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CQRM Accreditation


Certified in Quantitative Risk Management - CQRM
The IIPER-CQRM is an international certification awarded by the IIPER
IIPER-CQRM certification is accredited by the US National Certification Commission in collaboration with many international organizations.

It is aimed at managers, directors, professionals, analysts, and scholars interested in acquiring up-to-date and practical knowledge in risk management from a quantitative approach to measure risk and make informed decisions.

The IIPER is a member of the AACSB (Association for the Advancement of Collegiate Schools of Business), one of the largest accreditation agencies of business schools worldwide.

The CQRM can be taken online or face-to-face, in-company or at accreditation centres. Prof. Dr. Johnathan Mun and other global experts teach advanced topics and practical applications of risk management. Upon completion, a validation of knowledge (final exam) will be conducted to obtain the professional CQRM designation.

By participating in the certification, attendees will have elements to analyse and interpret data for risk measurement, understand the results obtained and suggest and make decisions based on the Monte Carlo risk simulations, statistics and econometric analysis, optimization, and real options applicable to their projects or investments.

IIPER-CQRM is an international certification awarded by the IIPER
Internationally as a Quantitative Risk Manager (CQRM-IIPER)

Why attend this certification?

  • To be certified internationally as a Quantitative Risk Manager (CQRM-IIPER).
  • The opportunity to learn from world experts who have outstanding credentials and extensive practical experience.
  • Understand how to make more informed decisions in times of uncertainty and achieve better business outcomes.
  • Learn about the latest theoretical approaches and practical applications for risk analysis and management.
  • Update and immerse yourself in techniques that allow you to understand the past and the present and more accurately forecast the future.
  • To model industry-specific problems and implement risk analysis using Risk Simulator, Real Options SLS, and PEAT tools, capable of analysing large volumes of information and working with the latest implementation of risk management analytics.

Topics & Framework

Chapter 1: Introduction to the Training and What to Expect
Chapter 2: How Are Business Decisions Made?
Chapter 3: What Is Risk and Why Should Risk be Considered?
Chapter 4: Overview of Risk Analysis Software Applications

Chapter 1: Overview of Risk Simulator Software
Chapter 2: Profiling, Assumptions, Forecasts, and Running Simulations
Chapter 3: Interpreting the Forecast Statistics
Chapter 4: Simulation Run Preferences and Seed Values
Chapter 5: Running Reports, Saving and Extracting Simulation Data

Chapter 1: Correlating and Truncating Distributions
Chapter 2: Alternate Parameters
Chapter 3: Multidimensional Simulations
Chapter 4: Distributional Fitting
Chapter 5: Due Diligence and Pitfalls in Simulation

Chapter 1: Static Tornado and Spider Charts
Chapter 2: Dynamic Sensitivity Analysis and Scenario Analysis
Chapter 3: Hypothesis Test on Different Distributions
Chapter 4: Nonparametric Bootstrap Simulation

Chapter 1: Introduction to Optimisation
Chapter 2: Continuous Optimisation
Chapter 3: Integer Optimisation

Chapter 1: Overview of Forecasting Techniques and Data Types
Chapter 2: Forecasting Without Data
Chapter 3: Time-Series Analysis Forecasting
Chapter 4: Nonlinear Extrapolation
Chapter 5: Multivariate Linear and Nonlinear Regression Analysis
Chapter 6: Stochastic Processes
Chapter 7: Advanced Forecasting: Box-Jenkins ARIMA and Auto ARIMA,
GARCH, J-Curve, S-Curves, Markov Chains, Data Diagnostics, Statistical
Properties, Basic Econometrics

Chapter 1: Real Options: What, Where, Who, When, How, and Why?
Chapter 2: Sample Applied Business Cases
Chapter 3: Overview of Different Options Valuation Techniques
Chapter 4: Risk-Neutral Probability Technique
Chapter 5: Solving a Basic European and American Call Option
Chapter 6: Using Excel to Solve a Basic European and American Call Option
Chapter 7: Abandonment, Expansion, Contraction, and Chooser Options

Chapter 1: Overview of the Different SLS Modules and Volatility Estimates
Chapter 2: Volatility Estimates
Chapter 3: Options with Changing Inputs and Customised Exotic Options
Chapter 4: MSLS: Multiple Sequential Compound Options
Chapter 5: MNLS: Solving Mean-Reverting, Jump-Diffusion, and Dual-Asset
Rainbow Options using Trinomial, Quadranomial, and Pentanomial Lattices
Chapter 6: Framing Real Options—Structuring the Problem
Chapter 7: The Next Steps…


Project Management Institute (PMI)

Project Management Institute (PMI):
30 PDU Credits for PMP Certification Holders; Certified PfMP, PgMP, PMI-ACP, PMI-PBA, PMI-SP, and RMP are awarded 30 Credits

Institute of Chartered Financial Analysts (ICFA)

Institute of Chartered Financial Analysts (ICFA):
39 Continuing Education CE Credits.

