This document provides information about an investment risk management course taught by Louis Tremblay. It includes a biography of Louis Tremblay detailing his educational background and professional experience. The syllabus outlines the course content, schedule of topics to be covered, and evaluation methods which involve quizzes, a group project, and a final exam. The course will use tools like Excel, PowerPoint and Bloomberg.
20140528 - ESGs (Czech Society of Actuaries) - Shaun LazzariShaun Lazzari
This document discusses testing and validating stochastic economic scenarios. It covers:
1) Using economic scenario generators (ESGs) to generate scenarios for variables like interest rates, equities, and credit spreads for purposes like valuation and risk analysis.
2) Formulating calibration assumptions, which involves calibrating models to market data while addressing data limitations.
3) Validating scenario sets through analyses like no-arbitrage tests, market consistency checks, and assessing distributional features to ensure scenarios are reasonable.
This document outlines Robert Bain's presentations on toll road forecasting errors and how to improve traffic forecasting. It discusses litigation related to inaccurate toll road forecasts, feedback from clients on how forecasts can provide more value, and directions for the industry to improve practices. Key recommendations include providing more transparency into assumptions, embracing uncertainty, focusing reports on future forecasts rather than past models, and emphasizing the narrative explanations ("traffic story") behind projections.
This document provides an overview of On Demand Agility (ODA), a global technology firm that offers outsourcing solutions. ODA helps clients achieve their business goals through technology and process outsourcing. It has over 1200 resources across multiple locations and expertise in industries like banking, financial services, and insurance. ODA provides services like staff augmentation, project outsourcing, and managed capacity across various technologies and domains. It has a track record of successful projects for clients like Credit Suisse, Royal Bank of Scotland, and Prudential.
Rob Bain - Toll Road Forecasting (abridged)JumpingJaq
The document discusses feedback from clients and lawyers on how to improve toll road traffic and revenue forecasting reports. Clients suggest focusing forecasts more on the future rather than just the base year, providing better transparency into the modeling process, and telling a compelling "traffic story" narrative. Lawyers describe past toll road litigation related to allegedly misleading traffic projections, with settlements in the tens to hundreds of millions of dollars. The feedback indicates a need for more realistic, evidence-based forecasts that consider a range of possible outcomes.
Investor readiness: Startup valuation by Startups.beStartUps.be
Check out the most reliable methodologies and become aware of the risk factors. More information on getting investor-ready: www.startups.be/fundraising
The document discusses risk management in StatoilHydro. It defines key terms like risk, threat, and opportunity. It outlines StatoilHydro's values and risk picture, including different types of risks. It describes the risk management process, which includes risk identification, assessment, response actions, and control. Tools for risk management include a risk module in PIMSWeb for input, use, and output of risk data. Risk management is important throughout a project's lifecycle to understand uncertainties and their potential impacts.
20140528 - ESGs (Czech Society of Actuaries) - Shaun LazzariShaun Lazzari
This document discusses testing and validating stochastic economic scenarios. It covers:
1) Using economic scenario generators (ESGs) to generate scenarios for variables like interest rates, equities, and credit spreads for purposes like valuation and risk analysis.
2) Formulating calibration assumptions, which involves calibrating models to market data while addressing data limitations.
3) Validating scenario sets through analyses like no-arbitrage tests, market consistency checks, and assessing distributional features to ensure scenarios are reasonable.
This document outlines Robert Bain's presentations on toll road forecasting errors and how to improve traffic forecasting. It discusses litigation related to inaccurate toll road forecasts, feedback from clients on how forecasts can provide more value, and directions for the industry to improve practices. Key recommendations include providing more transparency into assumptions, embracing uncertainty, focusing reports on future forecasts rather than past models, and emphasizing the narrative explanations ("traffic story") behind projections.
This document provides an overview of On Demand Agility (ODA), a global technology firm that offers outsourcing solutions. ODA helps clients achieve their business goals through technology and process outsourcing. It has over 1200 resources across multiple locations and expertise in industries like banking, financial services, and insurance. ODA provides services like staff augmentation, project outsourcing, and managed capacity across various technologies and domains. It has a track record of successful projects for clients like Credit Suisse, Royal Bank of Scotland, and Prudential.
Rob Bain - Toll Road Forecasting (abridged)JumpingJaq
The document discusses feedback from clients and lawyers on how to improve toll road traffic and revenue forecasting reports. Clients suggest focusing forecasts more on the future rather than just the base year, providing better transparency into the modeling process, and telling a compelling "traffic story" narrative. Lawyers describe past toll road litigation related to allegedly misleading traffic projections, with settlements in the tens to hundreds of millions of dollars. The feedback indicates a need for more realistic, evidence-based forecasts that consider a range of possible outcomes.
