apps.reacfin.com/ESG/

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Defining types of simulations
ESG modeling principles
• Interest rates
o Mean reverting stochastic processes
o Stochastic models per maturities allowing negative rates (through displacement factors)
o To be calibrated: Initial investable rate levels, LT mean reversion levels, mean reversion speed, volatilities,
displacement factors*
• Credit Spreads (for different asset classes**)
o Mean reverting stochastic processes with transition probabilities
o Stochastic models per ‘full letter’ rating (for bonds***) allowing negative spreads (through displacement factors)
o Deterministic term structure which can be made specific for each asset class
o To be calibrated: Initial investable spreads levels, LT mean reversion levels, mean reversion speed, volatilities,
probabilities of rating transitions (transition matrices) , displacement factors*
(*) Calibrated as twice worst historical observed
(**) See slides dedicated to ESG granularity
(***) For loans: performing or defaulted
• Non-Fixed Incomes
o Equities, Real Estate & Alternative Investments
o Non-reverting processes with constant incomes yields (e.g. dividend yields or rental incomes)
o To be calibrated: Long term average total return, volatility, incomes yield
• Dependencies structure
o Constant Correlation matrices
o Possibility to foresee dimension reduction using PCA approach

Selecting stochastic models
Select the first tab ‘Input”
Our on-line demo version is limited to 5
main asset classes and each of them
may be modelled either using 3 types of
processes:
• Geometric Brownian Motions (GBM)
• Displaced Black-Karasinski (BKD)
• Displaced Cox-Ingersoll-Ross (DCIR)
Our professional tailored versions will
allow you to model a large number of
additional risk drivers using wide range
of stochastic models
In first tab “Input”

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Illustrative example: Dispersion of the projections for 3 Months € interest rates
Calibration period: from 2000-01-01 to 2014-11-05
Black-Karasinski Cox-Ingersoll-Ross
Log-Normal distribution for the changes in rates
(proportional to the IR level)
Chi-Squared distribution for the changes in rates
(proportional to the square root of the IR level)
Percentiles:
• Geometric Brownian Motions (GBM): Constant trend and normally distributed random increments
• Displaced Black-Karasinski (BKD): Mean revering processes with log-normal distribution of random increments
• Displaced Cox-Ingersoll-Ross (DCIR): Mean revering processes with random increments having a Chi-square distribution
Selecting stochastic models
Defining types of simulations In first tab “Input”

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• Our online demo-model generates correlated risk drives for:
o Different maturities of interest rates
o Different rating buckets (Investment Grade only) of 5 years credit spreads
• Given the large number of risk drivers simulated, some unwanted behaviors (e.g. too frequent interest rates curve
inversions or a-typical inversions for spreads along ratings) can happen.
• To limit such issues, users may thus chose to reduce the number of random variables (factors) used for each risk drivers
(using principal components analysis techniques).
Selecting the number of stochastic factors
Under the « Select Models » menu

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• Per default all processes are calibrated historically (based on monthly data only for the online demo*)
• The longest possible data series ranges from Jan-2000 to October 2014**
• Users may select to calibrate their models on part of these data only by modifying the “Date Range” as illustrated
below.
Selecting the calibration period
(*) Calibration on daily (and in some cases even intra-day) data possible in the professional tailored versions
(**) Unlike our professional tailored versions, in the on-line demo user cannot modify the historical data series used for calibration
Under the « Select Factors » menu

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• At the bottom of the first tab ”Input”, users may
then define:
o the start date of their stochastic simulation
o The number of simulations to be performed
o The number of years over which simulations have to
be performed (the “Projection Horizon”)
o The frequency of simulated points of this projection
horizon (in the demo version*: annual or monthly)
• For instance, in the example illustrated on the
right, the model will thus project 1000 scenarios
each starting on April 14th 2016. Since we chose to
project annually over a 10y horizon we will thus get
1000 simulations for each of the following dates:
14-Apr.-2017, 14-Apr.-2018, … ,until 14-Apr.-2016
• To launch the calculations, click on the black button
“Calculate”.
• Then switch to tab “Outputs” to see the results.
Simulation options
(*) Unlike our professional tailored versions which will also offer different frequencies of projections (e.g. quarterly, weekly, daily, tailored)
• Large numbers of simulations (especially on monthly step sizes) may induce long computation times.

