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on Computational Economics |
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Issue of 2012‒02‒15
four papers chosen by |
| By: | Dario Caldara; Jesús Fernández-Villaverde; Juan F. Rubio-Ramírez; Yao Wen |
| Abstract: | This paper compares different solution methods for computing the equilibrium of dynamic stochastic general equilibrium (DSGE) models with recursive preferences such as those in Epstein and Zin (1989 and 1991) and stochastic volatility. Models with these two features have recently become popular, but we know little about the best ways to implement them numerically. To fill this gap, we solve the stochastic neoclassical growth model with recursive preferences and stochastic volatility using four different approaches: second- and third-order perturbation, Chebyshev polynomials, and value function iteration. We document the performance of the methods in terms of computing time, implementation complexity, and accuracy. Our main finding is that perturbations are competitive in terms of accuracy with Chebyshev polynomials and value function iteration while being several orders of magnitude faster to run. Therefore, we conclude that perturbation methods are an attractive approach for computing this class of problems. |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2012-04&r=cmp |
| By: | Sörensen K.; Vanovermeire Ch.; Busschaert S. |
| Abstract: | Determining the optimal number and location of intermodal transshipment terminals is a decision that strongly influences the viability of the intermodal transportation alternative. In this paper, we develop a model and an optimization method that provides policy makers with a tool to help them take these decisions. The objective of the terminal location problem described in this paper is to determine which of a set of potential terminal locations to use and which not and how to route the supply and demand of a set of customers (representing zones of supply and demand) through the network (by both uni- and intermodal transport) so as to minimize the total cost. We develop two different metaheuristic procedures that both consist of two phases: a solution construction phase and a solution improvement phase. The first metaheuristic constructs solutions using a GRASP procedure, the second one uses the relatively unknown attribute based hill climber (ABHC) heuristic. Innovative in our approach is the integration of a fast heuristic procedure to approximate the total cost given the set of open terminals. Both metaheuristics are compared to the results of an MIP solver. A thorough performance assessment uncovers that both metaheuristics generate close-to-optimal solutions in very short computing times. An argument in favor of the ABHC approach is that it is parameter-free and hence more transparent and likely to be accepted in a business or policy environment. |
| Date: | 2012–01 |
| URL: | http://d.repec.org/n?u=RePEc:ant:wpaper:2012001&r=cmp |
| By: | Ribin Lye; James Peng Lung Tan; Siew Ann Cheong |
| Abstract: | We describe a bottom-up framework, based on the identification of appropriate order parameters and determination of phase diagrams, for understanding progressively refined agent-based models and simulations of financial markets. We illustrate this framework by starting with a deterministic toy model, whereby $N$ independent traders buy and sell $M$ stocks through an order book that acts as a clearing house. The price of a stock increases whenever it is bought and decreases whenever it is sold. Price changes are updated by the order book before the next transaction takes place. In this deterministic model, all traders based their buy decisions on a call utility function, and all their sell decisions on a put utility function. We then make the agent-based model more realistic, by either having a fraction $f_b$ of traders buy a random stock on offer, or a fraction $f_s$ of traders sell a random stock in their portfolio. Based on our simulations, we find that it is possible to identify useful order parameters from the steady-state price distributions of all three models. Using these order parameters as a guide, we find three phases: (i) the dead market; (ii) the boom market; and (iii) the jammed market in the the phase diagram of the deterministic model. Comparing the phase diagrams of the stochastic models against that of the deterministic model, we realize that the primary effect of stochasticity is to eliminate the dead market phase. |
| Date: | 2012–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1202.0606&r=cmp |
| By: | Jozef Barunik; Lukas Vacha |
| Abstract: | This paper proposes generalization of the popular realized volatility framework by allowing its measurement in the time-frequency domain and bringing robustness to both noise as well as jumps. Based on the generalization of Fan and Wang (2007) approach using smooth wavelets and Maximum Overlap Discrete Wavelet Transform, we present new, general theory for wavelet decomposition of integrated variance. Using wavelets, we not only gain decomposition of the realized variance into several investment horizons, but we are also able to estimate the jumps consistently. Basing our estimator in the two-scale realized variance framework of Zhang et al. (2005), we are able to utilize all available data and get unbiased estimator in the presence of noise as well. The theory is also tested in a large numerical study of the small sample performance of the estimators and compared to other popular realized variation estimators under different simulation settings with changing noise as well as jump level. The results reveal that our wavelet-based estimator is able to estimate and forecast the realized measures with the greatest precision. Another notable contribution lies in the application of the presented theory. Our time-frequency estimators not only produce more efficient estimates, but also decompose the realized variation into arbitrarily chosen investment horizons. The results thus provide a better understanding of the dynamics of stock markets. |
| Date: | 2012–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1202.1854&r=cmp |