Metamodeling for Variable Annuities (Chapman and Hall/CRC Financial Mathematics Series)

Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Preface
I: Preliminaries
	1: Computational Problems in Variable Annuities
		1.1 Variable Annuities
		1.2 Computational Problems Related to Daily Hedging
		1.3 Computational Problems Related to Financial Reporting
		1.4 Summary
	2: Existing Approaches
		2.1 Scenario Reduction
			2.1.1 Scenario Ranking
			2.1.2 Representative Scenarios
			2.1.3 Importance Sampling
			2.1.4 Curve Fitting
			2.1.5 Random Sampling
		2.2 Inforce Compression
			2.2.1 Cluster Modeling
			2.2.2 Replicating Liabilities
			2.2.3 Replicated Stratified Sampling
		2.3 Summary
	3: Metamodeling Approaches
		3.1 A General Framework
		3.2 Literature Review
		3.3 Summary
II: Experimental Design Methods
	4: Latin Hypercube Sampling
		4.1 Description of the Method
		4.2 Implementation
		4.3 Examples
		4.4 Summary
	5: Conditional Latin Hypercube Sampling
		5.1 Description of the Method
		5.2 Implementation
		5.3 Examples
		5.4 Summary
	6: Hierarchical k-Means
		6.1 Description of the Method
		6.2 Implementation
		6.3 Examples
		6.4 Summary
III: Metamodels
	7: Ordinary Kriging
		7.1 Description of the Model
		7.2 Implementation
		7.3 Applications
			7.3.1 Ordinary Kriging with Latin Hypercube Sampling
			7.3.2 Ordinary Kriging with Conditional Latin Hypercube Sampling
			7.3.3 Ordinary Kriging with Hierarchical k-Means
		7.4 Summary
	8: Universal Kriging
		8.1 Description of the Model
		8.2 Implementation
		8.3 Applications
			8.3.1 Universal Kriging with Latin Hypercube Sampling
			8.3.2 Universal Kriging with Conditional Latin Hypercube Sampling
			8.3.3 Universal Kriging with Hierarchical k-Means
		8.4 Summary
	9: GB2 Regression Model
		9.1 Description of the Model
		9.2 Implementation
		9.3 Applications
			9.3.1 GB2 with Latin Hypercube Sampling
			9.3.2 GB2 with Conditional Latin Hypercube Sampling
			9.3.3 GB2 with Hierarchical k-Means
		9.4 Summary
	10: Rank Order Kriging
		10.1 Description of the Model
		10.2 Implementation
		10.3 Applications
			10.3.1 Rank Order Kriging with Latin Hypercube Sampling
			10.3.2 Rank Order Kriging with Conditional Latin Hypercube Sampling
			10.3.3 Rank Order Kriging with Hierarchical k-Means
		10.4 Summary
	11: Linear Model with Interactions
		11.1 Description of the Model
		11.2 Implementation
		11.3 Applications
			11.3.1 Linear Model with Latin Hypercube Sampling
			11.3.2 Linear Model with Conditional Latin Hypercube Sampling
			11.3.3 Linear Model with Hierarchical k-Means
		11.4 Summary
	12: Tree-Based Models
		12.1 Description of the Model
		12.2 Implementation
		12.3 Applications
			12.3.1 Regression Trees with Latin Hypercube Sampling
			12.3.2 Regression Trees with Conditional Latin Hypercube Sampling
			12.3.3 Regression Trees with Hierarchical k-Means
		12.4 Summary
	A: Synthetic Datasets
		A.1 The Synthetic Inforce
		A.2 The Greeks
Bibliography
Index