Investor readiness: Startup valuation by Startups.beStartUps.be
Check out the most reliable methodologies and become aware of the risk factors. More information on getting investor-ready: www.startups.be/fundraising
The document discusses risk management in StatoilHydro. It defines key terms like risk, threat, and opportunity. It outlines StatoilHydro's values and risk picture, including different types of risks. It describes the risk management process, which includes risk identification, assessment, response actions, and control. Tools for risk management include a risk module in PIMSWeb for input, use, and output of risk data. Risk management is important throughout a project's lifecycle to understand uncertainties and their potential impacts.
This document summarizes some of the key challenges in computational finance, specifically around valuing and risk managing derivatives. It discusses how derivatives are priced using simple models but notes credit and liquidity risks were not fully accounted for. It then covers the importance of credit valuation adjustments to account for counterparty default. The rest of the document discusses the complexity of implementing these adjustments at a portfolio level with many instruments and counterparties, and the use of GPUs to help with the significant computational requirements. Finally, it outlines some research projects underway to develop more advanced modeling techniques.
Impact of Valuation Adjustments (CVA, DVA, FVA, KVA) on Bank's Processes - An...Andrea Gigli
The talk hold in London on September 10th at the 5th Annual XVA Forum on Funding, Capital and Valuation. It covered some implications of Valuation Adjustments like CVA, DVA, FVA and KVA (XVAs) in the Pricing of Derivatives, Data Model Definition, Risk Management, Accounting, Trade Workflow processing.
This document discusses an approach to gathering and analyzing global financial information using artificial intelligence to produce standardized discounted cash flow valuations for over 3,000 stocks. Key points include:
1) Traditional investment analysis relies on estimates of cash flows, but systems need to apply risk thinking at that level to outperform.
2) Global data availability varies in quality and pace, so assembling information on a global basis is challenging.
3) The approach uses artificial intelligence to estimate recurring revenue and cost items, normalize trends, and produce risk-adjusted DCF valuations across sectors.
4) Analysts review the results for errors and market events, applying limited corrections to leverage the scaling of labor that AI allows
Credit risk with neural networks bankruptcy prediction machine learningArmando Vieira
The document discusses credit risk management with AI tools. It summarizes that credit scoring is used to statistically quantify risk by converting applicant information into numbers and a score. The objective is to forecast future performance based on past client behavior. It then discusses using various machine learning models like HLVQ-C and neural networks to predict financial distress, classify companies, and improve bankruptcy prediction. The models were tested on real world credit and financial datasets.
In this study we survey practices and supervisory expectations for stress testing (ST), in a credit risk framework for banking book exposures. We introduce and motivate ST; and discuss the function, supervisory requirements and expectations, credit risk parameters, interpretation results
with respect to ST. This includes a typology of ST (uniform testing, risk factor sensitivities, scenario analysis; and historical, statistical and hypothetical scenarios) and procedures for con-ducting ST. We conclude with two simple and practical stress testing examples, one a ratings migration based approach, and the other a top-down ARIMA modeling approach.
Notes for Computational Finance lectures, Antoine Savine at Copenhagen Univer...Antoine Savine
The document discusses computational finance and machine learning in finance. It begins by noting the need for speed in pricing and hedging derivatives, as institutions must compute values and sensitivities rapidly to hedge risk before markets move. Traditional methods become impractical for complex transactions. The document then discusses various techniques to achieve faster computation, including Monte Carlo simulation, adjoint differentiation, leveraging hardware, and machine learning. Regulatory requirements like counterparty valuation adjustment (CVA) further increase computational demands. Overall, the document emphasizes that speed is critical in financial computation and an active area of research.
The document provides information about the Center of Mathematical Finance (CMF) programs. It discusses CMF's history and key milestones since 2007. It outlines their current programs, including Financial Analysis, Quantitative Analysis, and new programs in Economic Analysis and Data-Driven Analysis. It also introduces the instructors and consultants involved in the Financial Analysis and Quantitative Analysis programs.
This document discusses the risk management system at MOSL & Literacy for derivatives and commodity trading in Ahmedabad, India. It outlines the objectives of studying the firm's risk management and literacy levels. Various risks for investors and the firm are identified. The risk management process involves identifying, analyzing, planning for, controlling, and communicating risks. MOSL sets exposure limits for clients based on factors like market conditions, client history, account position, risk profile, and income. The document also analyzes investors' preferences for trading instruments, sources of learning, and the most preferred broking firms.
This document describes regulatory reporting services offered by FD-Reporting including Solvency II, AIFMD, liquidity, and other regulatory reports. Key features include real-time data import and validation, predefined templates and procedures, and delivery of reports on-site or remotely. The platform requires minimal infrastructure and offers savings of 55% compared to multi-system reporting. Future enhancements will include additional regulations and full XBRL support. Contact information is provided to discuss customized solutions and timelines for new clients.
1. Pavel Shevchenko works for CSIRO's Quantitative Risk Management group, which develops mathematical models for financial and other risks.
2. CSIRO is Australia's national science agency, with over 6500 staff across various divisions including Mathematical and Information Sciences.
3. The Quantitative Risk Management group applies techniques like extreme value analysis, dependence modelling, and Bayesian methods to areas like financial, infrastructure, environmental and security risks.