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Simulation options
Visualizing the results In second tab “Outputs”
Select the second tab
‘Outputs”
This grey tag indicates the
program is computing and
results are about to come
Select here the
asset class you
want to visualize
Select here the sub-
asset class
characteristics you
want to visualize
(e.g. maturity or
rating)
Tick this bow if you want
to visualize projection
results together with the
historical time series that
where used for calibration

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Simulation options
Select the second tab
‘Outputs”
This grey tag indicates the
program is computing and
results are about to come
Select here the
asset class you
want to visualize
Select here the sub-
asset class
characteristics you
want to visualize
(e.g. maturity or
rating)
Tick this bow if you want
to visualize projection
results together with the
historical time series that
where used for calibration

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Charts options
• All charts of our are produced using Ploty (https://plot.ly/feed/) and are thus interactive charts
• By passing your mouse over a chart, the following option icon menu will appear in the upper-right corner
Download chart as
a png image
Save chart on Ploty server
for editing
Zoom on selected part of the chart
Explore (“pan”) the chart when zoomed
Zoom in and zoom out at
the center of the chart
Reset axis
Auto scale

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Visualizing the results
Screenshots of the ESG
ESG set-up & definitions board Results assessment & calibration Scenario's & parameters exportation

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Table of content
SECTIONS
CONTENT
 Who we are
 Our driving values
 Reacfin’s management
 Reacfin’s 4 core fields of expertise
 What we deliver
 Examples of recent assignments
• On-line demo User Guide
• About Reacfin

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Who we are
Reacfin s.a. is a Belgian-based actuary, risk & portfolio
management consulting firm.
We develop innovative solutions and robust tools for
Risk and Portfolio management.
The company started its activities in 2004 as a spin-off of the University of
Louvain, focused on actuarial consultancy to Belgian insurers, pension
funds and mutual organizations. Rapidly, Reacfin expanded its business
internationally and broadened its scope to various aspects of quantitative
& qualitative risk management, financial modeling and strategic advice to
financial institutions.
Spread over its 3 offices in Louvain-La-Neuve, Antwerp and Luxembourg,
Reacfin employs about 30 consultants most of which hold PhD’s or highly
specialized university degrees.
What we do
• Modeling
• Risk implementation advisory
• Validation & model reviews
• Specialized strategic risk consulting

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Our driving values
Excellence: our outstanding feature
To deliver more than is expected from us, we attract the best people and develop their skills to the most
cutting-hedging techniques supported by a robust and rigorous knowledge management framework.
Innovation: our founding ambition
Leveraging on our profound academic roots, we are dedicated on creating inventive solutions by
combining our extensive professional experience with the latest scientific research.
Integrity: our commitment
We put work ethics, client's best interest and confidentiality as the foundation of our work. We are fully
independent and dedicated at telling the truth.
Solution-driven: our focus
We produce for our clients tangible long-term sustainable value. We help our clients not only to reach
the top, we help them reaching the stable top.
Reliability: our characteristic
We never compromise on the quality of our work, the respect of deadlines & budgets and our other
commitments. We don’t produce reports, we deliver results!
We put great emphasis at strictly articulating our work around 5 fundamental driving values:

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Reacfin’s management
Prof. Pierre Devolder (Chairman)
• Pr. Actuarial Science & Finance
• PhD in Sciences
• MSc. Actuarial Sciences
• MSc. Mathematics
• Non-Life Ex.Co. Member Axa Belgium
Xavier Maréchal
• MSc. Actuarial Sciences
• MSc. Civil Engineering
• MSc. Business Management
• Researcher in Actuarial Science and
author of several refence books
Dr. Maciej Sterzynski
• PhD Economics
• MSc. Economics & Finance
• MSc. Law
• Specialist of qualitative Risk Management
• Co-Author of the CRD and Solvency II directive
Francois Ducuroir
• MSc. Appl. Mathematics
• Msc. Applied Economics
• Structured Solutions Benelux at Barclays Capital
• CPM & Capital solutions at BNP Paribas Fortis
• Prof. Banks & Fin. Instit Mgmt

