New Economics Papers
on Computational Economics
Issue of 2006‒08‒19
two papers chosen by

  1. Valuation of financial assets using montecarlo: when the world is not so normal By MAYA O. Cecilia
  2. Simulating Knowledge-Generation and -Distribution Processes in Innovation Collaborations and Networks By Andreas Pyka; Nigel Gilbert; Petra Ahrweiler

  1. By: MAYA O. Cecilia
    Abstract: Valuing financial assets when the world is not as normal as assumed by many financial models requires a method flexible enough to function with different distributions which, at the same time, can incorporate discontinuities such as those that arise from jump processes. The Monte Carlo method fulfills al these requirements, in adition to being accurate and efficient, which makes this numerical method the most suitable one in those cases that do not conform to normality. This paper applies monte Carlo to the valuation of financial assets, specifically financial options, when the underlying asset follows stochastic volatility or jump-diffusion processes.
    Date: 2004–09–01
  2. By: Andreas Pyka (University of Augsburg, Department of Economics); Nigel Gilbert (School of Human Sciences, University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom); Petra Ahrweiler (Research Center Media and Politics, Institute for Political Science, University of Hamburg, Germany)
    Abstract: An agent-based simulation model representing a theory of the dynamic processes involved in innovation in modern knowledge-based industries is described. The agent-based approach al-lows the representation of heterogeneous agents that have individual and varying stocks of knowledge. The simulation is able to model uncertainty, historical change, effect of failure on the agent population, and agent learning from experience, from individual research and from partners and collaborators. The aim of the simulation exercises is to show that the artificial innovation networks show certain characteristics they share with innovation networks in knowledge intensive industries and which are difficult to be integrated in traditional models of industrial economics.
    Keywords: innovation networks, agent-based modelling, scale free networks
    JEL: O31 O32 L22
    Date: 2006–08

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