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Optimal Asset Pricing

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  • Turner, Rolf
  • Banerjee, Pradeep
  • Shahlori, Rayomand

Abstract

We describe an R package for determining the optimal price of an asset which is “perishable” in a certain sense, given the intensity of customer arrivals and a time-varying price sensitivity function which specifies the probability that a customer will purchase an asset offered at a given price at a given time. The package deals with the case of customers arriving in groups, with a probability distribution for the group size being specified. The methodology and software allow for both discrete and continuous pricing. The class of possible models for price sensitivity functions is very wide, and includes piecewise linear models. A mechanism for constructing piecewise linear price sensitivity functions is provided.

Suggested Citation

  • Turner, Rolf & Banerjee, Pradeep & Shahlori, Rayomand, 2014. "Optimal Asset Pricing," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(i11).
    • Handle: RePEc:jss:jstsof:v:058:i11
    DOI: http://hdl.handle.net/10.18637/jss.v058.i11
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    1. Soetaert, Karline & Petzoldt, Thomas & Setzer, R. Woodrow, 2010. "Solving Differential Equations in R: Package deSolve," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i09).
    2. Banerjee, Pradeep K. & Turner, T. Rolf, 2012. "A flexible model for the pricing of perishable assets," Omega, Elsevier, vol. 40(5), pages 533-540.
    3. Wen Zhao & Yu-Sheng Zheng, 2000. "Optimal Dynamic Pricing for Perishable Assets with Nonhomogeneous Demand," Management Science, INFORMS, vol. 46(3), pages 375-388, March.
    4. Richard Van Slyke & Yi Young, 2000. "Finite Horizon Stochastic Knapsacks with Applications to Yield Management," Operations Research, INFORMS, vol. 48(1), pages 155-172, February.
    5. John W. Mamer, 1986. "Successive Approximations for Finite Horizon, Semi-Markov Decision Processes with Application to Asset Liquidation," Operations Research, INFORMS, vol. 34(4), pages 638-644, August.
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