Energy Institute (EI)

Energy Institute (EI):
30 points for Continuing Professional Development.

American Institute of CPA (AICPA):
Specialized Knowledge Area Group Live for 39 CPE Credits.

Institution of Chemical Engineers (IChemE)

Institution of Chemical Engineers (IChemE):
30 credits for Continuous Professional Development.

Institute of Risk Management (IRM)

Institute of Risk Management (IRM):
30 credits for Continuous Professional Development.

Professional Development

The CQRM-IIPER is approved by the Project Management Institute (PMI) and other professional bodies where participants can obtain between 30 to 45 PDUs, CPDs and CEs (continuing education credits) for the CQRM training and obtaining their certification.

"Unleash your Potential in Quantitative Risk Management, Strategic Real Options and Decision Analytics.

Application of CQRM Knowledge in your industry

Corporate and Industry

Optimize and diversify the risk of an investment portfolio to maximize the financial profitability of the projects (VAN, TIR, ROI, RAROC) and the results of the investment. Create analysis of alternatives for risk reduction and improvement of growth opportunities, acquisitions, diversification, Joint Venture, outsourcing, project schedule, and project cost risk. Evaluate stock management, logistics management, and average time between failures.

Government and Military

Use traditional financial and economic analysis to assess, prioritize, and optimize the public sector, government, nonprofit organizations, public finances, and military projects (portfolio acquisitions, advanced weapons research) in uncertain conditions. Evaluating value for society, forecasting demand, making a hierarchical analysis of society's needs, an analysis of alternatives, decision analysis, portfolio mix, and risk reduction.


Credit and microfinance analysis, compensation of executives (ESOP), financial and business valuation, exchange rate hedges, interest rate hedges and inflation immunization, investment valuation, and investment portfolio optimization. Identify and model the likelihood of occurrence of risk events, perform hypothesis tests to see if certain risk reduction and mitigation programs are, in fact, effective, and adjust data to distribution curves to identify their probability and impact.

Pharmacy and Biotechnology

Learn how to perform an optimal analysis of market risk inherent in the pharmaceutical industry, perform a process of patent valuation considering effects of uncertainty, valuation of sequential investments by phases and the optimal time of execution of these phases, calculate probability of technical success in the biotechnology industry, and evaluate opportunities spin-off.


Learn how to model risk and uncertainty using Monte Carlo simulation and making use of probability distributions. Improve growth opportunities by making use of forecasting techniques and predict variables such as profitability and volatility. Understand the different analytical tools and how they can be used for the modeling of credit risk, market risk, liquidity risk, and operational risk.

Oil and Gas

Identify and model the likelihood of occurrence of risk events (e.g., the need for spare parts, insufficient spare parts available, downtime, uptime, accidents, average operating time of equipment before failure, the maintenance program, breakdowns, temperature control, and extreme vibration modeling). Optimize and diversify the risk of capital infrastructure processes, joint ventures, portfolio mix, pre-investment analysis, and price prediction (outputs of raw material).

Real State

Development of mixed use of real estate, obtaining the optimum moment of an investment in real estate through a process of simulation of scenarios, property valuation management including the present uncertainty in the price of the infrastructure investment components and their volatility over time.


Learn how to perform an optimal management of assets and liabilities considering the effects of uncertainty in decisions, such as structuring coverage portfolios, and how to calculate insurance premiums by doing simulation analysis, how to perform Immunization in an Investment portfolio.


Perform valuation of mergers and acquisitions processes considering and modeling all the risks present in this activity and in the sector, obtaining the optimal selling price, performing a pre-investment analysis and capital infrastructure modeling the uncertainty through an analysis of risks that allow us to make reliable decisions.


Perform actuarial analysis using simulation methods, such as evaluating health insurance, methodological procedures according to the health reform law, performing the hospital risk management process considering simulation procedure, resource optimization applications and modeling of processes in technology medical.

Information / Technology

Perform assessment of procurement processes considering and modeling all the risks present in the sector, cost reduction using optimization processes, information security risk management, and technology mix, and assess the effect of technology updates over time.

Public Services / Energy

Identify the impact of all key variables, make an analysis of scenarios to model and identify effects on the variables of interest in the face of changes in prices and / or costs, adjust operational risk data with probability distributions, and make predictions of energy demand and types of generation, inputs, and materials online.

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