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on Computational Economics |
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Issue of 2005‒07‒18
four papers chosen by |
| By: | Yves Atchade (Department of Mathematics and Statistics, University of Ottawa and LRSP) |
| Abstract: | This paper proposes an adaptive version for the Metropolis adjusted Langevin algorithm with a truncated drift (T-MALA). The scale parameter and the covariance matrix of the proposal kernel of the algorithm are simultaneously and recursively updated in order to reach the optimal acceptance rate of 0:574 (see Roberts and Rosenthal (2001)) and to estimate and use the correlation structure of the target distribution. We develop some convergence results for the algorithm. A simulation example is presented. |
| Keywords: | Markov Chain Monte Carlo, Stochastic approximation algorithms, Metropolis Adjusted Langevin algorithm, geometric rate of convergence. |
| JEL: | C10 C40 |
| Date: | 2005–03–01 |
| URL: | http://d.repec.org/n?u=RePEc:pqs:wpaper:0272005&r=cmp |
| By: | Thomas Pitz (Laboratory of Experimental Economics University of Bonn); Thorsten Chmura (Laboratory of Experimental Economics University of Bonn) |
| Abstract: | Multi-Agent Based Simulation is a branch of Distributed Artificial Intelligence that builds the base for computer simulations which connect the micro and macro level of social and economic scenarios. This paper presents a new method of modelling the formation and change of patterns of action in social systems with the help of Multi-Agent Simulations. The approach is based on two scientific concepts: Genetic Algorithms [Goldberg 1989, Holland 1975] and the theory of Action Trees [Goldman 1971]. Genetic Algorithms were developed following the biological mechanisms of evolution. Action Trees are used in analytic philosophy for the structural description of actions. The theory of Action Trees makes use of the observation of linguistic analysis that through the preposition by a semi-order is induced on a set of actions. Through the application of Genetic Algorithms on the attributes of the actions of an Action Tree an intuitively simple algorithm can be developed with which one can describe the learning behaviour of agents and the changes in action spaces. Using the extremely simplified economic action space, in this paper called “SMALLWORLDâ€, it is shown with the aid of this method how simulated agents react to the qualities and changes of their environment. Thus, one manages to endogenously evoke intuitively comprehensible changes in the agents‘ actions. This way, one can observe in these simulations that the agents move from a barter to a monetary economy because of the higher effectiveness or that they change their behaviour towards actions of fraud. |
| Keywords: | Multi agent system, genetic algorithms, actiontrees, learning, decision making, economic and social behaviour, distributed artificial intelligence |
| JEL: | C8 |
| Date: | 2005–07–14 |
| URL: | http://d.repec.org/n?u=RePEc:wpa:wuwpco:0507002&r=cmp |
| By: | Manuel Ammann (University of St. Gallen); Axel Kind (University of St. Gallen); Christian Wilde (Johann Wolfgang Goethe University Frankfurt) |
| Abstract: | We propose and empirically study a pricing model for convertible bonds based on Monte Carlo simulation. The method uses parametric representations of the early exercise decisions and consists of two stages. Pricing convertible bonds with the proposed Monte Carlo approach allows us to better capture both the dynamics of the underlying state variables and the rich set of real-world convertible bond specifications. Furthermore, using the simulation model proposed, we present an empirical pricing study of the US market, using 32 convertible bonds and 69 months of daily market prices. Our results do not confirm the evidence of previous studies that market prices of convertible bonds are on average lower than prices generated by a theoretical model. Similarly, our study is not supportive of a strong positive relationship between moneyness and mean pricing error, as argued in the literature. |
| Keywords: | Convertible bonds, Pricing, American Options, Monte Carlo simulation |
| JEL: | G13 G14 |
| Date: | 2005–07–16 |
| URL: | http://d.repec.org/n?u=RePEc:wpa:wuwpfi:0507015&r=cmp |
| By: | WILLIAM N. GOETZMANN (Yale School of Management, International Center for Finance); JEFFREY D. FISHER (Indiana University) |
| Abstract: | In this paper we simulate the performance of real estate portfolios using cash flows from commercial properties over the period 1977 Q4 through 2004 Q2. Our methodology differs from analyses that rely upon historical time-weighted rates of return on property. We relax implicit rebalancing and mark to market assumptions inherent in time-series analysis. We use the distribution of internal rates of return to analyze the performance distribution of commercial property investment. We examine the performance of real estate in the context of portfolios of stocks and bonds over the same period. |
| Keywords: | Asset Allocation, Real Estate |
| JEL: | G11 R33 |
| Date: | 2005–07–15 |
| URL: | http://d.repec.org/n?u=RePEc:ysm:somwrk:ysm456&r=cmp |