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Explore cutting-edge statistical methodologies for collecting, analyzing, and modeling online auction data Online auctions are an increasingly important marketplace, as the new mechanisms and formats underlying these auctions have enabled the capturing and recording of large amounts of bidding data that are used to make important business decisions. As a result, new statistical ideas and innovation are needed to understand bidders, sellers, and prices. Combining methodologies from the fields of statistics, data mining, information systems, and economics, Modeling Online Auctions introduces a new approach to identifying obstacles and asking new questions using online auction data. The authors draw upon their extensive experience to introduce the latest methods for extracting new knowledge from online auction data. Rather than approach the topic from the traditional game-theoretic perspective, the book treats the online auction mechanism as a data generator, outlining methods to collect, explore, model, and forecast data. Topics covered include: Data collection methods for online auctions and related issues that arise in drawing data samples from a Web site Models for bidder and bid arrivals, treating the different approaches for exploring bidder-seller networks Data exploration, such as integration of time series and cross-sectional information; curve clustering; semi-continuous data structures; and data hierarchies The use of functional regression as well as functional differential equation models, spatial models, and stochastic models for capturing relationships in auction data Specialized methods and models for forecasting auction prices and their applications in automated bidding decision rule systems Throughout the book, R and MATLAB® software are used for illustrating the discussed techniques. In addition, a.

One view regarding auction duration suggests that longer auctions would result in more bidders and more bids, which in turn would result in higher prices. An opposing view is that shorter auctions might appeal to impatient bidders, or alternatively, that shorter duration might lead to more competitive dynamics. To examine these competing notions, we conduct pairwise comparisons of simultaneous auctions identical in all but duration. The auctions are conducted on two different platforms—eBay and a local auction site. We find that in eBay auctions, longer duration increases the number of bidders and bids, and consequently increases final prices by about 11%. On the local auction website, with far fewer auctions and a more steady set of participants, the effect is reversed, and shorter auctions generate higher prices by about 20%. Both sets of effects are robust and significant. We look at bidding activity on both sites to try to get at the root of that reversal. We find that in eBay auctions, the higher price in the longer-duration auction is accompanied by a higher number of participating bidders and a higher number of bids placed in the auction. In the local site, we find that the auction duration does not significantly affect the number of participating bidders or the number of bids placed in an auction. However, the magnitude of jump bids is negatively and significantly correlated with duration. These jump bids are in turn shown to impact final prices.

ABSTRACTThe 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.