THOMAS MEYER, is the co-author of Beyond the J Curve (translated into Chinese, Japanese, and Vietnamese), J Curve Exposure, Mastering Illiquidity (all by Wiley), and two CAIA books, which are required reading for Level II of the Chartered Alternative Investment Analyst ® Program. He authored Private Equity Unchained (by Palgrave MacMillan).

• Acknowledgments xiiiChapter 1 Introduction 1Scope of the book 1Quick glossary 2The challenge of private capital 2Risk and uncertainty 3Why do we need commitment pacing? 4Illiquidity 4The siren song of the secondary market 4How does commitment pacing work? 5Significant allocations needed 7Multi‐asset‐class allocations 8Intra‐asset‐class diversification 8Engineering a resilient portfolio 9Organisation of the book 10Chapter 2 Institutional Investing in Private Capital 15Limited partnerships 15Structure 16Criticism 18Costs of intermediation 18Inefficient fund raising 18Addressing uncertainty 19Conclusion 19Chapter 3 Exposure 21Exposure definition 21Layers of investment 23Net asset value 23Undrawn commitments 24Commitment risk 24Timing 24Classification 25Exposure measures – LP’s perspective 25Commitment 26Commitment minus capital repaid 26Repayment‐age‐adjusted commitment 27Exposure measures – fund manager’s perspective 28Ipev Nav 28IPEV NAV plus uncalled commitments 29Repayment‐age‐adjusted accumulated contributions 30Summary and conclusion 31Chapter 4 Forecasting Models 37Bootstrapping 37Machine learning 38Takahashi–Alexander model 40Model dynamics 40Strengths and weaknesses 46Variations and extensions 47Stochastic models 49Stochastic modelling of contributions, distributions, and NAVs 49Comparison 50Conclusion 51Chapter 5 Private Market Data 53Fund peer groups 53Organisation of benchmarking data 53Bailey criteria 54Data providers 55Business model 55Public route 55Voluntary provision 56Problem areas 56Biases 57Survivorship bias 57Survivorship bias in private markets 58Impact 58Conclusion 59Chapter 6 Augmented TAM – Outcome Model 61From TAM to stochastic forecasts 61Use cases for stochastic cash‐flow forecasts 62Funding risk 62Market risk 65Liquidity risk 65Capital risk 66Model architecture 66Outcome model 67Pattern model 67Portfolio model 68System considerations 68Semi‐deterministic TAM 68Adjusting ranges for lifetime and TVPI 70Ranges for fund lifetimes 71Ranges for fund TVPIs 73Picking samples 76Constructing PDF for TVPI based on private market data 78A1*TAM results 82Chapter 7 Augmented TAM – Pattern Model 85A2*tam 86Reactiveness of model 86Model overview 87Changing granularity 89Injecting randomness 89Setting frequency of cash flows 90Setting volatility for contributions 92Setting volatility for distributions 94Scaling and re‐ picking cash‐ flow samples 94Convergence A2*TAM to TAM 95Split cash flows in components 97Fees 98Fixed returns 102Cash‐ flow‐ consistent NAV 103Principal approach 103First contributions, then distributions 103Forward pass 104Backward pass 104Combination 104Summary 105Chapter 8 Modelling Avenues into Private Capital 109Primary commitments 109Modelling fund strategies 110Parameter as suggested by Takahashi and Alexander (2002) 110Further findings on parameters 113Basing parameters on comparable situations 113Funds of funds 114Secondary buys 114Secondary FOFs 116Co‐investments 118Basic approach 118Co‐investment funds 119Syndication 119Side funds 119Impact on portfolio 120Chapter 9 Modelling Diversification for Portfolios of Limited Partnership Funds 123The LP diversification measurement problem 123Fund investments 124Diversification or skills? 124Aspects of diversification 125A (non‐ESG‐compliant) analogy 125Commitment efficiency 126Exposure efficiency 126Outcome assessment 126Diversifying commitments 127Assigning funds to clusters 127Diversification dimensions 128Self‐proclaimed definitions 128Market practices 128The importance of diversification over vintage years 129Other dimensions and their impact on risks 129Include currencies? 130Definitions 131Styles 131Classification groups 132Style drifts 133Robustness of classification schemes 133Modelling vintage year impact 134Commitment efficiency 135Importance of clusters 135Partitioning into clusters 136Measurement approach 137Remarks 139Mobility barriers 139Similarity is a measure for barriers to switching between classes 140Similarity is not correlation 140Is there an optimum diversification? 