The following provides a quick summary pertaining to ISO compliance:<\/p>\n
- \n
- The IRM methodology we employ is in line with ISO 31000:2009 Clauses 2.3 and 2.8 requiring a risk management process (Figure 1), as well as Clause 5 (5.4.2 requiring risk identification where we use Tornado analysis and scenario analysis; 5.4.3. requiring quantitative risk analysis where we apply Monte Carlo risk simulations; 5.4.4 where existing Excel\u2010based evaluation models are used and overlaid with IRM methodologies such as simulations; etc.). See Modeling Risk, 3rd Edition\u2019s Chapter 1 for details on the IRM methodology.<\/li>\n
<\/p>\n
- ISO 31000:2009 Clause 5.4.4 looks at the risk tolerance levels and comparing various risk levels in a portfolio optimization and efficient frontier analysis employed in our IRM methodology (Figure 2). See Modeling Risk, 3rd Edition\u2019s Chapters 10 and 11 for optimization and efficient frontier modeling.<\/li>\n
<\/p>\n
- Figure 3 shows quantified consequences and the likelihoods (probabilities and confidence levels) of potential events that can occur using simulations, as required in ISO 31000:2009 Clauses 2.1 and 5.4.3.<\/li>\n
<\/p>\n
- ISO 31000:2009 Clause 5.4.3 requires viewing the analysis from various stakeholders, multiple consequences, and multiple objectives to develop a combined level of risk. This perspective is achieved through a multicriteria optimization and efficient frontier analysis (Figure 4) in the IRM process. See Modeling Risk, 3rd Edition\u2019s Chapters 10 and 11 for optimization and efficient frontier modeling.<\/li>\n
<\/p>\n
- ISO 31000:2009 Clause 3F requires that historical data and experience as well as stakeholder feedback and observation coupled with expert judgment be used to forecast future risk events. The IRM process employs a family of 16 forecasting methods (Figure 5 shows an example of the ARIMA model) coupled with risk simulations with high fidelity to determine the best goodness\u2010of\u2010fit when historical data exists, or using subject matter expert estimates and stakeholder assumptions, we can apply the Delphi method and custom distribution to run\u00a0risk simulations on the forecasts. See Modeling Risk, 3rd Edition\u2019s Chapters 8 and 9 for forecast methods and analytical details.<\/li>\n
<\/p>\n
- ISO 31000:2009 Clauses 3C, 5.4.3, 5.5, and 5.5.2 require risk evaluations on risk treatments,\u00a0options to execute when there are different types of risks, and selecting and implementing\u00a0various risk treatment strategic options that are not solely reliant on economics. The IRM\u2019s\u00a0strategic real options methodology allows users to model multiple path\u2010independent and\u00a0path\u2010dependent implementation strategies or alternate courses of action that are generated\u00a0to mitigate downside risks and take advantage of upside potentials (Figure 6). See Modeling\u00a0Risk, 3rd Edition\u2019s Chapters 12 and 13 for details on real options analysis modeling techniques.<\/li>\n
<\/p>\n
- Figure 7 illustrates how ISO 31000:2009 Clauses 3D, 3E, and 5.4.3 are satisfied using the IRM
\nprocess of probability distribution fitting of uncertain variables and how their interdependencies (correlations) are executed.<\/li>\n<\/p>\n
- Risk controls are required in ISO 31000:2009 Clauses 2.26, 4.43, and 5.4.3 (Figure 8). The control charts and Risk Effectiveness calculations in PEAT ERM help decision makers identify if a particular risk mitigation strategy and response that was enacted had sufficiently and statistically significantly affected the outcomes of future risk states.<\/li>\n
<\/p>\n
- Scenarios, cascading, and cumulative effects (consequences) are also the focus of ISO 31000:2009 Clause 5.4.2. The IRM method employs Tornado analysis, scenario analysis, dynamic sensitivity analysis, and risk simulations (Figure 9) to identify which input(s) have the highest impact on the organization\u2019s risks and model their impacts on the total risks of the organization.<\/li>\n
<\/p>\n
- ISO 31000:2009 Clause 5.2 requires proper communication of risk exposures and consequences, and an understanding of the basis and reasons of each risk. The PEAT ERM Risk Dashboards provide details and insights for a better understanding of the issues governing each of the risk issues in an organization (Figure 10).<\/li>\n