Direct Surety’s roots are in the construction industry. Through the use of technology, Direct Surety underwriters show contractors exactly how their bonding limits are determined. Working with a proprietary risk analysis system and Enterprise Risk Management (ERM) methodology, Direct Surety determines operational strengths and weaknesses, and then suggests strategic improvement options to help contractors raise profitability, earn more credit and obtain better pricing.
Direct Surety is the only company that enables contractors to:
• Go direct to the decision maker to establish surety credit
• See exactly how credit limits are determined
• Obtain a clear plan to improve credit limits and lower price
• Work under a signed non-disclosure agreement
• Establish a backup line of surety credit
• Switch from a broker when ready
Direct Surety – Surety bonds for the Digital Age. Push your limits.
1. The document describes a two-dimensional scorecard approach to prioritize grants and lending that considers both risk and poverty impacts.
2. A risk scorecard is developed using borrower characteristics and historical performance data to estimate creditworthiness. Projects are ranked by their risk scores, with only those passing a minimum threshold considered further.
3. A poverty scorecard is then applied using data on project location, employment impacts, and spillover effects. Principal component analysis is used to rank projects based on their potential for poverty reduction. The highest scoring projects that also passed the risk threshold are prioritized for funding.
This document provides a summary of Philip Green's experience and expertise. He has over 12 years of experience as a senior project manager, business analyst, and functional architect on global energy trading and risk management projects. He has expertise in capital markets, derivatives, foreign exchange, and other financial domains. He is proficient in various trading, risk management, and regulatory reporting systems and has experience across many asset classes and products.
enableIT is a global company that provides expertise in capital markets. It has customers including sell-side institutions like investment banks and brokers, buy-side institutions like investment managers and hedge funds, and solution providers. enableIT focuses on capital market solutions and information risk management across equities, commodities, derivatives, fixed income, forex, and prime brokerage. It helps customers with electronic trading, risk management, data management, and governance, regulatory, compliance, and controls. enableIT works on both a time and expense model and a project basis with fixed bids or capped time and expenses.
In World Expo 2010 Shanghai – the most visited Expo in the World History
https://www.britannica.com/event/Expo-Shanghai-2010
China’s official organizer of the Expo, CCPIT (China Council for the Promotion of International Trade https://en.ccpit.org/) has chosen Dr. Alyce Su as the Cover Person with Cover Story, in the Expo’s official magazine distributed throughout the Expo, showcasing China’s New Generation of Leaders to the World.
Confirmation of Payee (CoP) is a vital security measure adopted by financial institutions and payment service providers. Its core purpose is to confirm that the recipient’s name matches the information provided by the sender during a banking transaction, ensuring that funds are transferred to the correct payment account.
Confirmation of Payee was built to tackle the increasing numbers of APP Fraud and in the landscape of UK banking, the spectre of APP fraud looms large. In 2022, over £1.2 billion was stolen by fraudsters through authorised and unauthorised fraud, equivalent to more than £2,300 every minute. This statistic emphasises the urgent need for robust security measures like CoP. While over £1.2 billion was stolen through fraud in 2022, there was an eight per cent reduction compared to 2021 which highlights the positive outcomes obtained from the implementation of Confirmation of Payee. The number of fraud cases across the UK also decreased by four per cent to nearly three million cases during the same period; latest statistics from UK Finance.
In essence, Confirmation of Payee plays a pivotal role in digital banking, guaranteeing the flawless execution of banking transactions. It stands as a guardian against fraud and misallocation, demonstrating the commitment of financial institutions to safeguard their clients’ assets. The next time you engage in a banking transaction, remember the invaluable role of CoP in ensuring the security of your financial interests.
For more details, you can visit https://technoxander.com.
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This document summarizes some of the key challenges in computational finance, specifically around valuing and risk managing derivatives. It discusses how derivatives are priced using simple models but notes credit and liquidity risks were not fully accounted for. It then covers the importance of credit valuation adjustments to account for counterparty default. The rest of the document discusses the complexity of implementing these adjustments at a portfolio level with many instruments and counterparties, and the use of GPUs to help with the significant computational requirements. Finally, it outlines some research projects underway to develop more advanced modeling techniques.
Impact of Valuation Adjustments (CVA, DVA, FVA, KVA) on Bank's Processes - An...Andrea Gigli
The talk hold in London on September 10th at the 5th Annual XVA Forum on Funding, Capital and Valuation. It covered some implications of Valuation Adjustments like CVA, DVA, FVA and KVA (XVAs) in the Pricing of Derivatives, Data Model Definition, Risk Management, Accounting, Trade Workflow processing.
This document discusses an approach to gathering and analyzing global financial information using artificial intelligence to produce standardized discounted cash flow valuations for over 3,000 stocks. Key points include:
1) Traditional investment analysis relies on estimates of cash flows, but systems need to apply risk thinking at that level to outperform.
2) Global data availability varies in quality and pace, so assembling information on a global basis is challenging.
