Y rl spot workshop on new markets new economics
Download
1 / 47

YRL Spot Workshop on - PowerPoint PPT Presentation


  • 213 Views
  • Uploaded on

Y!RL Spot Workshop on New Markets, New Economics. Welcome! Specific examples of new trends in economics, new types of markets virtual currency prediction (“idea”) markets experimental economics Interactive, informal ask questions rountable discussion wrap-up.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'YRL Spot Workshop on' - victoria


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Y rl spot workshop on new markets new economics l.jpg
Y!RL Spot Workshop onNew Markets, New Economics

  • Welcome!

  • Specific examples of new trends in economics, new types of markets

    • virtual currency

    • prediction (“idea”) markets

    • experimental economics

  • Interactive, informal

    • ask questions

    • rountable discussion wrap-up


Distinguished guests thanks l.jpg
Distinguished guests (thanks!)

  • Edward CastronovaProf. Economics, Cal State Fullerton

  • John LedyardProf. Econ & Social Sciences, CalTech

  • Justin WolfersProf. Economics, Stanford


Schedule l.jpg
Schedule

11am-noon Castronova on the Future of Cyberspace Economies

noon-1pm Lunch provided

1pm-2pm Ledyard on ~ Information Markets and Experimental Economics

2pm-3pm Wolfers on ~ Prediction Markets, Play Money, & Gambling

3pm-3:30pm Pennock on Dynamic Pari-Mutuel Market for Hedging, Speculating

3:30pm-4pm Roundtable Discussion


A dynamic pari mutuel market for hedging wagering and information aggregation l.jpg

A Dynamic Pari-Mutuel Market for Hedging, Wagering, and Information Aggregation

David M. Pennock

paper to appear EC’04, New York


Economic mechanisms for speculating hedging l.jpg
Economic mechanisms for speculating, hedging Information Aggregation

  • Financial

    • Continuous Double Auction (CDA)stocks, options, futures, etc

    • CDA with market maker (CDAwMM)

  • Gambling

    • Pari-mutuel market (PM)horse racing, jai alai

    • Bookmaker (essentially like CDAwMM)

  • Socially distinct, logically the same

  • Increasing crossover


Take home message l.jpg
Take home message Information Aggregation

  • A dynamic pari-mutuel market (DPM)

  • New financial mech for speculating on or hedging against an uncertain event; Cross btw PM & CDA

  • Only mech (to my knowledge) to

    • involve zero risk to market institution

    • have infinite (buy-in) liquidity

    • continuously incorporate new info;allow cash-out to lock in gain, limit loss


Outline l.jpg
Outline Information Aggregation

  • Background

    • Financial “prediction” markets

    • Pari-mutuel markets

    • Comparing mechs:PM, CDA, CDAwMM, MSR

  • Dynamic pari-mutuel mechanism

    • Basic idea

    • Three specific variations; Aftermarkets

    • Open questions/problems


What is a financial prediction market l.jpg

Information Aggregation 6

= 6 ?

= 6

I am entitled to:

$1 if

$0 if

What is a financial“prediction market”?

  • Take a random variable, e.g.

  • Turn it into a financial instrument payoff = realized value of variable

2004 CAEarthquake?

US’04Pres =Bush?


Real time forecasts l.jpg
Real-time forecasts Information Aggregation

  • price  expectation of random variable(in theory, in lab, in practice, ...huge literature)

  • Dynamic information aggregation

    • incentive to act on info immediately

    • efficient market  today’s price incorporates all historical information; best estimator

  • Can cash out before event outcome

  • BUT, requires bi-lateral agreement


Updating on new information l.jpg
Updating on new information Information Aggregation


The flip side of prediction hedging e g options futures insurance l.jpg

Allocate risk (“hedge”) Information Aggregation

insured transfers risk to insurer, for $$

farmer transfers risk to futures speculators

put option buyer hedges against stock drop; seller assumes risk

Aggregate information

price of insurance prob of catastrophe

OJ futures prices yield weather forecasts

prices of options encode prob dists over stock movements

market-driven lines are unbiased estimates of outcomes

IEM political forecasts

The flip-side of prediction: HedgingE.g. options, futures, insurance, ...


