The bounds of heavy-tailed return distributions in evolving complex networks

摘要: Abstract We consider the evolution of scale-free networks according to preferential attachment schemes and show conditions for which exponent characterizing degree distribution is bounded by upper lower values. Our framework an agent model, presented in context economic trades, shows emergence critical behavior. Starting from a brief discussion about main features evolving network we that logarithmic return distributions have heavy tails, corresponding bounding values can be derived. Finally, discuss these findings model risk.