The Art of Commitment Pacing: Engineering Allocations to Private Capital

Advanced guidance for institutional investors, academics, and researchers on how to manage a portfolio of private capital funds The Art of Commitment Pacing: Engineering Allocations to Private Capital provides a much-needed analysis of the issues that face investors as they incorporate closed ended-funds targeting illiquid private assets (such as private equity, private debt, infrastructure, real estate) into their portfolios. These private capital funds, once considered "alternative" and viewed as experimental, are becoming an increasingly standard component of institutional asset allocations.   However, many investors still follow management approaches that remain anchored in the portfolio theory for liquid assets but that often lead to disappointing results when applied to portfolios of private capital funds where practically investors remain committed over nearly a decade.   When planning for such commitments, investment managers and researchers are faced with practical questions such as:   How to measure and control the real exposure to private assets?  How to forecast cash-flows for commitments to private capital funds?   What ranges for their returns and lifetime are realistic, and how can the investor’s skill be factored in?   Over which dimensions should a portfolio be diversified and how much diversification is enough?  How can the impact of co-investments or secondaries be modelled?  How to design pacing plans that lead to resilient and efficient portfolios?  What stress scenarios should be considered and how can they be applied?  These are just examples of the many questions for which answers are provided. The Art of Commitment Pacing describes established and new methodologies for building up and controlling allocations to such investments. This book offers a systematic approach for building up and controlling allocations to such investments.  The Art of Commitment Pacing is a valuable addition to the libraries of investment managers, as well as portfolio and risk managers involved in institutional investment. The book will also be of interest to advanced students of finance, researchers, and other practitioners who require a detailed understanding of forecasting and portfolio management methodologies. 

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