Essays on Auctions in Financial Markets
Industrial Organization , Auctions
This thesis studies the design of auctions used in financial markets. The first chapter studies the market for Credit Default Swaps (CDS), which are financial derivative products that insure bond investors against default. Determining their payout is complicated because the volume of insurance is larger than the debt outstanding and the value of a bond is heterogeneous. CDS payouts are set in a two-stage auction. In the first stage dealers commit to supply or purchase a fixed quantity at the unknown final price. Then, the excess supply or demand is announced and a multi-unit uniform price auction is held to clear the market. The paper develops and estimates a structural model of bidding in these auctions and uses it to explore the dynamic auction process and to consider counterfactual changes to a static double auction. The second chapter proposes a computational method to solve for Bayes-Nash equilibria in games. It is applicable when the equilibrium can be characterized by a set of necessary conditions that link a distribution of private information (values) and actions (bids). The method allows for the solution of the multi-unit auction game with step-function bids that characterize, for example, electricity and Treasury auctions. A set of simulations studies the performance of this solution method in (i) symmetric first price auctions and (ii) multi-unit auctions, and then the method is applied to evaluate a counterfactual uniform price auction for the Turkish Treasury. The third chapter studies the scoring auction process used by the Federal Deposit Insurance Corporation (FDIC) to resolve insolvent banks. Although the basic structure of the scoring rule is known to bidders, they are uncertain about how the FDIC makes trade-offs between the different components. Uncertainty over the scoring rule motivates bidders to submit multiple bids for the same failed bank. To evaluate the effects of uncertainty and multiple bidding for FDIC costs we develop a methodology for analyzing multidimensional bidding environments where the auctioneer’s scoring weights are unknown to bidders, ex-ante. We estimate private valuations for failed banks and compute counter-factual experiments in which scoring uncertainty is eliminated.