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Valuation in infinite-horizon sequential markets with portfolio constraints

Author

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  • Kevin X.D. Huang

    (Department of Economics, Utah State University, 3530 Old Main Hill, Logan, UT 84322-3530, USA)

Abstract

We develop a theory of valuation of assets in sequential markets over an infinite horizon and discuss implications of this theory for equilibrium under various portfolio constraints. We characterize a class of constraints under which sublinear valuation and a modified present value rule hold on the set of non-negative payoff streams in the absence of feasible arbitrage. We provide an example in which valuation is non-linear and the standard present value rule fails in incomplete markets. We show that linearity and countable additivity of valuation hold when markets are complete. We present a transversality constraint under which valuation is linear and countably additive on the set of all payoff streams regardless of whether markets are complete or incomplete.

Suggested Citation

  • Kevin X.D. Huang, 2002. "Valuation in infinite-horizon sequential markets with portfolio constraints," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 20(1), pages 189-198.
    • Handle: RePEc:spr:joecth:v:20:y:2002:i:1:p:189-198
    Note: Received: March 9, 2000; revised version: February 13, 2001
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    Cited by:

    1. Werner, Jan, 2014. "Rational asset pricing bubbles and debt constraints," Journal of Mathematical Economics, Elsevier, vol. 53(C), pages 145-152.

    More about this item

    Keywords

    Valuation; Infinite horizon; Portfolio constraint.;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D50 - Microeconomics - - General Equilibrium and Disequilibrium - - - General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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