Presentation by AIS\\’ CEO, Mr. Ramon Trias, at GARP\\’s chapter "Stress testing: new approaches for a new paradigm" (New York, May 13, 2010)
This document discusses Value at Risk (VaR), a risk measurement technique used in finance. There are three main methods to calculate VaR: the historical method, variance-covariance method, and Monte Carlo simulation. The historical method looks at past losses and assumes history will repeat. The variance-covariance method assumes returns are normally distributed. Monte Carlo simulation models future returns through hypothetical trials. All methods make assumptions that may not reflect reality and have limitations around changing distributions over time. VaR is widely used but also faces criticism for relying on untested models and giving a false sense of confidence.
This document discusses value at risk (VaR), a technique used to measure financial risk. VaR is measured by the amount of potential loss, the probability of that loss, and the time frame. There are three main analytical methods for calculating VaR: the analytical method which uses assumptions of normal distribution, the historical method which estimates risk based on past portfolio performance, and Monte Carlo simulation which generates random outcomes to analyze risk.
This document discusses Value at Risk (VaR) and related concepts over multiple learning outcomes (LOs). It introduces VaR and explains why it was widely adopted as a risk measure. It also defines how to calculate VaR for single and multiple assets, and how to convert between time periods. The document discusses assumptions of VaR calculations and reasons for using continuously compounded returns. It also addresses factors that affect portfolio risk and how to calculate VaR for linear and non-linear derivatives. Finally, it introduces cash flow at risk (CFaR) and how VaR and CFaR can be used to evaluate projects and allocate risk.
Over the past few decades investing has become increasingly complex - as such it would seem that the tools necessary to manage this risk have also increased in complexity.
Maynard argues that this might not necessarily be the case and that simple tools can help investors navigate through complexity.
En el encuentro del 18/8/11 David Mermelstein, especialista SAS, mostró metodología integrada de tratamiento de riesgos bancarios y stress testing. Además mostró ejemplos/demos de SAS Risk Dimensiones, solución SAS para manejo integrado de riesgos bancarios. (ver demos en www.youtube.com/analyticsconosur
Stress Testing A Practical Approach To The Analysis Of Systemic Stability Part1Pilar Mateo
First part of the presentation AIS used when was convocated by IMF in Washington to show their stress testing method (RDF).
-----
Primera parte de la presentación empleada por los representantes de AIS durante su comparescencia organizada por el FMI en su sede de Washington para conocer el método RDF de stress testing de AIS.
The document discusses the challenges in building effective stress testing models that meet regulatory guidelines. It outlines key things regulators look for, such as models being clearly linked to macroeconomic variables and scenario design covering all material risks. It also discusses techniques for incorporating macroeconomic factors into models through macro-to-micro modeling. The document emphasizes balancing model complexity with usability and explains how to build flexible models to address atypical stress scenarios.
This document discusses two methods for calculating Value-at-Risk (VaR): 1) Assuming a normal distribution of portfolio returns and using a GARCH model to estimate conditional volatility, and 2) A nonparametric bootstrap method. The normal distribution assumption is appropriate only during calm periods but will underestimate risk during turbulent times. The bootstrap method does not rely on distributional assumptions and better accounts for uncertainty in conditional variance dynamics to provide more accurate VaR estimates. An empirical exercise applies the two methods to the CAC40 index to demonstrate how the normal distribution method fails VaR tests during turbulence while the bootstrap method passes.
This document discusses Value at Risk (VaR), a risk measurement technique used in finance. There are three main methods to calculate VaR: the historical method, variance-covariance method, and Monte Carlo simulation. The historical method looks at past losses and assumes history will repeat. The variance-covariance method assumes returns are normally distributed. Monte Carlo simulation models future returns through hypothetical trials. All methods make assumptions that may not reflect reality and have limitations around changing distributions over time. VaR is widely used but also faces criticism for relying on untested models and giving a false sense of confidence.
This document discusses value at risk (VaR), a technique used to measure financial risk. VaR is measured by the amount of potential loss, the probability of that loss, and the time frame. There are three main analytical methods for calculating VaR: the analytical method which uses assumptions of normal distribution, the historical method which estimates risk based on past portfolio performance, and Monte Carlo simulation which generates random outcomes to analyze risk.