3) The approach uses artificial intelligence to estimate recurring revenue and cost items, normalize trends, and produce risk-adjusted DCF valuations across sectors.
4) Analysts review the results for errors and market events, applying limited corrections to leverage the scaling of labor that AI allows
Credit risk with neural networks bankruptcy prediction machine learningArmando Vieira
The document discusses credit risk management with AI tools. It summarizes that credit scoring is used to statistically quantify risk by converting applicant information into numbers and a score. The objective is to forecast future performance based on past client behavior. It then discusses using various machine learning models like HLVQ-C and neural networks to predict financial distress, classify companies, and improve bankruptcy prediction. The models were tested on real world credit and financial datasets.
In this study we survey practices and supervisory expectations for stress testing (ST), in a credit risk framework for banking book exposures. We introduce and motivate ST; and discuss the function, supervisory requirements and expectations, credit risk parameters, interpretation results
with respect to ST. This includes a typology of ST (uniform testing, risk factor sensitivities, scenario analysis; and historical, statistical and hypothetical scenarios) and procedures for con-ducting ST. We conclude with two simple and practical stress testing examples, one a ratings migration based approach, and the other a top-down ARIMA modeling approach.
Notes for Computational Finance lectures, Antoine Savine at Copenhagen Univer...Antoine Savine
The document discusses computational finance and machine learning in finance. It begins by noting the need for speed in pricing and hedging derivatives, as institutions must compute values and sensitivities rapidly to hedge risk before markets move. Traditional methods become impractical for complex transactions. The document then discusses various techniques to achieve faster computation, including Monte Carlo simulation, adjoint differentiation, leveraging hardware, and machine learning. Regulatory requirements like counterparty valuation adjustment (CVA) further increase computational demands. Overall, the document emphasizes that speed is critical in financial computation and an active area of research.
The document provides information about the Center of Mathematical Finance (CMF) programs. It discusses CMF's history and key milestones since 2007. It outlines their current programs, including Financial Analysis, Quantitative Analysis, and new programs in Economic Analysis and Data-Driven Analysis. It also introduces the instructors and consultants involved in the Financial Analysis and Quantitative Analysis programs.
This document discusses the risk management system at MOSL & Literacy for derivatives and commodity trading in Ahmedabad, India. It outlines the objectives of studying the firm's risk management and literacy levels. Various risks for investors and the firm are identified. The risk management process involves identifying, analyzing, planning for, controlling, and communicating risks. MOSL sets exposure limits for clients based on factors like market conditions, client history, account position, risk profile, and income. The document also analyzes investors' preferences for trading instruments, sources of learning, and the most preferred broking firms.
This document describes regulatory reporting services offered by FD-Reporting including Solvency II, AIFMD, liquidity, and other regulatory reports. Key features include real-time data import and validation, predefined templates and procedures, and delivery of reports on-site or remotely. The platform requires minimal infrastructure and offers savings of 55% compared to multi-system reporting. Future enhancements will include additional regulations and full XBRL support. Contact information is provided to discuss customized solutions and timelines for new clients.
1. Pavel Shevchenko works for CSIRO's Quantitative Risk Management group, which develops mathematical models for financial and other risks.
2. CSIRO is Australia's national science agency, with over 6500 staff across various divisions including Mathematical and Information Sciences.
3. The Quantitative Risk Management group applies techniques like extreme value analysis, dependence modelling, and Bayesian methods to areas like financial, infrastructure, environmental and security risks.
Direct Surety’s roots are in the construction industry. Through the use of technology, Direct Surety underwriters show contractors exactly how their bonding limits are determined. Working with a proprietary risk analysis system and Enterprise Risk Management (ERM) methodology, Direct Surety determines operational strengths and weaknesses, and then suggests strategic improvement options to help contractors raise profitability, earn more credit and obtain better pricing.
Direct Surety is the only company that enables contractors to:
• Go direct to the decision maker to establish surety credit
• See exactly how credit limits are determined
• Obtain a clear plan to improve credit limits and lower price
• Work under a signed non-disclosure agreement
• Establish a backup line of surety credit
• Switch from a broker when ready
Direct Surety – Surety bonds for the Digital Age. Push your limits.
1. The document describes a two-dimensional scorecard approach to prioritize grants and lending that considers both risk and poverty impacts.
2. A risk scorecard is developed using borrower characteristics and historical performance data to estimate creditworthiness. Projects are ranked by their risk scores, with only those passing a minimum threshold considered further.
3. A poverty scorecard is then applied using data on project location, employment impacts, and spillover effects. Principal component analysis is used to rank projects based on their potential for poverty reduction. The highest scoring projects that also passed the risk threshold are prioritized for funding.