Reacfin’s 4 core fields of expertise: Our centers of excellence
ALM, Portfolio Management & Quantitative Finance
 Implementation and calibration of stochastic models for
valuation, trading and risk Management purposes
 Times series analysis & modelling
 Pricing of financial instruments & development of ALM models
 Design/review/implementation of systematic trading &
hedging strategies
 Business intelligence in ALM or Portfolio Management
 Tools development (Valuation, Pricing, hedging, portfolio
replication, etc.)
 Design of Capital Management solutions
Insurance specialities
Life, Health and Pension
 DFA* Models
 Capital Requirement assessment
 Business valuation support
 Product development (pricing, profitability,..) & Reserving
 Model validation
Non-Life
 Reserving: triangle methods, individual claims modelling
 Pricing: frequency and severity modelling, large claims
analysis, credibility methods, commercial constraints
 DFA models: cash-flows projection, calibration of models
 Reinsurance: modelling covers, optimal reinsurance
programs
Qualitative Risk Management, Restructuring & Operations
 Organization & Governance
 Businesses restructuring & change management
 Implementation and industrialization of processes
 Internal & regulatory reporting (KRI’s & KPI’s dashboards)
 Model Review frameworks
 Model Documentation
(*) DFA = Dynamic Financial Analysis
Risk & Portfolio Management
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What we deliver
Balanced and
pragmatic
approach
No black box
Solutions
Documentation,
coaching &
training
 Client-centric solutions focussed on deliverables
 Respecting the principle of proportionality
 Cost efficient within tight pre-agreed budgets
 We deliver results, not reports!
 Open source solutions
 Close cooperation with our clients
 Clear & comprehensive documentation compliant existing or upcoming regulation
 Adapted trainings at all levels of the organisation
 Coaching support for implementation and operationnalisation of processes
Clearly structured
processes
 Lean & efficient tailored project management
 Regular progress reviews
 Close cooperation with our clients
State of the art
technical skills
 Expertise in most advanced quantitative modelling & academic excellence of a spin-off
 All our consultants hold multiple masters or Phd.
 Best-in-class qualitative risk management leveraging on highly experienced senior consultants
 Hands-on implementation solutions, tested for real-world conditions
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Strategic Asset Allocation for Bank & Insurance group
• Strong ‘natural’ unbalance
between assets and liabilities
durations
• Limited excess capital available
• Regulatory, accounting and Risk
Management constraints
• Limited view on non-Govies asset
classes & peers practices
• European Retail Bank with
material insurance activities
• Aims at optimizing its asset
allocation for both banking and
insurance businesses
• Conservative risk profile materially
constrained by board defined Risk
Appetite
• Current allocation materially
concentrates the portfolio in local
Govies
Client Situation
• Robust target model portfolio’s for bank and insurance
along 4 key dimensions (Asset types, Maturities,
Ratings and Sectors)
• Proposal for rationale reinvestment rules depending
on prevailing market conditions
• Introduction of new asset classes
• Improved NII, expected Total Return and Sharpe Ratio
• Asset allocation tool in R and in Excel
Results
Reacfin’s Contribution
Issues
• Exhaustive mapping and categorization of investable
asset classes (using a benchmark based approach)
• Robust methodology for Risk, Returns & Correlations
calibration
• Peers benchmarking through tailored surveys
• Modeling of portfolio dynamics under constraints of
Capital consumption (both Basel II/III and Solvency II),
Liquidity requirements, Accounting volatility, etc.
• Optimization both in Value & for NII for businesses
independently and at bank-insurance group level
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Example of previous assignment: All dummy numbers &
graphs for illustrative
purposes only

Investment Portfolio Diagnostic
• Insurance Company operates under
significant restrictions from regulator
• Portfolio inherited illiquid distressed
positions.
• Liabilities expected to change significantly
in the coming years due to change in
corporate strategy.
• Large independent Insurance Company
going through significant business model
changes
• Portfolio Management, ALM and Risk
departments in reorganization process
• Client asked Reacfin to review current
portfolio for main drivers of risks & return,
identify opportunities & threats and
propose recommendations in term of
investment strategy, processes and
organisation.
Client Situation
• Benchmarking the portfolio for risk-return
& dedicated visualization tools
• Estimating the benefit of diversification, its
stability and its expected evolution.
• Recommendations for each asst classes
• Expected behavior of the portfolio under
crisis situations.
• Process & organizational improvements
Results
Issues
• Modeling portfolio using over 50 different
sub-asset classes both along market- &
portfolio managers-based assumptions
• Modeling the « natural » evolution of the
portfolio for short and mid term horizons
taking into account insurances net
in/outflows, expected maturities &
coupons, defaults, growth, etc.
• Scenario & sensitivity analysis
• Interpretation of results and
recommendations
29
purposes only

Strategic Asset Allocation Study for Pension Fund
Client Situation
Results
30
• Modeling of the asset classes using a
rough aggregated asset but sufficiently
in line with the risks and allowing for
stochastic behavior
• Projections of liability cash flows and
technical reserves
• Determination of decision criteria
• Client: Belgian Pension Fund
• Client asked Reacfin to analyze its
strategic asset portfolio allocation and
potentially suggest for modifications,
improvement.
• Specificities as rebalancements
constraints, reported loss, cash
constraints
• Obvious and easy to understand decision
criteria.
• Improved strategic assess allocation,
(confirmed or adjusted) i.e. in line with
risk tolerance of the management and
expected return
• Better knowledge of the current and
future potential risks depending on
economy evolution
• Aggregation, modeling and calibration of
assets.
• Generation of scenarios
• Projection of the different asset values
along each scenario
• Thanks to a pension specialized partner,
projections of the liabilities
• Definition of risk indicators taking into
account asset and liabilities projections
• Computation of these risk indicators
purposes only
Issues

