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Incomplete Markets

  • Nicholas H. Bingham
  • Rüdiger Kiesel
Part of the Springer Finance book series (FINANCE)

Abstract

We now return to general continuous-time financial market models in the setting of §6.1, i.e. there are d + 1 primary traded assets whose price processes are given by stochastic processes S 0,..., S d , which are assumed to be adapted, right-continuous with left-limits (RCLL) and strictly positive semi-martingales on a filtered probability space (Ω, F, ℙ, F) (as usual F = (F t ) t≤T ). We assume that the market is free of arbitrage, in the sense that there exist equivalent martingale measures, but it contains non-attainable contingent claims, i.e. there are cash flows that cannot be replicated by self-financing trading strategies. In view of Theorem 6.1.5 this means that we do not have a unique equivalent martingale measure. We try to answer the obvious questions in this setting: how should we price the non-attainable contingent claims, i.e. which of the possible equivalent martingale measures should we pick for our valuation formula based on expectation, and, how can we construct hedging strategies for the non-attainable contingent claims to ‘minimize the risk? We try to answer these two questions in the general setting and then consider a prominent example of an incomplete market, a market with stochastic volatility, in more detail.

Keywords

Trading Strategy Stochastic Volatility Price Process Contingent Claim Martingale Measure 
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-Verlag London 2004

Authors and Affiliations

  • Nicholas H. Bingham
    • 1
    • 2
  • Rüdiger Kiesel
    • 3
    • 4
  1. 1.Department of Probability and StatisticsUniversity of SheffieldSheffieldUK
  2. 2.Department of Mathematical SciencesBrunel UniversityUxbridge MiddlesexUK
  3. 3.Department of Financial MathematicsUniversity of UlmUlmGermany
  4. 4.Department of StatisticsLondon School of EconomicsLondonUK

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