Download presentation

Presentation is loading. Please wait.

Published byAustyn Couch Modified over 2 years ago

1
A Bidding Model for Auctions of Offshore Alternative Energy Sites Prepared for the Eastern Economic Association New York, March 1, 2009 by Radford Schantz, US Department of the Interior (Radford.Schantz@mms.gov) and Walter Stromquist, Swarthmore College (mail@walterstromquist.com)

2
Background Energy production sites on public land are auctioned under a variety of authorities and using various auction formats. The focus in this paper is on: – oil and gas leases, for which (partly) we have an auction model, and, – alternative energy site leases, where the future auction format is a subject of discussion and planning.

3
Oil and Gas Lease Auctions Offshore: first-price sealed-bid… – … must be auctioned; there is no process of noncompetitive award. – The main authority is the Outer Continenal Shelf Lands Act (1953), 43 USC 1331. Onshore: second-price open ascending… – … auctioned when there is competitive interest. Absent that interest, an onshore lease can be awarded noncompetitively. – The main authority is the Federal Onshore Oil and Gas Leasing Reform Act (1987).

4
Alternative energy site auctions Onshore … BLM administers wind energy rights-of-way (ROW). The main authority is the Federal Land Policy and Management Act (1976), 43 USC 1701. Three basic types of ROW: energy testing; site testing; and commercial development. Sometimes first-come, first-serve noncompetitive process…. competitive process if plan requires it or if more than one applicant shows substantive interest.

5
Offshore alternative energy Section 388 of the Energy Policy Act of 2005 amended the OCS Lands Act and gave Interior authority to grant leases for alternative energy activities. “COMPETITIVE OR NONCOMPETITIVE BASIS- …the Secretary shall issue a lease, easement, or right-of- way on a competitive basis unless the Secretary determines after public notice of a proposed lease, easement, or right-of-way that there is no competitive interest.”

6
First offshore leases planned Measurement leases: for five years or more, but they cannot be converted into commercial leases. Forty nominations were received…16 areas that we consider top priority. StateEnergyProcess New Jersey6 windNoncompetitive Delaware1 windNoncompetitive Georgia3 windNoncompetitive Florida4 current3 competitive, 1 noncompetitive California2 wave1 competitive, 1 noncompetitive

7
Example: NJ wind sites

8
Wind

9
Wave

10
Ocean current

11
Auction models needed Offshore oil and gas: we routinely use a lease sale forecasting model, IMODEL, described next… Lack models for onshore energies… Seeking to develop auction models for offshore alternative energy… ➜ to help to determine the best auction format(s) for this new program

12
The Bidding Model First Price, Sealed Bid Auctions Common-value model Public and private information Symmetric equilibrium strategies Special features: - Number of potential bidders is a random variable May be determined inside the model, from an equilibrium condition - Role for uninformed bidders In special cases, may determine effective minimum bid for informed bidders - Flexible private information model Additive errors, but they apply to a transformed version of value

13
Rules of the Auction Seller announces Minimum bid, b min Bidding fee (ignore the fee for today!) Anyone can bid Highest bidder takes the property, pays amount of highest bid. Bidders do not know how many other bidders there are. TODAY’S EXAMPLE: Minimum bid $128 thousand No bidding fee

14
The Common Value Value of property = V, same for all bidders. Model V as a random variable We use an intermediate random variable: U = “value parameter” V is related to U: V = v(U) (increasing, but not usually linear) Why the extra level of complication? We want to use additive errors, but it isn’t plausible that estimating errors are independent of the size of V. But errors might be independent of some transformed version of V, and that is U.

15
The Common Value: Public Information All public information is represented by a probability distribution on U. (the “prior distribution”). Represent the prior by a cumulative distribution function (cdf): H (u) = Pr ( U ≤ u ) This determines a prior distribution on V itself. H may be continuous, discrete, or mixed. Bidders effectively use H as a prior distribution, and update their estimates of value based on their private signals and other circumstances H and the value function v(U) are common knowledge.