This document discusses Value at Risk (VaR) and related concepts over multiple learning outcomes (LOs). It introduces VaR and explains why it was widely adopted as a risk measure. It also defines how to calculate VaR for single and multiple assets, and how to convert between time periods. The document discusses assumptions of VaR calculations and reasons for using continuously compounded returns. It also addresses factors that affect portfolio risk and how to calculate VaR for linear and non-linear derivatives. Finally, it introduces cash flow at risk (CFaR) and how VaR and CFaR can be used to evaluate projects and allocate risk.
Over the past few decades investing has become increasingly complex - as such it would seem that the tools necessary to manage this risk have also increased in complexity.
Maynard argues that this might not necessarily be the case and that simple tools can help investors navigate through complexity.
En el encuentro del 18/8/11 David Mermelstein, especialista SAS, mostró metodología integrada de tratamiento de riesgos bancarios y stress testing. Además mostró ejemplos/demos de SAS Risk Dimensiones, solución SAS para manejo integrado de riesgos bancarios. (ver demos en www.youtube.com/analyticsconosur
Stress Testing A Practical Approach To The Analysis Of Systemic Stability Part1Pilar Mateo
First part of the presentation AIS used when was convocated by IMF in Washington to show their stress testing method (RDF).
-----
Primera parte de la presentación empleada por los representantes de AIS durante su comparescencia organizada por el FMI en su sede de Washington para conocer el método RDF de stress testing de AIS.
The document discusses the challenges in building effective stress testing models that meet regulatory guidelines. It outlines key things regulators look for, such as models being clearly linked to macroeconomic variables and scenario design covering all material risks. It also discusses techniques for incorporating macroeconomic factors into models through macro-to-micro modeling. The document emphasizes balancing model complexity with usability and explains how to build flexible models to address atypical stress scenarios.
This document discusses two methods for calculating Value-at-Risk (VaR): 1) Assuming a normal distribution of portfolio returns and using a GARCH model to estimate conditional volatility, and 2) A nonparametric bootstrap method. The normal distribution assumption is appropriate only during calm periods but will underestimate risk during turbulent times. The bootstrap method does not rely on distributional assumptions and better accounts for uncertainty in conditional variance dynamics to provide more accurate VaR estimates. An empirical exercise applies the two methods to the CAC40 index to demonstrate how the normal distribution method fails VaR tests during turbulence while the bootstrap method passes.
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.
Interventions required to meet business objectives from Forecasting Methods,
Quantitative & Qualitative Methods,
Forecast Accuracy , Error Reduction to
CPFR
Interventions required to meet business objectives - from Forecasting Methods,
Forecast Accuracy / Error Reduction,
Integrate – Sales Forecast / Production to undertaking a CPFR
Practical Aspects of Stochastic Modeling.pptxRon Harasym
The document provides an overview of stochastic modeling for actuaries. It defines stochastic modeling as a technique that uses random variables and simulations to model complex systems over time. The key advantages are the ability to study long-term outcomes under different scenarios and to better understand risk. Limitations include significant effort required and reliance on input assumptions. Stochastic modeling is preferred when risks are complex or path dependent. The document outlines the modeling steps and discusses concepts like the conditional tail expectation.
The document outlines various techniques for stand-alone risk analysis, including sensitivity analysis, scenario analysis, break-even analysis, simulation analysis, and decision tree analysis. It provides examples and procedures for conducting each type of analysis. Sensitivty analysis and scenario analysis are discussed in detail through examples. Simulation analysis covers defining probability distributions, dealing with correlations, and issues in application. Decision tree analysis is introduced as a tool for sequential decision making under risk.
Pillar III presentation 2 27-15 - redacted versionBenjamin Huston
This document provides an overview of a market-based indicators approach to stress testing financial institutions in the United States. It describes using a systemic risk dashboard to monitor risks, a contingent claims analysis model to estimate institutions' default probabilities, and generalized additive models to project default probabilities under stress scenarios. Historical results are also recapped. Key findings on macroeconomic contributions and inter-sector spillovers are presented. Annexes provide details on modeling methodologies.
Xiaorong Zou has over 10 years of experience in model validation and risk management. She currently works as a Senior Manager at BMO Financial Group, where she manages a team that validates market risk models. Prior to this role, she worked as a lecturer teaching mathematics and finance courses. She has a PhD in Mathematics and masters degrees in Electrical Engineering, Actuarial Science, and Applied Math.
In few years only, Supply Chain Management became one of the trendiest topic for organizations facing globalized markets. But in parallel it also remained one of the foggiest topic for managers at every level.
To develop Managers' level of understanding, let's review the main tendencies forging Supply Chains in 2017.