Continuous double auction cda l.jpg
Continuous double auction Information AggregationCDA

  • k-double auction repeated continuously

  • buyers and sellers continually place offers

  • as soon as a buy offer  a sell offer, a transaction occurs

  • At any given time, there is no overlap btw highest buy offer & lowest sell offer


Slide13 l.jpg

http://tradesports.com Information Aggregation


Slide14 l.jpg

http://www.biz.uiowa.edu/iem Information Aggregation

http://us.newsfutures.com/


Running comparison l.jpg
Running comparison Information Aggregation


Cda with market maker l.jpg
CDA with market maker Information Aggregation

  • Same as CDA, but with an extremely active, high volume trader (often institutionally affiliated) who is nearly always willing to sell at some price p and buy at price q  p

  • Market maker essentially sets prices; others take it or leave it

  • While standard auctioneer takes no risk of its own, market maker takes on considerable risk, has potential for considerable reward


Slide17 l.jpg

http://www.wsex.com/ Information Aggregation

http://www.hsx.com/


Bookmaker l.jpg
Bookmaker Information Aggregation

  • Common in sports betting, e.g. Las Vegas

  • Bookmaker is like a market maker in a CDA

  • Bookmaker sets “money line”, or the amount you have to risk to win $100 (favorites), or the amount you win by risking $100 (underdogs)

  • Bookmaker makes adjustments considering amount bet on each side &/or subjective prob’s

  • Alternative: bookmaker sets “game line”, or number of points the favored team has to win the game by in order for a bet on the favorite to win; line is set such that the bet is roughly a 50/50 proposition


Running comparison19 l.jpg
Running comparison Information Aggregation


What is a pari mutuel market l.jpg

A Information Aggregation

B

What is a pari-mutuel market?

  • E.g. horse racetrack style wagering

  • Two outcomes: A B

  • Wagers:


What is a pari mutuel market21 l.jpg
What is a pari-mutuel market? Information Aggregation

A

B

  • E.g. horse racetrack style wagering

  • Two outcomes: A B

  • Wagers:


What is a pari mutuel market22 l.jpg
What is a pari-mutuel market? Information Aggregation

A

B

  • E.g. horse racetrack style wagering

  • Two outcomes: A B

  • Wagers:


What is a pari mutuel market23 l.jpg

$ on B 8 Information Aggregation$ on A 4

1+ = 1+ =$3

total $ 12$ on A 4

= = $3

What is a pari-mutuel market?

A

B

  • E.g. horse racetrack style wagering

  • Two outcomes: A B

  • 2 equivalentways to considerpayment rule

    • refund + share of B

    • share of total


What is a pari mutuel market24 l.jpg
What is a pari-mutuel market? Information Aggregation

  • Before outcome is revealed, “odds” are reported, or the amount you would win per dollar if the betting ended now

    • Horse A: $1.2 for $1; Horse B: $25 for $1; … etc.

  • Strong incentive to wait

    • payoff determined by final odds; every $ is same

    • Should wait for best info on outcome, odds

    •  No continuous information aggregation

    •  No notion of “buy low, sell high” ; no cash-out


Running comparison25 l.jpg
Running comparison Information Aggregation


Dynamic pari mutuel market basic idea l.jpg
Dynamic pari-mutuel market Information AggregationBasic idea

  • Standard PM: Every $1 bet is the same

  • DPM: Value of each $1 bet varies depending on the status of wagering at the time of the bet

  • Encode dynamic value with a price

    • price is $ to buy 1 share of payoff

    • price of A is lower when less is bet on A

    • as shares are bought, price rises; price is for an infinitesimal share; cost is integral


Dynamic pari mutuel market example interface l.jpg
Dynamic pari-mutuel market Information AggregationExample Interface

A

B

A

B

  • Outcomes: A B

  • Current payoff/shr: $5.20 $0.97

$3.27

$3.27

$3.27

$3.27

$3.27

$3.27

$3.25

sell 100@

$0.85

sell 100@

market maker

traders

sell 100@

sell 100@

$3.00

$0.75

$1.50

$0.50

sell 35@

sell 3@

$1.25

$0.25

buy 4@

buy 200@

buy 52@

$1.00


Dynamic pari mutuel market setup notation l.jpg
Dynamic pari-mutuel market Information AggregationSetup & Notation

A

B

A

B

  • Two outcomes: A B

  • Price per share: pri1 pri2

  • Payoff per share: Pay1 Pay2

  • Money wagered: Mon1 Mon2 (Tot=Mon1+Mon2)

  • # shares bought: Num1 Num2


How are prices set l.jpg
How are prices set? Information Aggregation

  • A price function pri(n) gives the instantaneous price of an infinitesimal additional share beyond the nth

  • Cost of buying n shares:

  • Different assumptions lead to different price functions, each reasonable


Redistribution rule l.jpg
Redistribution rule Information Aggregation

  • Two alternatives

    • Losing money redistributed. Winners get: original money refunded + equal share of losers’ money

    • All money redistributed. Winners get equal share of all money

  • For standard PM, they’re equivalent

  • For DPM, they’re significantly different


Losing money redistributed l.jpg

! Information Aggregation

Losing money redistributed

  • Payoffs: Pay1=Mon2/Num1 Pay2=.