\n<\/ul>\n
\n![\"Page-3-Image-1\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-3-Image-1.jpg\")
FIGURE 1 ISO 31000:2009\u2014IRM.<\/center>\n<\/div>\n <\/p>\n
\n![\"Page-4-Image-2\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-4-Image-2.jpg\")
FIGURE 2 ISO 31000:2009\u2014risk tolerance.<\/center>\n<\/div>\n <\/p>\n
\n![\"Page-5-Image-3\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-5-Image-3.jpg\")
FIGURE 3 ISO 31000:2009\u2014consequences and likelihood.<\/center>\n<\/div>\n <\/p>\n
\n![\"Page-6-Image-4\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-6-Image-4.jpg\")
FIGURE 4 ISO 31000:2009\u2014multiple stakeholder objectives and consequences.<\/center>\n<\/div>\n <\/p>\n
\n![\"Page-7-Image-5\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-7-Image-5.jpg\")
FIGURE 5 ISO 31000:2009\u2014historical data and future forward forecast.<\/center>\n<\/div>\n <\/p>\n
\n![\"Page-8-Image-6\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-8-Image-6.jpg\")
FIGURE 6 ISO 31000:2009\u2014multiple options, strategies, and alternatives.<\/center>\n<\/div>\n <\/p>\n
\n![\"Page-9-Image-7\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-9-Image-7.jpg\")
FIGURE 7 ISO 31000:2009 structured approach, fitting, and correlations.<\/center>\n<\/div>\n \n![\"Page-10-Image-8\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-10-Image-8.jpg\")
FIGURE 8 ISO 31000:2009\u2014risk control efficiency and effectiveness.<\/center>\n<\/div>\n <\/p>\n
\n![\"Page-11-Image-9\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-11-Image-9.jpg\")
FIGURE 9 ISO 31000:2009\u2014consequences, cascades, and scenarios.<\/center>\n<\/div>\n <\/p>\n
\n![\"Page-12-Image-10\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-12-Image-10.jpg\")
FIGURE 10 ISO 31000:2009\u2014communication and consultation.<\/center>\n<\/div>\n <\/p>\n
Compliance with Basel II and Basel III Regulatory Requirements<\/strong><\/p>\n
The following provides a summary of Basel II and Basel III compliance when using the IRM methodology:<\/p>\n
- \n
- Figure 11 shows Monte Carlo risk simulations applied to determine confidence levels, percentiles,
\nand probabilities of occurrence using historically fitted data or forecast expectations. These methods are in line with Basel II and Basel III requirements Sections 16 and 161 concerning the use of historical simulations, Monte Carlo simulations, and 99th percentile confidence intervals. See Modeling Risk, 3rd Edition\u2019s Chapters 5 and 6 for details on simulations and data fitting techniques.<\/li>\n<\/p>\n
- Figure 12 shows a correlated simulation of a portfolio of assets and liabilities, where asset returns
\nare correlated against one another in a portfolio and optimization routines were run on the simulated results. These processes provide compliance with Basel II and Basel III requirements Sections 178, 232, and 527(f) involving correlations, Value at Risk or VaR models, portfolios of segments, and pooled exposures (assets and liabilities). See Modeling Risk, 3rd Edition\u2019s Chapter 5 for correlated simulations and Chapter 7\u2019s case study on Basel II and Basel III Credit, Market, Operational, and Liquidity Risks with Asset Liability Management for details on how VaR models are computed based on historical simulation results.<\/li>\n<\/p>\n
- Figure 13 shows Value at Risk percentile and confidence calculations using structural models and
\nsimulation results that are in line with Basel II and Basel III requirements Sections 179, 527(c), and
\n527(f). As noted above, see Modeling Risk, 3rd Edition\u2019s Chapter 7\u2019s case study for details on how VaR models are computed based on historical simulation results.<\/li>\n<\/p>\n
- Figure 14 shows the computations of probability of default (PD) as required in the Basel Accords,
\nspecifically Basel II and Basel III Section 733 and Annex 2\u2019s Section 16. PD can be computed using structural models or based on historical data through running basic ratios to more advanced binary logistic models. Modeling Risk, 3rd Edition\u2019s Chapter 7\u2019s case study as well as Chapter 14\u2019s Credit and Market Risk case study provide more insights into how PD can be computed using these various methods.<\/li>\n<\/p>\n
- Figure 15 shows the simulation and generation of interest rate yield curves using Risk Simulator