Model risk and validation are important processes for banks that rely on models. There are several potential sources of model risk over a model's lifecycle from data issues to changes over time that impact applicability. Effective validation ensures models are performing as intended and identifies limitations. It should include independent review and testing using quantitative and qualitative techniques on a regular basis to verify models continue to meet requirements.
Risk adjusted strategy for economic activity 2020-04-22.pdf (1)SABC News
There is early evidence that the full national lockdown imposed since 26 March 2020 has successfully limited the spread of the coronavirus. However, there are serious risks associated with lifting lockdown restrictions too soon, or in an unsystematic and disorderly manner.
SA Lockdown: Risk adjusted strategy for economic activity 2020-04-22.pdfSABC News
The document discusses a risk-adjusted strategy for easing lockdown restrictions and restarting economic activity in South Africa. It proposes implementing an alert level system with clearly defined levels of restriction that can be tightened or relaxed based on epidemiological trends. The levels would range from level 1 with low virus spread and high health system readiness to level 5 with high virus spread and low readiness. It also outlines criteria for prioritizing which sectors can resume operations, focusing on sectors with low transmission risk and severe economic impact or high economic value. The criteria are ranked from transmission risk to economic impact to value.
Sa presidency Covid-19 5-level Strategy draft documentRandolf Jorberg
This document is circulating since yesterday. It shows plans for a slow restart of economic activity for a post-lockdown South Africa and comes with a bad outlook for us restaurant operators. This document IS authentic, but it is is only one of many draft documents circulating.
"Khusela Diko has confirmed the doc circulating is from the Presidency but that it has changed significantly and will be finalised and elaborated on tomorrow night" #coronavirus #covid19 #lockdown2020 #SouthAfrica
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.
The document discusses various types of risks faced by financial institutions including market risk, liquidity risk, credit risk, and operational risk. It provides an overview of how to manage these risks through a generic risk management approach of identifying, prioritizing, classifying, quantifying, and mitigating risks. Dynamic hedging is discussed as a technique to manage risks from guarantees on investment products through regular adjustments of hedge positions.
Value-at-Risk (VaR) has been adopted as the cornerstone and commonlanguage of risk management by virtually all major financial institutions and regulators. However, this risk measure has failed to warn the market participants during the financial crisis. In this paper, we show this failure may come from the methodology that we use to calculate VaR and not necessarily for VaR measure itself. we compare two different methods for VaR calculation, 1)by assuming the normal distribution of portfolio return, 2)
by using a bootstrap method in a nonparametric framework. The Empirical exercise is implemented on CAC 40 index, and the results show us that the first method will underestimate the market risk - the failure of VaR measure occurs. Yet, the second method overcomes the shortcomings of the first method and provides results that pass the tests of VaR evaluation.
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.
The document summarizes a presentation on challenges in practical market risk management. It discusses the shift from VaR to expected shortfall measures to better assess tail risk. New regulations are making trading and banking book boundaries clearer and requiring greater emphasis on model testing, back-testing and validation. Market risk systems need to be upgraded to ensure real-time measurements and data availability so risk can be incorporated into trading decisions. Overall regulatory scrutiny and focus from senior management are driving large-scale changes in market risk management.
- The document discusses issues with current commercial practices in process control engineering and lack of agreement on how to measure the value and performance of control systems.
- It argues that the primary purpose of process control should be to maximize expected net present value profit (ENPVP) and that Clifftent, a quantitative risk management method, provides a rigorous way to measure the financial value of dynamic performance and improved control.
- Using Clifftent, the value of process control comes from three sources: optimizing setpoints, reducing dynamic variance at a setpoint, and further optimizing setpoints for the reduced-variance situation, taking into account penalties for violating process limits.
Global economic growth is finally picking up, albeit slowly, and the opportunities are there for those organizations ready to face the associated risks and challenges. The latest issue of Performance magazine delves into some of these challenges as well as the opportunities.
For further information visit: http://performance.ey.com/
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.
Interventions required to meet business objectives from Forecasting Methods,
Quantitative & Qualitative Methods,
Forecast Accuracy , Error Reduction to
CPFR
Interventions required to meet business objectives - from Forecasting Methods,
Forecast Accuracy / Error Reduction,
Integrate – Sales Forecast / Production to undertaking a CPFR
Practical Aspects of Stochastic Modeling.pptxRon Harasym
The document provides an overview of stochastic modeling for actuaries. It defines stochastic modeling as a technique that uses random variables and simulations to model complex systems over time. The key advantages are the ability to study long-term outcomes under different scenarios and to better understand risk. Limitations include significant effort required and reliance on input assumptions. Stochastic modeling is preferred when risks are complex or path dependent. The document outlines the modeling steps and discusses concepts like the conditional tail expectation.