This document provides a summary of Philip Green's experience and expertise. He has over 12 years of experience as a senior project manager, business analyst, and functional architect on global energy trading and risk management projects. He has expertise in capital markets, derivatives, foreign exchange, and other financial domains. He is proficient in various trading, risk management, and regulatory reporting systems and has experience across many asset classes and products.
enableIT is a global company that provides expertise in capital markets. It has customers including sell-side institutions like investment banks and brokers, buy-side institutions like investment managers and hedge funds, and solution providers. enableIT focuses on capital market solutions and information risk management across equities, commodities, derivatives, fixed income, forex, and prime brokerage. It helps customers with electronic trading, risk management, data management, and governance, regulatory, compliance, and controls. enableIT works on both a time and expense model and a project basis with fixed bids or capped time and expenses.
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In World Expo 2010 Shanghai – the most visited Expo in the World History
https://www.britannica.com/event/Expo-Shanghai-2010
China’s official organizer of the Expo, CCPIT (China Council for the Promotion of International Trade https://en.ccpit.org/) has chosen Dr. Alyce Su as the Cover Person with Cover Story, in the Expo’s official magazine distributed throughout the Expo, showcasing China’s New Generation of Leaders to the World.
Confirmation of Payee (CoP) is a vital security measure adopted by financial institutions and payment service providers. Its core purpose is to confirm that the recipient’s name matches the information provided by the sender during a banking transaction, ensuring that funds are transferred to the correct payment account.
Confirmation of Payee was built to tackle the increasing numbers of APP Fraud and in the landscape of UK banking, the spectre of APP fraud looms large. In 2022, over £1.2 billion was stolen by fraudsters through authorised and unauthorised fraud, equivalent to more than £2,300 every minute. This statistic emphasises the urgent need for robust security measures like CoP. While over £1.2 billion was stolen through fraud in 2022, there was an eight per cent reduction compared to 2021 which highlights the positive outcomes obtained from the implementation of Confirmation of Payee. The number of fraud cases across the UK also decreased by four per cent to nearly three million cases during the same period; latest statistics from UK Finance.
In essence, Confirmation of Payee plays a pivotal role in digital banking, guaranteeing the flawless execution of banking transactions. It stands as a guardian against fraud and misallocation, demonstrating the commitment of financial institutions to safeguard their clients’ assets. The next time you engage in a banking transaction, remember the invaluable role of CoP in ensuring the security of your financial interests.
For more details, you can visit https://technoxander.com.
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Clive Bolton, CEO, Life Insurance M&G Plc
Jim Boyd, CEO, Equity Release Council
Molly Broome, Economist, Resolution Foundation
Nida Broughton, Co-Director of Economic Policy, Behavioural Insights Team
Jonathan Cribb, Associate Director and Head of Retirement, Savings, and Ageing, Institute for Fiscal Studies
Joanna Elson CBE, Chief Executive Officer, Independent Age
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Steve Groves, Chair, Key Retirement Group
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Sue Lewis, ILC Trustee
Siobhan Lough, Senior Consultant, Hymans Robertson
Mick McAteer, Co-Director, The Financial Inclusion Centre
Stuart McDonald MBE, Head of Longevity and Democratic Insights, LCP
Anusha Mittal, Managing Director, Individual Life and Pensions, M&G Life
Shelley Morris, Senior Project Manager, Living Pension, Living Wage Foundation
Sarah O'Grady, Journalist
Will Sherlock, Head of External Relations, M&G Plc
Daniela Silcock, Head of Policy Research, Pensions Policy Institute
David Sinclair, Chief Executive, ILC
Jordi Skilbeck, Senior Policy Advisor, Pensions and Lifetime Savings Association
Rt Hon Sir Stephen Timms, former Chair, Work & Pensions Committee
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During the event, the results of the 25-th monthly survey of business executives “Ukrainian Business during the war”, which was conducted in May 2024, were presented.
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Digital, interactive art showing the struggle of a society in providing for its present population while also saving planetary resources for future generations. Spread across several frames, the art is actually the rendering of real and speculative data. The stereographic projections change shape in response to prompts and provocations. Visitors interact with the model through speculative statements about how to increase savings across communities, regions, ecosystems and environments. Their fabulations combined with random noise, i.e. factors beyond control, have a dramatic effect on the societal transition. Things get better. Things get worse. The aim is to give visitors a new grasp and feel of the ongoing struggles in democracies around the world.
Stunning art in the small multiples format brings out the spatiotemporal nature of societal transitions, against backdrop issues such as energy, housing, waste, farmland and forest. In each frame we see hopeful and frightful interplays between spending and saving. Problems emerge when one of the two parts of the existential anaglyph rapidly shrinks like Arctic ice, as factors cross thresholds. Ecological wealth and intergenerational equity areFour at stake. Not enough spending could mean economic stress, social unrest and political conflict. Not enough saving and there will be climate breakdown and ‘bankruptcy’. So where does speculative design start and the gambling and betting end? Behind each fabular frame is a four ratio problem. Each ratio reflects the level of sacrifice and self-restraint a society is willing to accept, against promises of prosperity and freedom. Some values seem to stabilise a frame while others cause collapse. Get the ratios right and we can have it all. Get them wrong and things get more desperate.
