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 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

Inträasset¿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 inträasset 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