Pensions and Retirement Training Course on Quantitative Models in Pension Investing
Master Pensions Retirement Training with expert training. 10 Days course with certification. Comprehensive training program. Online & in-person. Enroll now!
Pensions And Retirement Courses10 DaysCertificate Included
Duration
10 Days
Mode
Online & Physical
Certificate
Included
Language
English
Course Overview
This advanced course provides pension fund professionals with the skills to apply quantitative models for portfolio construction, risk management, and performance analysis. Participants will learn how to use statistical, econometric, and computational models to enhance investment decision-making, optimize asset allocation, and assess risk exposures. The course emphasizes practical applications, case studies, and hands-on exercises to integrate quantitative methods into pension fund investing.
Secure enrollment • Professional certificate included
Learning Objectives
By the end of this course, participants will be able to:
Understand the role of quantitative models in pension fund investment strategies.
Apply statistical and econometric techniques to analyze financial data.
Build asset allocation and portfolio optimization models.
Use factor models, risk models, and performance attribution techniques.
Conduct stress testing, scenario analysis, and risk simulations.
Integrate liability-driven investment (LDI) considerations into models.
Evaluate model assumptions, limitations, and robustness.
Implement models for monitoring and reporting risk-adjusted returns.
Incorporate ESG and sustainability factors into quantitative models.
Communicate quantitative insights effectively to trustees and stakeholders.
Course Content
Module 1: Introduction to Quantitative Models in Pension Investing Purpose and scope of quantitative modeling Overview of statistical, econometric, and computational methods Historical evolution and application in pension fund management Key challenges and limitations of quantitative approaches Case study: Quantitative modeling success in global pension funds
Module 2: Statistical Techniques for Financial Data Descriptive and inferential statistics Time series analysis, autocorrelation, and volatility modeling Probability distributions and their application in finance Regression analysis for asset and liability modeling Monte Carlo simulation basics Workshop: Applying statistical techniques to portfolio and market data
Module 3: Portfolio Construction Models Mean-variance optimization and Markowitz framework Modern portfolio theory, CAPM, and multifactor models Factor-based portfolio construction Incorporating constraints: liquidity, ESG, regulatory, and risk limits Scenario-based portfolio design and rebalancing strategies Practical exercise: Building and stress-testing a multi-asset portfolio model
Module 4: Risk Modeling and Measurement Value-at-Risk (VaR), Conditional VaR, and Expected Shortfall Stress testing under historical and hypothetical scenarios Factor models for risk decomposition Sensitivity analysis for interest rate, currency, and equity shocks Portfolio stress visualization and dashboards Workshop: Risk modeling and analysis for diversified pension portfolios
Module 5: Performance Attribution and Benchmarking Risk-adjusted return metrics: Sharpe ratio, Information ratio, alpha, beta Attribution analysis by asset class, sector, and manager Benchmark selection and custom benchmark design Tracking error and performance drift analysis Visualization techniques for communicating performance results Practical exercise: Performance attribution using model outputs
Module 6: Liability-Driven Investment (LDI) Modeling Integrating pension liabilities into portfolio construction Asset-liability matching and duration gap management Cash flow matching, immunization strategies, and liability overlays Funding ratio sensitivity analysis under multiple economic scenarios Stress testing portfolios against adverse liability scenarios Case study: LDI modeling for defined benefit pension funds
Module 7: Advanced Quantitative Techniques Multi-factor and macroeconomic modeling Monte Carlo simulation and stochastic modeling for scenario generation Optimization under constraints: ESG, regulatory, and liquidity Risk budgeting and factor exposures for portfolio allocation Machine learning applications in pension portfolio modeling Workshop: Applying advanced quantitative techniques to real portfolio cases
Module 8: ESG and Sustainability Integration Incorporating ESG factors into portfolio modeling Scenario and risk assessment for sustainable investments ESG-adjusted performance measurement Factor tilting and thematic investment strategies Benchmarking ESG integration across portfolios Practical exercise: ESG-integrated modeling and reporting
Module 9: Model Validation and Risk Management Evaluating model assumptions, robustness, and limitations Backtesting and out-of-sample validation techniques Stress testing model sensitivity and scenario analysis Governance of quantitative models in pension funds Documentation, audit, and regulatory compliance requirements Workshop: Model validation, risk reporting, and error-checking
Module 10: Capstone Project Designing a comprehensive quantitative investment framework Integrating portfolio construction, risk modeling, and ESG factors Performing scenario analysis and performance attribution Evaluating risk-adjusted returns and funding ratio impacts Group presentations: Full quantitative modeling application for a pension fund Developing a governance and monitoring plan for ongoing model oversight
Who Should Attend
Pension fund trustees, investment officers, portfolio managers, risk managers, actuaries, quantitative analysts, and consultants responsible for modeling, portfolio construction, and investment decision-making.
