This document discusses trends in economic capital modeling from 2005 to the present. It outlines phases of development including initial analytics, workflow/governance improvements, and advanced reporting. Future areas of focus are described like faster calculations, improved credit risk modeling, and incorporating "trust metrics" into risk management dashboards. Methods like curve fitting and least squares Monte Carlo for proxying liabilities are also summarized. The benefits of using more scenarios are presented, along with addressing regulator feedback on credit risk modeling.
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Quantifying reserve risk with worked examples for modeling for IFRS17 Risk Adjustment and Solvency 2 Risk Margin. In this presentation, we cover the modeling of Risk Adjustment of IFRS17 and compare it to Risk Margin of Solvency 2. We also cover strategic considerations to quantifying reserve risk.
We review basic reserving methodologies for reserving general insurance like lag analysis and chain ladder. We then move forward to consider multiple stochastic loss reserving models in detail and show how they uncover more insights than basic reserving models.
Operational Risk Loss Forecasting Model for Stress TestingCRISIL Limited
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EAD Parameter : A stochastic way to model the Credit Conversion FactorGenest Benoit
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Our paper will focus on two types of model: the Ornstein Uhlenbeck (OU) model – part of ARMA model types – and the Geometric Brownian Movement (GBM) model. First, we will describe, then implement and calibrate each model to ensure relevance and robustness of our results. Then, we will focus on GBM model to model CCF.
Performing Strategic Risk Management with simulation modelsWeibull AS
“How can you be better than us to understand our business risk?"
This is a question we often hear and the simple answer is that we don’t! But by using our methods and models we can utilize your knowledge in such a way that it can be systematically measured and accumulated throughout the business and be presented in easy to understand graphs to the management and board.
The main reason for this lies in how we can treat uncertainties 1 in the variables and in the ability to handle uncertainties stemming from variables from different departments simultaneously.
At The Boeing Center's 13th Annual Rosenblatt Memorial Lecture, Mike Pinedo, the Julius Schlesinger Professor of Operations Management in the Department of Information, Operations and Management Sciences at New York University's Leonard N. Stern School of Business, shared his insights about operational risk management in financial services.
Drawing from decades of experience in industrial systems development, Professor Pinedo spoke about operational failures and their causes, as well as the management of costs and operational risk in financial services. He also talked about his experience in measuring operational risk, including identifying key risk indicators and running multi-factor analyses.
---
Check out @theboeingcenter on social media for more supply chain and operational excellence digital content.
Building Risk Management into Enterprise Architectureiasaglobal
By Bill Estrem, MN Chapter Conference 11/15/2013 Get Lucky: Building Risk Management into Enterprise Architecture This presentation will examine how enterprise architects can apply risk management capabilities to the development and operation of an enterprise architecture. The approach incorporates the TOGAF 9 Risk Management framework along with other risk management methods. In particular, the approach will focus on the The Open Group Risk Management Taxonomy and Risk Assessment standard. Bill Estrem - President of Metaplexity Associates LLC
In-spite of large volumes of Contingent Credit Lines (CCL) in all commercial banks, the paucity of Exposure at Default (EAD) models, unsuitability of external data and inconsistent internal data with partial draw-downs has been a major challenge for risk managers as well as regulators in for managing CCL portfolios. This current paper is an attempt to build an easy to implement, pragmatic and parsimonious yet accurate model to determine the exposure distribution of a CCL portfolio. Each of the credit line in a portfolio is modeled as a portfolio of large number of option instruments which can be exercised by the borrower, determining the level of usage. Using an algorithm similar to basic the CreditRisk+ and Fourier Transforms we arrive at a portfolio level probability distribution of usage. We perform a simulation experiment using data from Moody\'s Default Risk Service, historical draw-down rates estimated from the history of defaulted CCLs and a current rated portfolio of such.
How to Coordinate Regulation (Basel III), Market and Business Strategy in the...AIS
AIS-GARP Madrid chapter 2013 - Optimize or die: How to Coordinate Regulation (Basel III), Market and Business Strategy in the Planning of a Financial Institution
Study ROI of Supply Chain Risk Management (riskmethods Nov 2014)Heiko Schwarz
“ROI of Supply Chain Risk Management” The study provides detailed information on which individual potentials - that can be evaluated in monetary terms - provide the source for calculating ROI of SCRM. Learn more download the brand new study about SCRM!
Building Your Employer Brand with Social MediaLuanWise
Presented at The Global HR Summit, 6th June 2024
In this keynote, Luan Wise will provide invaluable insights to elevate your employer brand on social media platforms including LinkedIn, Facebook, Instagram, X (formerly Twitter) and TikTok. You'll learn how compelling content can authentically showcase your company culture, values, and employee experiences to support your talent acquisition and retention objectives. Additionally, you'll understand the power of employee advocacy to amplify reach and engagement – helping to position your organization as an employer of choice in today's competitive talent landscape.
Implicitly or explicitly all competing businesses employ a strategy to select a mix
of marketing resources. Formulating such competitive strategies fundamentally
involves recognizing relationships between elements of the marketing mix (e.g.,
price and product quality), as well as assessing competitive and market conditions
(i.e., industry structure in the language of economics).