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Understanding Ponzi Schemes
A Ponzi scheme is an investment scam where returns are paid to earlier investors using the capital from newer investors, rather than from legitimate profit earned. The scheme relies on a constant influx of new investments to continue paying the promised returns. Eventually, when the flow of new money slows down or stops, the scheme collapses, leaving the majority of investors with substantial financial losses.
Historical Context: Charles Ponzi and His Legacy
Charles Ponzi is the namesake of this deceptive practice. In the 1920s, Ponzi promised investors in Boston a 50% return within 45 days or 100% return in 90 days through arbitrage of international reply coupons. Initially, he paid returns as promised, not from profits, but from the investments of new participants. When his scheme unraveled, it resulted in losses exceeding $20 million (equivalent to about $270 million today).
Notable American Ponzi Schemes
1. Bernie Madoff: Perhaps the most notorious Ponzi scheme in recent history, Bernie Madoff’s fraud involved $65 billion. Madoff, a well-respected figure in the financial industry, promised steady, high returns through a secretive investment strategy. His scheme lasted for decades before collapsing in 2008, devastating thousands of investors, including individuals, charities, and institutional clients.
2. Allen Stanford: Through his company, Stanford Financial Group, Allen Stanford orchestrated a $7 billion Ponzi scheme, luring investors with fraudulent certificates of deposit issued by his offshore bank. Stanford promised high returns and lavish lifestyle benefits to his investors, which ultimately led to a 110-year prison sentence for the financier in 2012.
3. Tom Petters: In a scheme that lasted more than a decade, Tom Petters ran a $3.65 billion Ponzi scheme, using his company, Petters Group Worldwide. He claimed to buy and sell consumer electronics, but in reality, he used new investments to pay off old debts and fund his extravagant lifestyle. Petters was convicted in 2009 and sentenced to 50 years in prison.
4. Eric Dalius and Saivian: Eric Dalius, a prominent figure behind Saivian, a cashback program promising high returns, is under scrutiny for allegedly orchestrating a Ponzi scheme. Saivian enticed investors with promises of up to 20% cash back on everyday purchases. However, investigations suggest that the returns were paid using new investments rather than legitimate profits. The collapse of Saivian l
4. Degree Year obtained
4
Enrolled Actuary (USA) 2000
Fellow Society of Actuaries (USA) 2001
Fellow Canadian Institute of Actuaries (Canada) 2001
Chartered Financial Analyst (CFA) 2002
Institut des Actuaires (France) 2021
FRM Candidate Level I obtained in 2021
Actuarial Science (Universite Laval – Quebec) 1995
Degree Year obtained
Bio Louis Tremblay
5. Period Company Location Role
5
1996 – 1998 Aon Hewitt Montreal Actuary – pension funds (actuarial liabilities)
1998 – 2001 Aon Hewitt New Jersey
Actuary – post-retirement benefits (other than pensions)
liabilities accounting
2001 – 2005 Aon Hewitt Montreal Actuary – pension funds (actuarial liabilities)
2005 – 2008 Caisse de dépôt et placement du Québec Montreal Business development
2007 – 2010 Université du Québec à Montréal Montreal Teacher, corporate finance
2008 – 2010 Aon Hewitt Montreal Investment consultant & ALM
2011 – 2020 Optimum Group Paris Chief Investment Officer (CIO) & General Manager (COO)
2020 – Institut Européen de Synergologie Paris Training
2021 –
Top Finance & NEOMA & Audencia &
other institutions
France Teacher : Stats, CFA, FRM, Data visualization, Power BI, …
…Bio Louis Tremblay
7. 7
1. Consumer staples
2. Consumer discretionary
3. Health care
4. Industrials
5. Energy
6. Financial services
7. Utilities
8. I.T.
9. Materials
10. Communications
11. Real estate
Global Industry Classification
Standard (GICS)
Defensive sectors
Aerospace
Personal care products
Automotive
manufacturers
• 11 sectors !
Competition
8. 8
• Choose 5 stocks (in CAC40)