The document outlines various techniques for stand-alone risk analysis, including sensitivity analysis, scenario analysis, break-even analysis, simulation analysis, and decision tree analysis. It provides examples and procedures for conducting each type of analysis. Sensitivty analysis and scenario analysis are discussed in detail through examples. Simulation analysis covers defining probability distributions, dealing with correlations, and issues in application. Decision tree analysis is introduced as a tool for sequential decision making under risk.
Pillar III presentation 2 27-15 - redacted versionBenjamin Huston
This document provides an overview of a market-based indicators approach to stress testing financial institutions in the United States. It describes using a systemic risk dashboard to monitor risks, a contingent claims analysis model to estimate institutions' default probabilities, and generalized additive models to project default probabilities under stress scenarios. Historical results are also recapped. Key findings on macroeconomic contributions and inter-sector spillovers are presented. Annexes provide details on modeling methodologies.
Xiaorong Zou has over 10 years of experience in model validation and risk management. She currently works as a Senior Manager at BMO Financial Group, where she manages a team that validates market risk models. Prior to this role, she worked as a lecturer teaching mathematics and finance courses. She has a PhD in Mathematics and masters degrees in Electrical Engineering, Actuarial Science, and Applied Math.
In few years only, Supply Chain Management became one of the trendiest topic for organizations facing globalized markets. But in parallel it also remained one of the foggiest topic for managers at every level.
To develop Managers' level of understanding, let's review the main tendencies forging Supply Chains in 2017.
Model risk and validation are important processes for banks that rely on models. There are several potential sources of model risk over a model's lifecycle from data issues to changes over time that impact applicability. Effective validation ensures models are performing as intended and identifies limitations. It should include independent review and testing using quantitative and qualitative techniques on a regular basis to verify models continue to meet requirements.
Risk adjusted strategy for economic activity 2020-04-22.pdf (1)SABC News
There is early evidence that the full national lockdown imposed since 26 March 2020 has successfully limited the spread of the coronavirus. However, there are serious risks associated with lifting lockdown restrictions too soon, or in an unsystematic and disorderly manner.
SA Lockdown: Risk adjusted strategy for economic activity 2020-04-22.pdfSABC News
The document discusses a risk-adjusted strategy for easing lockdown restrictions and restarting economic activity in South Africa. It proposes implementing an alert level system with clearly defined levels of restriction that can be tightened or relaxed based on epidemiological trends. The levels would range from level 1 with low virus spread and high health system readiness to level 5 with high virus spread and low readiness. It also outlines criteria for prioritizing which sectors can resume operations, focusing on sectors with low transmission risk and severe economic impact or high economic value. The criteria are ranked from transmission risk to economic impact to value.
Sa presidency Covid-19 5-level Strategy draft documentRandolf Jorberg
This document is circulating since yesterday. It shows plans for a slow restart of economic activity for a post-lockdown South Africa and comes with a bad outlook for us restaurant operators. This document IS authentic, but it is is only one of many draft documents circulating.
"Khusela Diko has confirmed the doc circulating is from the Presidency but that it has changed significantly and will be finalised and elaborated on tomorrow night" #coronavirus #covid19 #lockdown2020 #SouthAfrica
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.
The document discusses various types of risks faced by financial institutions including market risk, liquidity risk, credit risk, and operational risk. It provides an overview of how to manage these risks through a generic risk management approach of identifying, prioritizing, classifying, quantifying, and mitigating risks. Dynamic hedging is discussed as a technique to manage risks from guarantees on investment products through regular adjustments of hedge positions.
Value-at-Risk (VaR) has been adopted as the cornerstone and commonlanguage of risk management by virtually all major financial institutions and regulators. However, this risk measure has failed to warn the market participants during the financial crisis. In this paper, we show this failure may come from the methodology that we use to calculate VaR and not necessarily for VaR measure itself. we compare two different methods for VaR calculation, 1)by assuming the normal distribution of portfolio return, 2)
by using a bootstrap method in a nonparametric framework. The Empirical exercise is implemented on CAC 40 index, and the results show us that the first method will underestimate the market risk - the failure of VaR measure occurs. Yet, the second method overcomes the shortcomings of the first method and provides results that pass the tests of VaR evaluation.
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.