Pricing of a complex OTC derivatives
Client Situation
Results
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• Understanding the complexity of the
product and all the embedded options
to identify the main underlying risk
drivers
• Some underlying's of the derivative are
themselves derivative products
• Several options to look at the product
and to model it
• Client: tier-one European financial
institution conglomerate having large
OTC derivative positions on its balance
sheet
• Client asked Reacfin to set up a stable
process to quarterly fairly price this
derivative depending on its main risk
drivers.
• The Value at Risk of the price should also
be assessed.
• Recurrent valuation of the product and
its Value at Risk
• Better assessment of the valuation and
the risk related to the product
• Balance sheet compliant valuation
• Implementation of a market consistent
valuation of the product using a
quantitative model including risk neutral
economic scenarios generator
(calibration and generation) of several
market variables and a pricing tool.
• Documentation of the methodology
• Recurrent pricing reports (including
Value-at-Risk)
• Sensitivity analysis
purposes only
Issues

Development of Heston model for Equity in a Risk Neutral framework
Client Situation
Results
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• Reproduction of initial options market
prices is inaccurate
• In classical GBM models, implied
volatility is assumed constant, though it
is not in practice
• Current calibration and simulation
runtime is too long
• Client: International insurance company
• Client asked Reacfin to improve the risk
neutral scenarios generated
• Reproduction of initial market prices is
key in risk neutral framework
purposes only
Issues
• Determination of the model
requirements.
• Calibration of the model on instruments
market prices
• Implementation of martingale tests
• Integration in a whole economic scenario
generators
• Introduction of dependence structure
with the other risk factors
• Consequently improved reproduction of
initial market price
• Generation of a stochastic volatility
• Consequent reduction of runtime

ALM Model development & implementation
Client Situation
Results & Benefits
Reacfin Contribution
Issues
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• European insurance group
• Internal needs to produce appropriate risks and
value metrics to support managerial decisions
• Internal requirements to manage the business in
line with appropriate ALM indicators
• Pressure from regulators to improve risks and
capital management
• Improved risks & business comprehension
• Assets and Capital managed with objective and
exhaustive indicators
• Multiple uses for business management
• Fulfilled regulator(s) requirements with high
appreciation level
• Optimal ratio "Complex tool/costs"
• Propose collaboration for model conception and
metrics definition
• Definition of model scope, elements and
customization level
• Model structuration and implementation
• Model testing, operationalization and
documentation
• Operational implementation
• Continuation of developments with more
accurate mechanisms & multiple add-ons
purposes only
• Create a multi-purpose tool satisfying the
different stakeholders
• Insure exactitude for market consistency or/and
regarding historic observations
• Tool should be auditable & operational
• Tool should be flexible and ready-to-be enhanced

Saving Accounts replicator portfolio
• Duration assumptions constraints imposed
by local regulator
• Legacy model and related hedge
• Existing central software to be interfaced
for FTP & NII calculation purposes
• Client: Belgian Bank-Insurance company
• Strong foothold in all 3 Benelux countries
• Recent development of retail business
volumes
• Competitive pressure on deposit rates
• Pressure from local regulator to improve
the modeling and hedging of non-
maturing liabilities
Client Situation
• Improved NII margin through improved
hedging strategy
• Automated tool enabling the simulation
both ‘buy & hold’ investment strategies as
well as constant duration rebalancing
strategies
• Risk control & monitoring tools
Results
Issues
• Review & analysis of hedging techniques in
place
• Split of overall saving account modeling
within homogenous product categories for
hedging purposes
• Development of a fully automated
replicator model simulating ALM
investment processes
• Selection of optimal hedging strategy per
product categories & implementation of the
propose solution
• Automated interface with the existing
models
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purposes only

Contact details
Place de l’Université, 25
B-1348 Louvain-la-Neuve
(Belgium)
T +32 (0) 10 84 07 50
www.reacfin.com
35
François Ducuroir
Managing Partner
+32 472 72 32 05
francois.ducuroir@reacfin.com

Place de l’Université 25
B-1348 Louvain-la-Neuve
www.reacfin.com
Disclaimer:
The recipient of this document should treat all
information as strictly confidential and only in the
context stated below. Information may not be
disclosed to any third party without the prior join-
consent of Reacfin.
Estimates given in this presentation are based on our
current knowledge, they can be based upon our
previous experience within the Undertaking, as well as
taking into account similar projects in the same
context as the Undertaking, either locally, within
majority of the EU countries as well as overseas.
This presentation is only the supporting document of
a verbal presentation. Hence, it is not intended to be
exhaustive. Quoting or using this document on its own
might be misleading. As a result, these materials may
not be used by anybody except their authors nor
should they be relied upon in any way for any purpose
other than as contemplated by joint written
agreement with Reacfin.

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Presentation of Reacfin’s Economic Scenario Generator (ESG) online demo