16
The common value: Public information TODAY’S EXAMPLE: With probability 0.20, the property is “successful” U = + 1/2 V = + $ 1 million With probability 0.80, the property is “a failure” U = – 1/2 V = – $ 90 thousand Prior mean value of the tract: $128 thousand (same as the minimum bid!) H(u) in the example

17
How many bidders? JARGON ALERT: We call the potential bidders “EVALUATORS.” If they get private signals, they are “INFORMED EVALUATORS.” Otherwise, they are “UNINFORMED EVALUATORS.” (It seems wrong to call them all “bidders,” since they might choose not to bid.) The number of INFORMED EVALUATORS is a random variable, N. TODAY’S EXAMPLE: N is Poisson, with mean m = 3. q(n) = Pr (N = n) = (3 n /n!) exp(-3) for n = 0, 1, 2…

18
How many bidders? In the model, N is always Poisson. The user can specify the mean, m. OR: The user might let the model determine m internally. User specifies an “information cost,” k All evaluators may decide whether to “buy” a private signal (and so become informed evaluators) At equilibrium, they each choose to do so with some common probability p, chosen so that the expected profit for informed evaluators is exactly k. The consequence: N is Poisson, with mean m determined by k. WE’RE NOT DOING THIS TODAY.

19
Uninformed evaluators Do uninformed evaluators matter? Not usually. Sometimes, yes. Suppose that the lowest possible value of V is $200 thousand, but the minimum bid is $128 thousand. Then uninformed evaluators can rationally bid $200 thousand, and somebody surely will do so. This “uninformed bid” replaces the legal minimum as the starting point for the informed bidders. The uninformed evaluators have no effect in today’s example.

20
PRIVATE SIGNALS Each informed evaluator receives private information in the form of a PRIVATE SIGNAL. The i-th evaluator’s signal is X i. We model each signal as a random variable. ADDITIVE ERRORS: X i = U + R i Now R i is an ESTIMATING ERROR. The evaluator knows its own X i, but not R i or the other evaluators’ signals. We assume that estimating errors are INDEPENDENT and NORMALLY DISTRIBUTED. (They are independent of U, V, N, and each other.) The standard deviation of the private signals is the same for all evaluators. It is a model parameter, . TODAY WE’LL USE = 2.

21
PRIVATE SIGNALS (example) Left curve: distribution of private signals if U = - 1/2 Right curve: distribution of private signals if U = +1/2 Evaluators update their “success probabilities” based on their private signals (and other circumstances).

22
Summary of the inputs Inputs to the model: Bmin and feeRules of the auction v( )Defines value, V = v(U) H ( )Public (prior) distribution for U mMean number of informed evaluators (or specify information cost, k) standard deviation for estimating errors All of these are entered by the user, and are assumed to be common knowledge.

23
Analyzing the Game This is a non-cooperative game. Let’s find a symmetric equilibrium strategy: x* = smallest private signal that justifies a bid g(x) = optimal bid by an evaluator with signal x (assume that g(x) is increasing, and defined when x x*.) This model does not look for asymmetric equilibriums. This is a hard calculation! But not that hard. Standard theory applies.

24
The symmetric equilibrium strategy IN TODAY’s EXAMPLE: x* = 1.67814 (…so it takes an encouraging signal to justify a bid, but not an exceptionally rare signal.) Here is g:

25
How does bidding depend on m? Equilibrium bidding strategy for m = 1, 2, 3, 4 (m = mean number of informed evaluators) More competition -> Be more reluctant to bid at all But bid higher if you have a very favorable signal.

26
Distribution function for the High Bid Probability of no bid at all: 0.597 Probability of bid above $ 250 thousand: 0.026

27
Outputs from the model (in the example) Expected number of bidders: Each evaluator bids if its private signal is at least x*. If the tract is a success, this requires x* = 1.67814 so estimation error is 1.17824 which happens with probability 0.278. If the tract is a failure, it happens with probability 0.138. So, with three informed evaluators, the expected number of bids is (3)(0.278) = 0.834 (if success) (3)(0.138) = 0.414 (if failure) or 0.498 (averaged over cases). In this example, we should expect to get about ½ of a bid (on average).

28
More outputs from the model Expected number of bids Probability of getting at least one bid Expected value of highest bid, if there is a bid Expected revenue to seller The model can also give the distribution of these variables.

29
Empirical testing The model needs testing! The Interior Department maintains an extensive historical database of offshore leases bid on, amounts of bids, bidder identities, and physical characteristics of the properties. What else should we be doing with this model?

Similar presentations

OK

HAWKES LEARNING SYSTEMS math courseware specialists Copyright © 2010 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Chapter 8 Continuous.

HAWKES LEARNING SYSTEMS math courseware specialists Copyright © 2010 by Hawkes Learning Systems/Quant Systems, Inc. All rights reserved. Chapter 8 Continuous.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on power generation Slides for ppt on pollution Ppt on project rhino in indian Ppt on current account deficit us Ppt on grease lubrication lines Ppt on latest technology in ece Ppt on data backup and recovery Download ppt on pulse code modulation pdf Ppt on polynomials of 92 Natural disasters for kids ppt on batteries