The document summarizes a presentation on challenges in practical market risk management. It discusses the shift from VaR to expected shortfall measures to better assess tail risk. New regulations are making trading and banking book boundaries clearer and requiring greater emphasis on model testing, back-testing and validation. Market risk systems need to be upgraded to ensure real-time measurements and data availability so risk can be incorporated into trading decisions. Overall regulatory scrutiny and focus from senior management are driving large-scale changes in market risk management.
- The document discusses issues with current commercial practices in process control engineering and lack of agreement on how to measure the value and performance of control systems.
- It argues that the primary purpose of process control should be to maximize expected net present value profit (ENPVP) and that Clifftent, a quantitative risk management method, provides a rigorous way to measure the financial value of dynamic performance and improved control.
- Using Clifftent, the value of process control comes from three sources: optimizing setpoints, reducing dynamic variance at a setpoint, and further optimizing setpoints for the reduced-variance situation, taking into account penalties for violating process limits.
Global economic growth is finally picking up, albeit slowly, and the opportunities are there for those organizations ready to face the associated risks and challenges. The latest issue of Performance magazine delves into some of these challenges as well as the opportunities.
For further information visit: http://performance.ey.com/
8. RDF Method- Risk Dynamics into the Future Scenario Asset structure Macroeconomic model Micro models (portfolios) Conditional distribution Losses given risk drivers Global Risk measures “ Cul d’olla” Integral Partial instances: GDP 4th Q 2010=xxx && Unemployment 3rd Q 2010=yyy alternative structure Information & Decision system Intrinsic Risk correction Conditioned Economic Capital CVar,Cshortfall,CUL, Risk Contribution VAR-models Segmented Micro-models Marginal distribution Consolidated Distribution
Good afternoon, Ladies and Gentlemen I would like take advantage of this moment to thank to GARP for the opportunity to giving this conference and the audience for the interest in this subject. I am sure it is going to be useful to you. In the next fifteen minutes, I am going to present some experiences and analysis around the use of stress testing to support good decisions on management and strategic plan, helping analysts and managers to improve their position when faced with an eventual crises and extending their effect to economic capital calculation, pricing assessment and reporting. I will overview the new method that supports it. At the end, we will have fifteen minutes more for questions and answers, please, keep in mind we have a short time for this complex matter, I invite you to discuss it in a more informal way over cocktail.
We will start with some reflections about models and crisis, Trying afterwards to identify some drawbacks on currents methods of calculation –Vasiceck model, Lee model, Basel II … It will be followed by a short survey of a tool designed to avoid drawbacks and mistakes Concluding with a summary approach to the future of methods like RDF-AIS
The crisis has been very vast and deep. Lehman Brothers, Stock markets crash, distrustful models, Mr. Li’s formula, government rescues, Greece… As you know, we have as players bankers, financial analysts, rocket scientist, fat cats, supervisors and neo-classic economists and everybody is blaming each other. The rating agencies are not very happy either, but in general you have to be carefully when speaking about them. Without aiming to analyze this mess, as that could be very dangerous, I believe that we should make an interior, humble and honest look to shed some light on what has failed in the battle front that we defend and, if some simplification has served other players to justify their actions, to evaluate how we can improve the calculation methods and the control of its application as an aid to take the correct decisions. A complementary note, in the other end: at the beginning of the XIX century there was in Nottingham, UK, an important social movement called “luddites” whose followers destroyed the machines to consider them guilty of the loss of jobs for many skilled textile workers. Watch out!, there are desperate neo-luddites among us.