More Related Content
Similar to Trends in Economic Capital Modeling: Curt Burmeister, Head of Buy-Side Products, Risk Analytics, Algorithmics, IBM
We review basic reserving methodologies for reserving general insurance like lag analysis and chain ladder. We then move forward to consider multiple stochastic loss reserving models in detail and show how they uncover more insights than basic reserving models.
Operational Risk Loss Forecasting Model for Stress TestingCRISIL Limited
Presentation on ‘Operational Risk Loss Forecasting Model for Stress Testing – A Three-Stage Approach’ made by Dr. James Lu, Director, Risk & Analytics, CRISIL Global Research & Analytics (GR&A) at The 17th Annual OpRisk North America 2015, New York
EAD Parameter : A stochastic way to model the Credit Conversion FactorGenest Benoit
This white paper aims at estimating credit risk by modelling the Credit Conversion Factor (CCF) parameter related to the Exposure-at-Default (EAD). It has been decided to perform the estimation thanks to stochastic processes instead of usual statistical methodologies (such as classification tree or GLM).
Our paper will focus on two types of model: the Ornstein Uhlenbeck (OU) model – part of ARMA model types – and the Geometric Brownian Movement (GBM) model. First, we will describe, then implement and calibrate each model to ensure relevance and robustness of our results. Then, we will focus on GBM model to model CCF.
Performing Strategic Risk Management with simulation modelsWeibull AS
“How can you be better than us to understand our business risk?"
This is a question we often hear and the simple answer is that we don’t! But by using our methods and models we can utilize your knowledge in such a way that it can be systematically measured and accumulated throughout the business and be presented in easy to understand graphs to the management and board.
The main reason for this lies in how we can treat uncertainties 1 in the variables and in the ability to handle uncertainties stemming from variables from different departments simultaneously.
At The Boeing Center's 13th Annual Rosenblatt Memorial Lecture, Mike Pinedo, the Julius Schlesinger Professor of Operations Management in the Department of Information, Operations and Management Sciences at New York University's Leonard N. Stern School of Business, shared his insights about operational risk management in financial services.
Drawing from decades of experience in industrial systems development, Professor Pinedo spoke about operational failures and their causes, as well as the management of costs and operational risk in financial services. He also talked about his experience in measuring operational risk, including identifying key risk indicators and running multi-factor analyses.
---
Check out @theboeingcenter on social media for more supply chain and operational excellence digital content.
Building Risk Management into Enterprise Architectureiasaglobal
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In-spite of large volumes of Contingent Credit Lines (CCL) in all commercial banks, the paucity of Exposure at Default (EAD) models, unsuitability of external data and inconsistent internal data with partial draw-downs has been a major challenge for risk managers as well as regulators in for managing CCL portfolios. This current paper is an attempt to build an easy to implement, pragmatic and parsimonious yet accurate model to determine the exposure distribution of a CCL portfolio. Each of the credit line in a portfolio is modeled as a portfolio of large number of option instruments which can be exercised by the borrower, determining the level of usage. Using an algorithm similar to basic the CreditRisk+ and Fourier Transforms we arrive at a portfolio level probability distribution of usage. We perform a simulation experiment using data from Moody\'s Default Risk Service, historical draw-down rates estimated from the history of defaulted CCLs and a current rated portfolio of such.
How to Coordinate Regulation (Basel III), Market and Business Strategy in the...AIS
AIS-GARP Madrid chapter 2013 - Optimize or die: How to Coordinate Regulation (Basel III), Market and Business Strategy in the Planning of a Financial Institution
Study ROI of Supply Chain Risk Management (riskmethods Nov 2014)Heiko Schwarz
“ROI of Supply Chain Risk Management” The study provides detailed information on which individual potentials - that can be evaluated in monetary terms - provide the source for calculating ROI of SCRM. Learn more download the brand new study about SCRM!
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In this keynote, Luan Wise will provide invaluable insights to elevate your employer brand on social media platforms including LinkedIn, Facebook, Instagram, X (formerly Twitter) and TikTok. You'll learn how compelling content can authentically showcase your company culture, values, and employee experiences to support your talent acquisition and retention objectives. Additionally, you'll understand the power of employee advocacy to amplify reach and engagement – helping to position your organization as an employer of choice in today's competitive talent landscape.
Implicitly or explicitly all competing businesses employ a strategy to select a mix
of marketing resources. Formulating such competitive strategies fundamentally
involves recognizing relationships between elements of the marketing mix (e.g.,
price and product quality), as well as assessing competitive and market conditions
(i.e., industry structure in the language of economics).
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What might I learn?
A way to engage all in creating Inclusive Excellence. Lessons from the US military and their parallels to the story of Harry Potter. How belt systems and CI teams can destroy inclusive practices. How leadership language invites people to the party. There are three things leaders can do to engage everyone every day: maximizing psychological safety to create environments where folks learn, contribute, and challenge the status quo.
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Personal Brand Statement:
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