This book is devoted to the mathematical methods of metamodeling that can be used to speed up the valuation of large portfolios of variable annuities. It is suitable for advanced undergraduate students, graduate students, and practitioners. It is the goal of this book to describe the computational problems and present the metamodeling approaches in a way that can be accessible to advanced undergraduate students and practitioners. To that end, the book will not only describe the theory of these mathematical approaches, but also present the implementations.
Preface. Part 1: Preliminaries. 1. Computational Problems in Variable Annuities. 2. Existing Approaches. 3. Metamodeling Approach. Part 2: Experimental Design Methods. 4. Conditional Latin Hypercube Sampling. 5. Hierarchical Clustering. 6. Partitional Clustering. Part 3: Predictive Modeling. 7. Ordinary Kriging. 8. Universal Kriging. 9. GB2 Regression Model. 10. Spliced Regression Model. 11. Neural Networks. Appendix A: Sample Datasets.