• The winner at the end of the semester
1. Air Liquide Materials
2. Airbus Industrials
3. Alstom Industrials
4. ArcelorMittal Materials
5. AXA Financial
6. BNP Paribas Financial
7. Bouygues Industrials
8. Capgemini I.T.
9. Carrefour Consumer staples
10. Crédit Agricole Financial
11. Danone Consumer staples
12. Dassault Systè I.T.
13. Edenred Financial
14. Engie Utilities
15. EssilorLuxottic Healthcare
16. Eurofins Scient Healthcare
17. Hermès Consumer cyclical
18. Kering Consumer cyclical
19. L'Oréal Consumer staples
20. Legrand Industrials
21. LVMH Consumer cyclical
22. Michelin Industrials
23. Orange Communications
24. Pernod Ricard Consumer staples
25. Publicis Communications
26. Renault Consumer cyclical
27. Safran Industrials
28. Saint-Gobain Industrials
29. Sanofi Healthcare
30. Schneider Elec Industrials
31. Société Général Financial
32. Stellantis Consumer cyclical
33. STMicroelectro I.T.
34. Teleperformanc Communications
35. Thales Industrials
36. TotalEnergies Energy
37. Un.-Rod.-Westfi Real Estate
38. Veolia Industrials
39. Vinci Industrials
40. Worldline Communications
…Competition
9. City Date Chapters Type
9
Rouen (123) 11.01
• Bank’s economic capital vs credit risk
• Factors used to calculate economic capital for credit risk:
probability of default, exposure & loss rate
• Expected loss (EL) & Unexpected loss (UL)
Présentiel synchrone
Rouen (123) 24.01
• Variance of default probability (binomial distribution)
• UL for a portfolio & UL contribution of each asset
• How economic capital is derived
• Model credit loss distribution
Présentiel synchrone
Rouen (123) 24.01
• Experts-based approaches, statistical-based models &
numerical approaches to predicting default
• Rating migration matrix & probability of default…
• Rating agencies’ assignment methodologies
Présentiel synchrone
Syllabus
Rouen (124) 09.02 • Overview of equities markets & explanation of Project Présentiel synchrone
Rouen (123) 13.03
• Rating agencies’ assignment methodologies
• Relationship between rating & probability of default
• Agencies’ ratings to internal experts-based rating systems
• Structural & reduced-form approaches to predicting
default
Présentiel synchrone
10. City Date Chapters Type
10
Rouen (123) 13.03
• Merton model & default probability & distance to default
• Z-score & linear discriminant analysis (LDA) to classify a
sample of firms by credit quality
• Logistic regression model to estimate default probability
Présentiel synchrone
Rouen (124) 15.03 • ...Bloomberg & equities markets Présentiel synchrone
Rouen (123) 26.03
• Exponential vs Poisson distributions
• Hazard rate & probability functions for default time &
conditional default probabilities…
• Unconditional & conditional default probability
Présentiel synchrone
Rouen (123) 02.04
• Risk-neutral default rates from spreads
• PROs of CDS market to estimate hazard rates
• CDS spread used to derive a hazard rate curve
• Spread risk & measurement using the mark-to-market and
spread volatility
Présentiel synchrone
…Syllabus
Rouen (123) 14.03
• Representing credit spreads
• Compute one credit spread given others when possible
• Spread ‘01
• Default risk & Bernoulli trial
Distanciel
11. City Date Chapters Type
11
Rouen (123) 09.04
• Extreme value theory (EVT) & risk management
• Peaks-over-threshold (POT) approach
Présentiel synchrone
Rouen (123) 10.04 • Generalized extreme value & POT
• Multivariate EVT for risk management
Distanciel
Rouen (124) 03.04 • ...Bloomberg & equities markets Présentiel synchrone
…Syllabus
Rouen (123) 05.04
• Bootstrap simulation & coherent risk measures
• Historical simulation using non-parametric density
estimation
Présentiel synchrone
Rouen (123) 09.04
• Age-weighted, volatility-weighted, correlation-weighted &
filtered historical simulation approaches
• PROs & CONs of non-parametric estimation methods
Présentiel synchrone
12. City Date Chapters Type
12
Rouen (123) 12.04
• Backtesting & backtesting VaR models
• Type I & Type II errors
Présentiel synchrone
Rouen (123) 15.04
• Conditional coverage in backtesting framework
• Basel rules for backtesting
Présentiel synchrone
Rouen (123) 16.04 • Case studies for Credit Risk & Market Risk Asynchrone
Rouen (124) 17.04 • Measurement of performance Présentiel synchrone
Rouen (124) 19.04 • Review exercises Présentiel synchrone
…Syllabus
13. 13
• Tools used :
• Excel
• Power Point
• Bloomberg
…Syllabus
14. Evaluation Weight Information
14
Quizzes 50% • Unannounced
Final exam (2 hours) 50% • Centre des examens (after Course 10)
…Syllabus
123
Project 40% • Groups of 3 students
Final exam (2 hours) 60% • Centre des examens (after Course 10)
124
15. 15
• You need to create a Bloomberg account
• Rouen :
• Room G127
• Access code 434211
• Reims :
• Room 1A055
• Access code C6395A
• Once you are in the front of a Bloomberg terminal, create your account
Bloomberg
19. 19
• ...France : 600 asset managers
• https://fundsmagazine.optionfinance.fr/dossiers-de-la-redaction/les-50-
societes-de-gestion-qui-comptent-selection-2022.html
BNP Paribas AM
Eleva Capital
JP Morgan AM
Lazard Frères Gestion
Pictet AM
Tikehau Capital
Allianz Global Investors
Amiral Gestion
Amundi
Aviva Investors France
AXA IM
Axiom AI
BFT IM
BNY Mellon IM
Candriam
Carmignac
Comgest
CPR AM
Crédit Mutuel AM
DNCA Finance
La Financière de
l’Echiquier
La Française
Mandarine Gestion
M&G
Mirova
Montpensier Finance
Morgan Stanley IM
Natixis IM
Oddo BHF AM
Rothschild & Co AM
Europe
DWS
Ecofi
Edmond de Rothschild
AM
Eres Gestion
Fidelity International
Gemway Assets
Generali Investment Par
tners
Groupama AM
IM Global Partner
La Banque Postale AM
Robeco
Sanso IS
Schroders
Sycomore AM
Syquant Capital
Thematics AM
Tocqueville Finance
UBS La Maison de
Gestion
Varenne Capital
Partners
Vega IM
…Asset management
21. 21
$2.4 T $2.1 T $1.1 T
• Active vs passive investment strategies àPassive management has been gaining
market share over time vs active
management
àPassive management’s share of industry
revenues < its share of AUM
…Asset management
23. 23
• Typical job description
• Asset Manager Responsibilities :
Source : https://www.betterteam.com/asset-manager-job-
description#:~:text=Asset%20managers%20trade%2C%20manage%2C%20and,increase%20asset%20value%20and%20revenue.