  • Trader’s exp pay/shr for e shares: Pr(A) E[Pay1|A] + (1-Pr(A)) (-pri1)

  • Assume: E[Pay1|A]=Pay1 Pr(A) Pay1 + (1-Pr(A)) (-pri1)


Market probability l.jpg
Market probability Information Aggregation

  • Market probability MPr(A)

  • Probability at which the expected value of buying a share of A is zero

  • “Market’s” opinion of the probability

  • MPr(A) = pri1 / (pri1 + Pay1)


Price function i l.jpg
Price function I Information Aggregation

  • Suppose: pri1 = Pay2 pri2=Pay1natural, reasonable, reduces dimens., supports random walk hypothesis

  • Implies

    MPr(A) = Mon1 Num1 Mon1 Num1 + Mon2 Num2


Deriving the price function l.jpg
Deriving the price function Information Aggregation

  • Solve the differential equationdm/dn = pri1(n) = Pay2 = (Mon1+m)/Num2where m is dollars spent on n shares

  • cost1(n) = m(n) = Mon1[en/Num2-1]

  • pri1(n) = dm/dn = Mon1/Num2 en/Num2


Interface issues l.jpg
Interface issues Information Aggregation

  • In practice, traders may find costs as the sol. to an integral cumbersome

  • Market maker can place a series of discrete ask orders on the queue, e.g.

    • sell 100 @ cost(100)/100

    • sell 100 @ [cost(200)-cost(100)]/100

    • sell 100 @ [cost(300)-cost(200)]/100

    • ...


Price function ii l.jpg
Price function II Information Aggregation

  • Suppose: pri1/pri2 = Mon1/Mon2also natural, reasonable

  • Implies

    MPr(A) = Mon1 Num1 Mon1 Num1 + Mon2 Num2


Deriving the price function37 l.jpg
Deriving the price function Information Aggregation

  • First solve for instantaneous pricepri1=Mon1/Num1 Num2

  • Solve the differential equationdm/dn = pri1(n) = Mon1+m(Num1+n)Num2

    cost1(n) = m =

    pri1(n) = dm/dn =


All money redistributed l.jpg
All money redistributed Information Aggregation

  • Payoffs: Pay1=Tot/Num1 Pay2=.

  • Trader’s expected pay/shr for e shares:Pr(A) (Pay1-pri1) + (1-Pr(A)) (-pri1)

  • Market probabilityMPr(A) = pri1 / Pay1


Price function iii l.jpg
Price function III Information Aggregation

  • Suppose: pri1/pri2 = Mon1/Mon2

  • Implies

    • MPr(A) = Mon1 Num1 Mon1 Num1 + Mon2 Num2

    • pri1(m) = cost1(m) =


Aftermarkets l.jpg
Aftermarkets Information Aggregation

  • A key advantage of DPM is the ability to cash out to lock gains / limit losses

  • Accomplished through aftermarkets

  • All money redistributed: A share is a share is a share. Traders simply place ask orders on the same queue as the market maker


Aftermarkets41 l.jpg
Aftermarkets Information Aggregation

  • Losing money redistributed: Each share is different. Composed of:

    • Original price refundedpriI(A)where I(A) is indicator fn

    • PayoffPayI(A)


Aftermarkets42 l.jpg
Aftermarkets Information Aggregation

  • Can sell two parts in two aftermarkets

  • The two aftermarkets can be automatically bundled, hiding the complexity from traders

    • New buyer buys priI(A)+PayI(A) for pri dollars

    • Seller of priI(A) gets $ priMPr(A)

    • Seller of PayI(A) gets $ pri(1-MPr(A))


Alternative psuedo aftermarket l.jpg
Alternative “psuedo” aftermarket Information Aggregation

  • E.g. trader bought 1 share for $5

  • Suppose price moves from $5 to $10

    • Trader can sell 1/2 share for $5

    • Retains 1/2 share w/ non-negative value, positive expected value

  • Suppose price moves from $5 to $2

    • Trader can sell share for $2

    • Retains $3I(A) ; limits loss to $3 or $0


Running comparison44 l.jpg
Running comparison Information Aggregation

[Hanson 2002]


Pros cons of dpm types l.jpg
Pros & cons of DPM types Information Aggregation


Pros cons of dpms generally l.jpg
Pros & cons of DPMs generally Information Aggregation

  • Pros

    • No risk to mechanism

    • Infinite (buying) liquidity

    • Dynamic pricing / information aggregationAbility to cash out

  • Cons

    • Payoff vector indeterminate at time of bet

    • More complex interface, strategies

    • One sided liquidity (though can “hedge-sell”)

    • Untested


Open questions problems l.jpg
Open questions / problems Information Aggregation

  • Is E[Pay1|A]=Pay1 reasonable? Derivable from eff market assumptions?

  • DPM call market

  • Combinatorial DPM

  • Empirical testingWhat dist rule & price fn are “best”?

  • >2 discrete outcomes (trivial?)Real-valued outcomes


ad