Managing Risks in Multiple Online Auctions: An Options Approach*

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.

Subject Areas: Market Efficiency, Online Auctions, Options, Risk Management, and Simulation.

INTRODUCTION

The volume of trade in online auctions has grown at a phenomenal pace. For example, eBay, currently the world's largest online auction house, transacted a total of $14.87 billion worth of auction items in 2002. This represents a rapid growth from $5 billion to $9.3 billion in 2000 and 2001, respectively (eBay Inc., 2005). Initially, these online auction sites catered almost exclusively to used, collectible, or otherwise idiosyncratic and/or unique merchandise. Over time, the range of goods available has expanded to include a wider array of product categories such as commercial electronics, automobiles, and durable consumer products, through which bidders can bid on brand new items directly from the manufacturers or retailers.

The growing presence of repeat sellers (a typical characteristic of a business seller with an extended presence) who move new items through a number of auctions can further be confirmed by evaluating auction activity on eBay. Table 1 illustrates the volume of successful single-unit auctions for brand new, identical items for an eight-week period. For each item, sellers who initiated multiple auctions during this time period conducted at least two-thirds of the total number of auctions.

The focus of this article is on issues relevant to repeat sellers of new, identical items. In particular, we address risks inherent in utilizing auctions as a mechanism to sell multiple new items.

Seller Risks

The risk for repeat sellers of new, identical items is fundamentally an issue of revenue uncertainty. The final price at each auction is dictated by the actions of the participants at that auction, although, as shown in Bapna, Goes, Gupta, and Karuga (2002), other factors, such as bid increments, also play a role. Further, the bidder participation and the ensuing prices across a seller's auctions can vary widely and can do so to the detriment of the seller's revenue. From an allocative perspective, each individual auction results in allocating the item to the highest bidder in that auction. However, it is entirely likely that the revenue realized by a seller who conducts M single unit auctions is significantly lower than the sum of the highest bids placed by the top M bidders in the seller's auctions. This represents potential lost revenue to the seller; an item may be sold to a bidder while another bidder who placed a higher bid leaves empty-handed. This is illustrated in Table 2 for the most active seller of each of the seven items. For example, the most active seller of Play Station 2 conducted a total of 136 single-item auctions in our observation period. However, among the 136 highest bidders who participated with the seller, 65 were unsuccessful in winning an item from the seller. This clearly represents lost revenue for the seller, as 65 of the items were sold at a price lower than what these bidders were willing to pay.

A number of factors explain this phenomenon. First is the risk of the dilution of bidders. When a large number of auctions are run simultaneously it becomes more likely that the pool of interested bidders at each auction will shrink, reducing the competition among bidders, which in turn can result in a lower final ending price. A second risk factor arises from herd behavior, which can result in an inefficient distribution of bidders with high valuations across auctions (Alsemgeest, Noussair, & Olson, 1998; Dholakia & Soltysinski, 2001; Dholakia, Basuroy, & Soltysinski, 2002). A high concentration of high-valuation bidders at a few auctions will negatively impact seller revenue. Third, the timing of bidder entry and exit can present a potential problem. A preponderance of low-valuation bidders during a period of time can result in lower auction prices in that time span. Finally, for the length of time an auction is active, typically days, the cost of monitoring and participating in the auction can reduce competition.