\nand Modeling Toolkit models. These methods are in line with Basel II and Basel III requirements Section 763 requiring the analysis of interest rate fluctuations and interest rate shocks.<\/li>\n<\/p>\n
- Figure 16 shows additional models for volatile interest rate, financial markets, and other liquid
\ninstruments\u2019 instantaneous shocks using Risk Simulator\u2019s stochastic process models. These analyses conform to Basel II and Basel III requirements Sections 155, 527(a), and 527(b).<\/li>\n<\/p>\n
- Figure 17 shows several forecast models with high predictive and analytical power, which is a part
\nof the Risk Simulator family of forecast methods. Such modeling provides compliance with Basel II and Basel III requirements Section 417 requiring models of good predictive power.<\/li>\n<\/p>\n
- Figure 18 shows the list of financial and credit models available in the ROV Modeling Toolkit and
\nROV Real Options SLS software applications. These models conform to Basel II and Basel III requirements Sections 112, 203, and 527(e) requiring the ability to value over\u2010the\u2010counter (OTC) derivatives, nonlinear equity derivatives, convertibles, hedges, and embedded options.<\/li>\n<\/p>\n
- Figure 19 shows the modeling of foreign exchange instruments and hedges to determine the efficacy
\nand effectiveness of foreign exchange hedging vehicles and their impact on valuation, portfolio profitability, and VaR, in line with Basel II and Basel III Sections 131 and 155 requiring the analysis of different currencies, correlations, volatility, and hedges.<\/li>\n<\/p>\n
- Figure 20 shows the option\u2010adjusted spread (OAS), credit default swaps (CDS), and credit spread
\noptions (CSO) models in ROV Modeling Toolkit. These models provide compliance with Basel II and Basel III requirements Sections 140 and 713 pertaining to modeling and valuing credit derivatives and credit hedges.<\/li>\n\n<\/ul>\n
\n![\"Page-14-Image-11\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-14-Image-11.jpg\")
FIGURE 11 Basel II\/III confidence levels, Monte Carlo simulations, and credit risk.<\/center>\n<\/div>\n <\/p>\n
\n![\"Page-15-Image-12\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-15-Image-12.jpg\")
FIGURE 12 Basel II\/III correlated portfolios and correlated simulations.<\/center>\n<\/div>\n <\/p>\n
\n![\"Page-16-Image-13\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-16-Image-13.jpg\")
FIGURE 13 Basel II\/III Value at Risk and percentiles.<\/center>\n<\/div>\n <\/p>\n
\n![\"Page-17-Image-14\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-17-Image-14.jpg\")
FIGURE 14 Basel II\/III credit risk analysis.<\/center>\n<\/div>\n <\/p>\n
\n![\"Page-18-Image-15\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-18-Image-15.jpg\")
FIGURE 15 Basel II\/III interest rate risk and market shocks<\/center>\n<\/div>\n <\/p>\n
\n![\"Page-19-Image-16\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-19-Image-16.jpg\")
FIGURE 16 Basel II\/III volatility and adverse instantaneous shocks.<\/center>\n<\/div>\n <\/p>\n
\n![\"Page-20-Image-17\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-20-Image-17.jpg\")
FIGURE 17 Basel II\/III forecast models with strong predictive power.<\/center>\n<\/div>\n <\/p>\n
\n![\"Page-21-Image-18\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-21-Image-18.jpg\")
FIGURE 18 Basel II\/III modeling OTC derivatives and exotic convertibles.<\/center>\n<\/div>\n <\/p>\n
\n![\"Page-22-Image-19\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-22-Image-19.jpg\")
FIGURE 19 Basel II\/III modeling foreign exchange fluctuations<\/center>\n<\/div>\n <\/p>\n
\n![\"Page-23-Image-20\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-23-Image-20.jpg\")
FIGURE 20 Basel II\/III credit derivatives and hedging.<\/center>\n<\/div>\n <\/p>\n
Compliance with COSO Integrated ERM Framework<\/strong><\/p>\n
The following provides a quick summary of COSO Integrated ERM Framework compliance when using the IRM methodology:<\/p>\n
- \n
- Figure 21 (16.45) shows the PEAT ERM module\u2019s Risk Register tab where mitigation costs and benefits (gross risks reduced to residual risk levels), likelihood and impact measures, and spreads with varying precision levels ready for Monte Carlo risk simulation are situated, in compliance with COSO ERM Framework Sections 5 & 6<\/li>\n