141How many funds? 141Costs of diversification 141How to set a ‘satisficing’ number of funds? 143Portfolio impact 143Commitment efficiency timeline 143Portfolio‐level forecasts 143Appendix A – Determining similarities 145Appendix B – Geographical similarities 146Geographical diversification for private capital 146Regional groups 146Trade blocs 147Transport way connection 148Language barriers 148Limits to geography as diversifier 148Appendix C – Multi‐strategies and others 149Appendix D – Industry sector similarities 149Appendix E – Strategy similarities 149Appendix F – Fund management firm similarities 150Appendix G – Investment stage similarities 151Appendix H – Fund size similarities 152Chapter 10 Model Input Data 155Categorical input data 155Perceptions 156Regulation 156Risk managers 157Can data be objective? 157Moving from weak to strong data 158Chapter 11 Fund Rating/Grading 161Private capital funds and ratings 161Fiduciary ratings 161Fund rankings 162Internal rating systems 162Further literature 163Private capital fund gradings 163Scope and limitations 163Selection skill model 164Assumptions for grading 165Prototype fund grading system 165Ex‐ante weights 166Expectation grades 166Risk grades 169Quantification 171Chapter 12 Qualitative Scoring 173Objectives and scope 173Relevant dimensions 174Investment style 175Management team 176Fund terms 177Liquidity and exits 178Incentive structure 178Alignment and conflicts of interest 180Independence of decision‐making 181Viability 181Confirmation 182Scoring method 183Tallying 183Researching practices 184Ex‐post monitoring 184Assigning grades 185Appendix – Search across several private market data providers 186Interoperability 186Matching 187Chapter 13 Quantification Based on Fund Grades 191Grading process 191Quartiling 191Quantiles 192Quartiling 193Approach 194Example – how tall will she be? 195Probabilistic statement 196Controlling convergence 196LP selection skills 198Impact of risk grade 201TVPI sampling 203Chapter 14 Bottom- up Approach to Forecasting 205Look‐ through 205Regulation 205Fund ratings 206Look‐ through in practice 206Bottom‐ up 207Stochastic bottom‐ up models 207Machine‐ learning‐ based bottom‐ up models 207Overrides 208Investment intelligence 208Advantages and restrictions 208Treatment as exceptions 209Integration of overrides in forecasts by a top‐ down model 209Probabilistic bottom‐ up 211Expert knowledge for probability density functions? 212Estimating ranges 212Combining top‐ down with bottom‐ up 214Chapter 15 Commitment Pacing 217Defining a pacing plan 217Pacing phases 218Ramp‐up phase 219Maintenance phase 219Ramp‐down phase 220Controlling allocations 221Simulating the pacing plan 221Ratio‐based commitment rules 222Dynamic commitments 222Pacing plan outcomes 222‘Slow and steady’ 223Accelerated pacing plan 223Liquidity constraints 224Impact on cash‐flow profile 224Impact of commitment types 225Maintenance phase 228Recommitments 229Target NAV 229Cash‐flow matching 230Additional objectives and constraints 231Commit to high‐quality funds 231Achieve intra‐asset diversification 231Minimise opportunity costs 233Satisficing portfolios 233Conclusion 234Chapter 16 Stress Scenarios 235Make forecasts more robust 235Communication 235Specific to portfolio 236Impact of ‘Black Swans’ 236Interest rates and inflationary periods 237Modelling crises 238Delay of new commitments 238Changes in contribution rates 238Changes in distributions 239NAV impact and secondary transactions 240Lessons 240Building stress scenarios 241Market replay 241Varying outcomes 242Foreign exchange rates 244Varying portfolio dependencies 244Increasing and decreasing outcome dependencies 244Increasing and decreasing cash‐flow dependencies 247Blanking out periods of distributions 247Varying patterns 248Stressing commitments 249Extending and shortening of fund lifetimes 250Front‐loading and back‐loading of cash flows 251Foreign exchange rates and funding risk 251Increasing and decreasing frequency of cash flows 253Increasing and decreasing volatility of cash flows 254Conclusion 256Chapter 17 The Art of Commitment Pacing 259Improved information technology 259Direct investments 260Use of artificial intelligence 260Risk of private equity 261Securitisations 261Judgement, engineering, and art 262Abbreviations 263Glossary 267Biography 275Bibliography 277Index 289