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What Can Be Learned from Inverse Statistics?

  • Peter Toke Heden Ahlgren
  • Henrik Dahl
  • Mogens Høgh Jensen
  • Ingve Simonsen

Abstract

One stylized fact of financial markets is an asymmetry between the most likely time to profit and to loss. This gain–loss asymmetry is revealed by inverse statistics, a method closely related to empirically finding first passage times. Many papers have presented evidence about the asymmetry, where it appears and where it does not. Also, various interpretations and explanations for the results have been suggested. In this chapter, we review the published results and explanations. We also examine the results and show that some are at best fragile. Similarly, we discuss the suggested explanations and propose a new model based on Gaussian mixtures. Apart from explaining the gain–loss asymmetry, this model also has the potential to explain other stylized facts such as volatility clustering, fat tails, and power law behavior of returns.

Keywords

Gaussian Mixture Model Stylize Fact Return Level Bond Price Geometrical Brownian Motion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer 2010

Authors and Affiliations

  • Peter Toke Heden Ahlgren
    • 1
  • Henrik Dahl
    • 1
  • Mogens Høgh Jensen
    • 2
  • Ingve Simonsen
    • 3
  1. 1.Nykredit Asset ManagementCopenhagenDenmark
  2. 2.Niels Bohr InstituteCopenhagenDenmark
  3. 3.Department of PhysicsNorwegian University of Science and Technology (NTNU)TrondheimNorway

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