<\/p>\n
- Figure 22 (16.46) shows the PEAT ERM module where the likelihood and impact within a risk map is generated, in compliance with COSO AT\/Exhibit 5.13.<\/li>\n
<\/p>\n
- Figure 23 (16.47) shows compliance with COSO AT\/Exhibit 6.5 and COSO ERM Integrated Framework Section 6, where entity\u2010wide portfolio and business unit, department, and functional areas\u2019 gross and residual risks are computed.<\/li>\n
<\/p>\n
- Figure 24 (16.48) continues by showing a sample of the Risk Dashboard reports also in compliance with COSO AT\/Exhibit 6.5 and COSO ERM Integrated Framework Section 6, where entity\u2010wide portfolio and business unit, department, and functional areas\u2019 gross and residual risks are computed and compared against each other.<\/li>\n
<\/p>\n
- Figure 25 (16.49) shows the PEAT DCF module\u2019s efficient frontier model, consistent with COSO
\nAT\/Exhibit 3.7 requiring an analysis of the capital investment in relation to the returns within a diversified (optimized) portfolio.<\/li>\n<\/p>\n
- Figure 26 (16.50) shows the PEAT ERM and DCF modules\u2019 simulated results, where Value at Risk,
\npercentiles, and statistical probabilities can be obtained, in compliance with COSO AT\/Exhibit 5.5 requiring a range of outcomes based on distributional assumptions, and COSO ERM Integrated Framework Exhibit 5.2 requiring historical or simulated outcomes of future behaviors under probabilistic models.<\/li>\n<\/p>\n
- Figure 27 (16.51) shows compliance with COSO AT\/Exhibit 3.1 requiring the use of scenario
\nmodeling and stress testing.<\/li>\n<\/p>\n
- Figure 28 (16.52) shows the CMOL module in PEAT where scenario analysis, stress testing, and
\ngap analysis are performed, in compliance with COSO AT\/Exhibit 5.10, to complement probabilistic models.<\/li>\n<\/p>\n
- Figure 29 (16.53) shows compliance with COSO AT\/Exhibits 5.8 & 5.9 requiring the modeling of
\noperational and credit loss distributions with back\u2010testing or historical simulation, sensitivity analysis, and Value at Risk calculations.<\/li>\n\n<\/ul>\n
\n![\"Page-25-Image-21\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-25-Image-21.jpg\")
FIGURE 21 PEAT ERM and COSO Integrated Framework.<\/center>\n<\/div>\n <\/p>\n
\n![\"Page-26-Image-22\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-26-Image-22.jpg\")
FIGURE 22 PEAT ERM heat map and risk matrix.<\/center>\n<\/div>\n <\/p>\n
\n![\"Page-27-Image-23\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-27-Image-23.jpg\")
FIGURE 23 PEAT ERM portfolio and corporate view or residual risk.<\/center>\n<\/div>\n <\/p>\n
\n![\"Page-28-Image-24\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-28-Image-24.jpg\")
FIGURE 24 PEAT ERM portfolio, business unit, department, function view<\/center>\n<\/div>\n <\/p>\n
\n![\"Page-29-Image-25\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-29-Image-25.jpg\")
FIGURE 25 PEAT DCF module featuring capital versus returns efficient frontier<\/center>\n<\/div>\n <\/p>\n
\n![\"Page-30-Image-26\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-30-Image-26.jpg\")
FIGURE 26 PEAT ERM & DCF modules with risk simulation results with Value at Risk<\/center>\n<\/div>\n <\/p>\n
\n![\"Page-31-Image-27\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-31-Image-27.jpg\")
FIGURE 27 PEAT ERM & DCF modules with scenario analysis and heat map regions<\/center>\n<\/div>\n <\/p>\n
\n![\"Page-32-Image-28\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-32-Image-28.jpg\")
FIGURE 28 CMOL module with scenario analysis and stress testing<\/center>\n<\/div>\n <\/p>\n
\n![\"Page-33-Image-29\"](\"http:\/\/rovdownloads.com\/blog\/wp-content\/uploads\/2015\/03\/Page-33-Image-29.jpg\")
FIGURE 29 CMOL module with historical simulation (back\u2010testing) and Value at Risk<\/center>\n<\/div>\n <\/p>\n
- Figure 21 (16.45) shows the PEAT ERM module\u2019s Risk Register tab where mitigation costs and benefits (gross risks reduced to residual risk levels), likelihood and impact measures, and spreads with varying precision levels ready for Monte Carlo risk simulation are situated, in compliance with COSO ERM Framework Sections 5 & 6<\/li>\n
- Figure 11 shows Monte Carlo risk simulations applied to determine confidence levels, percentiles,
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