Tailored Economic Scenario Generators ( ESG ) solutions Presentation

Transcription

1 Tailored Economic Scenario Generators ( ESG ) solutions Presentation Reacfin (2016) Place de l Université 25, 1348 Louvain-La-Neuve, Belgium Please read the important disclaimer at the end of this presentation M: info@reacfin.com T: +32 (0)

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3 Table of content SECTIONS Reacfin s ESG solutions On-line demo About Reacfin CONTENT What is an ESG Reacfin s tailored solutions High-level model architecture Integration in full balance-sheet projection frameworks Model granularity 3

4 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 ) ESG s simulate risk drivers, not prices, values or balance-sheets items 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. Illustration Market Instruments Corporate Bonds Inflation Linked Bond Shares Risk drivers Credit Spreads Yield Curve Inflation Equity Price Indices 4

5 Reacfin s tailored open-source ESG solutions 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.) 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. 5

6 Typical high-level Model Architecture Inputs ESG Tool Downloadable Output Configuration Files Input assessment CSV files containing: Historical time series Modeling assumptions A priori calibration assumptions Graphical interface to check sanity of inputs & selection models assumptions ESG Calibration Tool Calibration parameters CSV file 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 ESG Scenarios CSV file Visualizing simulation results and performing adequacy tests 6

7 Designed to be integrated in full balance-sheet projection frameworks ESG Tool Calibration and simulation of risk drivers (e.g. interest rates, credit spreads) ESG Scenarios Our ESG solution is further designed to integrate into Reacfin s set of tools for portfolio- and balance-sheet projections. Instrument Tool Instrument Scenarios Balance Sheet Tool Balance Sheet Scenarios 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. 7

8 Key dimensions of risk drivers modeling granularity Possible level of granularity currently foreseen Fixed incomes Other Split per type of assets or liabilities* Split per type (e.g. RE, Equities, HF, etc.) Split per geographies Split per geographies Split per rating Split per Sectors Split per maturity bucket 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. 8

9 Intergation in Reacfin s SAA suite of tools Overall architecture Projection tools Optimization tools 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.) SAA optimization given wide sets of optimization criteria s and constraints 9

10 Table of content SECTIONS Reacfin s ESG solutions On-line demo About Reacfin CONTENT Where to find Reacfin s on-line ESG Demo Defining the types of simulations Visualizing the results 10

11 Where to find Reacfin s on-line ESG - Demo Simplified version available for demo purposes on

12 Defining types of simulations ESG modeling principles 12 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* 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 (*) Calibrated as twice worst historical observed (**) See slides dedicated to ESG granularity (***) For loans: performing or defaulted

13 Defining types of simulations In first tab Input 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

14 Defining types of simulations In first tab Input Selecting stochastic models 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 Illustrative example: Dispersion of the projections for 3 Months interest rates Calibration period: from to Black-Karasinski Cox-Ingersoll-Ross Percentiles: 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) 14

15 Defining types of simulations In first tab Input Selecting the number of stochastic factors 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). In first tab Input Under the «Select Models» menu 15

16 Defining types of simulations In first tab Input Selecting the calibration period 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. In first tab Input Under the «Select Factors» menu (*) 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 16

17 Defining types of simulations In first tab Input Simulation options At the bottom of the first tab Input, users may then define: o o o o the start date of their stochastic simulation The number of simulations to be performed The number of years over which simulations have to be performed (the Projection Horizon ) 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 14 th 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 To launch the calculations, click on the black button Calculate. Then switch to tab Outputs to see the results. Large numbers of simulations (especially on monthly step sizes) may induce long computation times. 17 (*) Unlike our professional tailored versions which will also offer different frequencies of projections (e.g. quarterly, weekly, daily, tailored)

18 Visualizing the results In second tab Outputs Simulation options This grey tag indicates the program is computing and results are about to come Select the second tab Outputs Select here the asset class you want to visualize Select here the subasset 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 18

19 Visualizing the results In second tab Outputs Simulation options This grey tag indicates the program is computing and results are about to come Select the second tab Outputs Select here the asset class you want to visualize Select here the subasset 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 19

20 Visualizing the results In second tab Outputs Charts options All charts of our are produced using Ploty ( 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 Reset axis Save chart on Ploty server for editing Auto scale Zoom on selected part of the chart Explore ( pan ) the chart when zoomed Zoom in and zoom out at the center of the chart 20

21 Visualizing the results Screenshots of the ESG ESG set-up & definitions board Results assessment & calibration Scenario's & parameters exportation 21

22 Table of content SECTIONS Reacfin s ESG solutions On-line demo User Guide About Reacfin CONTENT Who we are Our driving values Reacfin s management Reacfin s 4 core fields of expertise What we deliver Examples of recent assignments 22

23 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. What we do Modeling Risk implementation advisory Validation & model reviews Specialized strategic risk consulting 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. 23

24 Our driving values We put great emphasis at strictly articulating our work around 5 fundamental 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! 24

25 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 25

26 Reacfin s 4 core fields of expertise: Our centers of excellence Risk & Portfolio Management 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 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 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 26 (*) DFA = Dynamic Financial Analysis

27 What we deliver 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 Balanced and pragmatic approach Client-centric solutions focussed on deliverables Respecting the principle of proportionality Cost efficient within tight pre-agreed budgets No black box Solutions We deliver results, not reports! Open source solutions Close cooperation with our clients Clearly structured processes Lean & efficient tailored project management Regular progress reviews Close cooperation with our clients Documentation, coaching & training Clear & comprehensive documentation compliant existing or upcoming regulation Adapted trainings at all levels of the organisation Coaching support for implementation and operationnalisation of processes 27

28 Example of previous assignment: Strategic Asset Allocation for Bank & Insurance group All dummy numbers & graphs for illustrative purposes only Client Situation Reacfin s Contribution 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 Issues 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 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 Results 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 28

29 Example of previous assignment: Investment Portfolio Diagnostic All dummy numbers & graphs for illustrative purposes only Client Situation 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. Issues 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. Reacfin s Contribution 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 Results 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 29

30 Example of previous assignment: Strategic Asset Allocation Study for Pension Fund All dummy numbers & graphs for illustrative purposes only Client Situation 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. Reacfin s Contribution 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 Issues 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 Results 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 30

31 Example of previous assignment: Pricing of a complex OTC derivatives All dummy numbers & graphs for illustrative purposes only Client Situation 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. Reacfin s Contribution 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 Issues 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 Results 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 31

32 Example of previous assignment: Development of Heston model for Equity in a Risk Neutral framework All dummy numbers & graphs for illustrative purposes only Client Situation Reacfin s Contribution 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 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 Issues 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 Results Consequently improved reproduction of initial market price Generation of a stochastic volatility Consequent reduction of runtime 32

33 Example of previous assignment: ALM Model development & implementation All dummy numbers & graphs for illustrative purposes only Client Situation 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 Reacfin Contribution 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 Issues 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 Results & Benefits 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" 33

34 Example of previous assignment: Saving Accounts replicator portfolio All dummy numbers & graphs for illustrative purposes only Client Situation 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 nonmaturing liabilities Issues Duration assumptions constraints imposed by local regulator Legacy model and related hedge Existing central software to be interfaced for FTP & NII calculation purposes Reacfin s Contribution 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 Results 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 34

35 Contact details Place de l Université, 25 B-1348 Louvain-la-Neuve (Belgium) T +32 (0) François Ducuroir Managing Partner francois.ducuroir@reacfin.com 35

36 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 joinconsent 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. Place de l Université 25 B-1348 Louvain-la-Neuve