• Meeting with clients, determining needs & requirements, providing strategic advice & managing their assets accordingly
• Preparing risk analyses & financial & investment reports
• Creating, organizing & managing client portfolios
• Monitoring asset performance & recommending corrective measures
• Developing strategies to increase ROI and minimize risk factors & losses
• Reviewing policies & making recommendations for adjustments
• Researching relevant markets & identifying trends & patterns
• Collaborating with the AM team, company analysts & senior executives
• Liaising & negotiating with fund directors, property managers, attorneys, auditors, etc.
…Asset management
24. 24
• ...Typical job description
• Asset Manager Requirements :
Source : https://www.betterteam.com/asset-manager-job-
description#:~:text=Asset%20managers%20trade%2C%20manage%2C%20and,increase%20asset%20value%20and%20revenue.
• Degree in business, finance or related field
• Previous experience as an analyst or asset manager
• Proficiency in Microsoft Office & industry-related software
• Strong financial background with experience in financial modeling
• Strategically minded with strong analytical & problem-solving skills
• Excellent communication & interpersonal skills
• Excellent organizational & managerial skills
• Attention to detail
• Skilled negotiator
…Asset management
25. 25
• Roles
• Front-office : investment decisions & execution - relationship with brokers
• Back-office : trade reconciliation – relationship with Bank
• Middle-office :
• In between front & back !
• The way I see it : derivatives pricing, acute problems in the back-office, liaison between front & back-office
• Compliance :
• Handles relationship with Commissaires aux comptes, Autorité des marchés financiers & other third-parties
• Operational, new requirements from AMF, …
• Sales & marketing :
• Handles reporting as well
…Asset management
26. • $100 are invested at the end of 2020 :
• Portfolio manager A does very well when stock markets are booming, and poorly when markets
are falling
• Portfolio manager B does OK when stock markets are booming, and is not that bad when
markets are falling
• Which one should you choose ?
• Portfolio manager A is out of business
26
2021 2022 2023 2024 2025 2026 2027
S&P500 +20% -20% +20% -20% +20% -20% +20%
PM A +25% -25% +25% -25% +25% -25% +25%
PM B +15% -15% +15% -15% +15% -15% +15%
$100 at
12.31.27
106.17
103.00
107.41
When looking at past returns, never use the arithmetic
average à use geometric average instead !
…Asset management
28. 28
Returns (3 years)
𝛔 (volatility or standard deviation)
(3 years)
Drawdown (3 years)
…Asset management This is my business card
29. 29
Performance are NET of fees
(unlike North America)
Performances > 1 year are
cumulative (unlike N.A.)
…Asset management
30. • Challenges :
• Strategic :
• Find value !
• SRI / ESG
• Cryptos
• Big Data / Machine Learning à Power BI, Python, Jupyter, ...
• Compliance
• Tactical :
• Inflation, Ukraine, Trump in 2024...
30
…Asset management
31. 31
Trends in Asset allocation
Alternatives : 23%
Cash
3%
Fixed income
31%
Equities
43%
Real estate - Equity
9%
Real estate - Debt
3%
Private Equity
7%
Infrastructure
3%
Hedge funds
1%
Caisse de dépôt et placement du Québec -
Asset allocation 12.31.2003
32. 32
…Trends in Asset allocation
Alternatives : 45%
Main risks : poor returns
& cut in dividends
Main risks : interest
rate risk & default
Main risk : illiquidity
Fixed income
30%
Equities
25%
Real estate - Equity
12%
Private Equity
20%
Infrastructure
13%
Hedge funds
0%
Caisse de dépôt et placement du Québec -
Asset allocation 12.31.2022
33. 33
Varia
• You are at a TV show. There are 3 doors : behind one door is 1 M$ ; behind
each of the 2 other doors, there is a dog (so 2 dogs in total)
• You pick a door, and the host (he knows what's behind the doors) opens
another door with a dog (mandatory)
• He then ask : “Do you want to switch door?”
Should you?
• Yes
• No
34. Note : some of the study material comes
either from CFA Institute, Schweser or
GARP