Showing papers by "Kenneth L. Judd published in 2009"

Numerically Stable Stochastic Simulation Approaches for Solving Dynamic Economic Models

Abstract: We develop numerically stable stochastic simulation approaches for solving dynamic economic models. We rely on standard simulation procedures to simultaneously compute an ergodic distribution of state variables, its support and the associated decision rules. We differ from existing methods, however, in how we use simulation data to approximate decision rules. Instead of the usual least-squares approximation methods, we examine a variety of alternatives, including the least-squares method using SVD, Tikhonov regularization, least-absolute deviation methods, principal components regression method, all of which are numerically stable and can handle ill-conditioned problems. These new methods enable us to compute high-order polynomial approximations without encountering numerical problems. Our approaches are especially well suitable for high-dimensional applications in which other methods are infeasible.

Harnessing parallelism in multicore clusters with the all-pairs and wavefront abstractions

Abstract: Both distributed systems and multicore computers are difficult programming environments. Although the expert programmer may be able to tune distributed and multicore computers to achieve high performance, the non-expert may struggle to achieve a program that even functions correctly.We argue that high level abstractions are an effective way of making parallel computing accessible to the non-expert. An abstraction is a regularly structured framework into which a user may plug in simple sequential programs to create very large parallel programs. By virtue of a regular structure and declarative specification, abstractions may be materialized on distributed, multicore, and distributed multicore systems with robust performance across a wide range of problem sizes. In previous work, we presented the All-Pairs abstraction for computing on distributed systems of single CPUs. In this paper, we extend All-Pairs to multicore systems, and introduce Wavefront, which represents a number of problems in economics and bioinformatics. We demonstrate good scaling of both abstractions up to 32-cores on one machine and hundreds of cores in a distributed system.

Numerically Stable Stochastic Simulation Approaches for Solving Dynamic Economic Models

Abstract: We develop numerically stable stochastic simulation approaches for solving dynamic economic models. We rely on standard simulation procedures to simultaneously compute an ergodic distribution of state variables, its support and the associated decision rules. We differ from existing methods, however, in how we use simulation data to approximate decision rules. Instead of the usual least-squares approximation methods, we examine a variety of alternatives, including the least-squares method using SVD, Tikhonov regularization, least-absolute deviation methods, principal components regression method, all of which are numerically stable and can handle ill-conditioned problems. These new methods enable us to compute high-order polynomial approximations without encountering numerical problems. Our approaches are especially well suitable for high-dimensional applications in which other methods are infeasible.

Bond ladders and optimal portfolios

Abstract: Many bond portfolio managers argue that bond laddering tends to outperform other bond investment strategies because it reduces both market price risk and reinvestment risk for a bond portfolio in the presence of interest rate uncertainty. Despite the popularity of bond ladders as a strategy for managing investments in fixed-income securities, there is surprising little reference to this subject in the economics and finance literature. In this paper we analyze complex bond portfolios within the framework of a dynamic general equilibrium asset-pricing model. Equilibrium bond portfolios are nonsensical, implying a trading volume that vastly exceeds observed trading volume on ¯nancial markets. Such portfolios would also be very costly and thus suboptimal in the presence of even very small transaction costs. Instead portfolios combining bond ladders with a market portfolio of equity assets are nearly optimal investment strategies, which in addition would minimize transaction costs. This paper, therefore, provides a rationale for bond ladders as a popular bond investment strategy.

Bond Ladders and Optimal Portfolios

Abstract: Many bond portfolio managers argue that bond laddering tends to outperform other bond investment strategies because it reduces both market price risk and reinvestment risk for a bond portfolio in the presence of interest rate uncertainty. Despite the popularity of bond ladders as a strategy for managing investments in fixed-income securities, there is surprising little reference to this subject in the economics and finance literature. In this paper we analyze complex bond portfolios within the framework of a dynamic general equilibrium asset-pricing model. Equilibrium bond portfolios are nonsensical, implying a trading volume that vastly exceeds observed trading volume on financial markets. Such portfolios would also be very costly and thus suboptimal in the presence of even very small transaction costs. Instead portfolios combining bond ladders with a market portfolio of equity assets are nearly optimal investment strategies, which in addition would minimize transaction costs. This paper, therefore, provides a rationale for bond ladders as a popular bond investment strategy.