Showing results 1 to 10 of approximately 10.(refine search)
Evolutionary programming as a solution technique for the Bellman equation
Evolutionary programming is a stochastic optimization procedure that has proved useful in optimizing difficult functions. This paper shows that evolutionary programming can be used to solve the Bellman equation problem with a high degree of accuracy and substantially less CPU time than Bellman equation iteration. Future applications will focus on sometimes binding constraints, a class of problem for which standard solutions techniques are not applicable.
Portable random number generators
Computers are deterministic devices, and a computer-generated random number is a contradiction in terms. As a result, computer-generated pseudorandom numbers are fraught with peril for the unwary. We summarize much that is known about the most well-known pseudorandom number generators: congruential generators. We also provide machine-independent programs to implement the generators in any language that has 32-bit signed integers-for example C, C++, and FORTRAN. Based on an extensive search, we provide parameter values better than those previously available.
Model uncertainty, robust policies, and the value of commitment
Using results from the literature on H-control, this paper incorporates model uncertainty into Whiteman's (1986) frequency domain approach to stabilization policy. The derived policies guarantee a minimum performance level even in the worst of (a bounded set of) circumstances. ; For a given level of model uncertainty, robust H- policies are shown to be more 'activist' than Whiteman's H- policies in the sense that their impulse responses are larger. Robust policies also tend to be more autocorrelated. Consequently, the premium associated with being able to commit is greater under model ...
Predicting exchange rate volatility: genetic programming versus GARCH and RiskMetrics
This article investigates the use of genetic programming to forecast out-of-sample daily volatility in the foreign exchange market. Forecasting performance is evaluated relative to GARCH(1,1) and RiskMetrics? models for two currencies, the Deutsche mark and the Japanese yen. Although the GARCH and RiskMetrics? models appear to have an inconsistent marginal edge over the genetic program using the mean-squared-error (MSE) and R2 criteria, the genetic program consistently produces lower mean absolute forecast errors (MAE) at all horizons and for both currencies.
Predicting exchange rate volatility: genetic programming vs. GARCH and RiskMetrics
This article investigates the use of genetic programming to forecast out-of-sample daily volatility in the foreign exchange market. Forecasting performance is evaluated relative to GARCH(1,1) and RiskMetrics models for two currencies, DEM and JPY. Although the GARCH/RiskMetrics models appear to have a inconsistent marginal edge over the genetic program using the mean-squared-error (MSE) and R2 criteria, the genetic program consistently produces lower mean absolute forecast errors (MAE) at all horizons and for both currencies.
Is technical analysis in the foreign exchange market profitable? a genetic programming approach
Using genetic programming techniques to find technical trading rules, we find strong evidence of economically significant out-of-sample excess returns to those rules for each of six exchange rates, over the period 1981-1995. Further, when the dollar/deutschemark rules are allowed to determine trades in the other markets, there is a significant improvement in performance in all cases, except for the deutschemark/yen. Betas calculated for the returns according to various benchmark portfolios provide no evidence that the returns to these rules are compensation for bearing systematic risk. ...
A note on dynamic programming with homogeneous functions
This note shows that basic theorems of dynamic programming hold when the return function is homogeneous of degree theta <= 1.
The solution and estimation of discrete choice dynamic programming models by simulation and interpolation: Monte Carlo evidence
Over the past decade, a substantial literature on the estimation of discrete choice dynamic programming (DC-DP) models of behavior has developed. However, this literature now faces major computational barriers. Specifically, in order to solve the dynamic programming (DP) problems that generate agents' decision rules in DC-DP models, high dimensional integrations must be performed at each point in the state space of the DP problem. In this paper we explore the performance of approximate solutions to DP problems. Our approximation method consists of: 1) using Monte Carlo integration to simulate ...