Auction Data (auction + data)

Distribution by Scientific Domains

Selected Abstracts

Managing Risks in Multiple Online Auctions: An Options Approach,

Ram Gopal
ABSTRACT The scenario of established business sellers utilizing online auction markets to reach consumers and sell new products is becoming increasingly common. We propose a class of risk management tools, loosely based on the concept of financial options that can be employed by such sellers. While conceptually similar to options in financial markets, we empirically demonstrate that option instruments within auction markets cannot be developed employing similar methodologies, because the fundamental tenets of extant option pricing models do not hold within online auction markets. We provide a framework to analyze the value proposition of options to potential sellers, option-holder behavior implications on auction processes, and seller strategies to write and price options that maximize potential revenues. We then develop an approach that enables a seller to assess the demand for options under different option price and volume scenarios. We compare option prices derived from our approach with those derived from the Black-Scholes model (Black & Scholes, 1973) and discuss the implications of the price differences. Experiments based on actual auction data suggest that options can provide significant benefits under a variety of option-holder behavioral patterns. [source]

Optimal Nonparametric Estimation of First-price Auctions

ECONOMETRICA, Issue 3 2000
Emmanuel Guerre
This paper proposes a general approach and a computationally convenient estimation procedure for the structural analysis of auction data. Considering first-price sealed-bid auction models within the independent private value paradigm, we show that the underlying distribution of bidders' private values is identified from observed bids and the number of actual bidders without any parametric assumptions. Using the theory of minimax, we establish the best rate of uniform convergence at which the latent density of private values can be estimated nonparametrically from available data. We then propose a two-step kernel-based estimator that converges at the optimal rate. [source]

Estimating risk aversion from ascending and sealed-bid auctions: the case of timber auction data

Jingfeng Lu
Estimating bidders' risk aversion in auctions is a challenging problem because of identification issues. This paper takes advantage of bidding data from two auction designs to identify nonparametrically the bidders' utility function within a private value framework. In particular, ascending auction data allow one to recover the latent distribution of private values, while first-price sealed-bid auction data allow one to recover the bidders' utility function. This leads to a nonparametric estimator. An application to the US Forest Service timber auctions is proposed. Estimated utility functions display concavity, which can be partly captured by constant relative risk aversion. Copyright 2008 John Wiley & Sons, Ltd. [source]