Some biases are implied in currently used methods such as Vasicek, Basel II, Ho & Lee Formula, Merton formula: Non-measurable uncertainty components A principle relay behind the models: all events that can happen in the future, have a common origin with the past, usually is assumed a distribution of the Exponential Family (Normal, Bernoulli, Negative Binomial, Gamma, …). In the current crises, chaos were foreseen by experts and badly predicted by models. Non-symmetrical interactions among risks Risk of the same source can produce changes into variables with diverse time-lags. Moreover, correlation as a measure throughout all cycle hide the simultaneity of default in bad times. A spurious diversification can hide a non-contemporaneous dependence Non observable correlations Measuring correlation of bond’s returns can be at least feasible, but information on retail loans cannot be obtained seemingly, it means the concept of risk is equal but the way to obtain their signals are very different. Poor estimation of correlations between investments implies poor portfolio design. Missing significant idiosyncratic risk. Since the fifties, the theory of investment, the Portfolio Selection, the CAPM and the like, has been teaching us that the idiosyncratic risk vanishes with diversification, so, it can be negligible under some hypothesis. The lack of granularity (Herfindal coefficient high) mainly when produced by a mix of granular elements together with a very short number of very big investments produces fat tails loaded by the not-vanished idiosyncratic risk and, as a consequence, underestimation of capital needs and risk contribution and a false (and dangerous) measurement of solvency Lack of scalability and integration It is not easy to take into account the consequences of changing the structure of our portfolio in terms of losses distribution function using formulas that deals with the hypothesis of inelasticity of idiosyncratic risk. It can hardly be used as a tool for strategic planning if it is inelastic respect the change in portfolio volume and structure. Poor estimation of the diversification effects Mainly in low or strongly irregular granularity International Institutions can compensate the loss of credit quality operating outside the natural markets by the positive effects of diversification (if it is well evaluated) Slow answers, poor dialog user-algorithm When Montecarlo is applied, the answers need more and more processing time. Simulating different portfolio structures when faced with different scenarios, needs a fast response. Moreover, the foreseeable increase in capital regulatory ratios, will change the question “How much capital need” by “which is the best combination of investments subject to capital limitations”. A mathematical optimization of this kind, need a very fast response in each single evaluation.
In general, the risk models look for an equilibrium between expected value and variability, but in the real world these decisions are tied to the bad case. Gathering capital is another example. Solvency capital makes sense if able to cover a stressed situation, no matter related with the average thru the cycle.
A clever design on new capital and stress testing concepts can help improving the former frame: Not-measurable uncertainty components Expert or extra-model Scenarios carries the uncertainty No model can predict chaos, no expert can draw a detailed scenario and the path to reach it. Merging models and extra-models scenarios gives risk and uncertainty integrated. Not-symmetrical interactions among risks Multi-period and multi-equation models, macro and micro covers identifiable relations Effect of Macroeconomic variables on losses at each portfolio and extra-model scenarios, both defined with the most representative variables at some data, left few chances to be complete and coincident, so, a model embedded with the temporal structure of them will be necessary. VAR models fits well, integration with scenarios can be treated as a marginal distribution. Micro economic models –PD, LGD, EAD …- can be produced with current functions – Logistic, Logarithmic, Merton, … Not observable correlations in extreme events Correlations can be obtained as an output: Conditioned to a scene Correlations conditioned to events of stressed scenarios can offer more information (even being loosely defined) that correlations along the complete cycle. Moreover, instead of correlations on single risk drivers, total losses correlations is more useful. This Matrix Correlation should be obtained as an output, not as an input to the calculations. Missing significant idiosyncratic risk Poor estimation of the diversification effects Control of the idiosyncratic risk as well of the shared one gives a realistic estimation of fat tails Effect of the residual risk can be calculated Conditioned to a scene Lack of scalability and integration Taking into account non-lineal effects of volume on probability losses distribution Slow answers, poor dialog user-algorithm One to five seconds per simulation on a medium sized retail bank
To define an scenario means to fix some variables in some periods to the guessed figures given by the expert and optionally the variance around them. The modified VAR model, estimates the rest of variables with the most probable values and their correlation matrix, both of them conditioned to the economic scenario. As a consequence, after this step we have the PDF of all the variables by all the periods in the horizon of analysis. RDF has been designed to integrate this information with the micro models, the output is the losses distribution of probability, from which it can be obtained VaR, Shortfall, Expected Losses, Unexpected Losses and required Capital. All this risk measures are calculated conditioned to the scenario defined in the first step.
We observe that losses and Point In Time VaR have a cyclical variation with a fixed capital structure. In the stress periods Losses can be more greater than the Long Run VaR. The structure of whole portfolio is decomposed in 7 portfolios. An optimization of structure between portfolios (OPTIM) is made to reduce the VaR in streed periods. Currently, RDF is used by Bancaja to Re-structure of their Credit-Portfolio Price management of Real State assets Commercial planning Strategic Investments Plan Other case: Financiera Rural , a big Mexican Development Bank Credit Insurance Capital Calculation for Development targets
Credit risk analysis Concentration / diversification Limits by debtor and portfolio Marginal risk for debtor and portfolio Risk premium and RAROC by debtor and portfolio Risk management Solvency and stress Provisions forecasting Portfolio structure planning Securitization valuation Admission & follow-up Scoring limits based on objective PD for the next 3 years Back testing Strategic Planning “ Bottom-up” estimation objectives by branch, subject to macroeconomic situation