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  5. Scalar Market

Scalar Market

Definition

A Scalar Market has an outcome that is a value in a range (e.g., unemployment 4–6%). Payouts can be thresholded or linear with the realized value.

Structure

Unlike binary (Yes/No) or categorical (A/B/C) markets, scalar markets trade on numerical outcomes within a specified range:

  • Continuous range: e.g., 4.0% to 6.0% unemployment
  • Threshold or linear payout: Depends on platform design
  • More complex pricing: Requires understanding of distributions

Types of Scalar Markets

Threshold Scalar

Pays based on whether the outcome exceeds a threshold:

Example: "Will unemployment exceed 5.0%?"

  • Range: 4.0% - 6.0%
  • If outcome ≥ 5.0%: Pays $1.00
  • If outcome < 5.0%: Pays $0.00

This is effectively a binary market within a scalar framework.

Linear Scalar

Pays proportionally based on where the outcome falls in the range:

Example: "Unemployment rate in December"

  • Range: 4.0% - 6.0%
  • If outcome = 4.0%: Pays $0.00
  • If outcome = 5.0%: Pays $0.50
  • If outcome = 6.0%: Pays $1.00

Payout formula:

Payout = (Actual Value − Min) / (Max − Min)

Example Markets

Economic Indicators

"What will GDP growth be in Q4 2024?"

  • Range: -2.0% to +4.0%
  • Linear payout

"What will the unemployment rate be in December?"

  • Range: 3.5% to 6.5%
  • Linear payout

Temperature/Weather

"High temperature in NYC on July 4th?"

  • Range: 75°F to 95°F
  • Linear payout

Sports

"Total points scored in the Super Bowl?"

  • Range: 30 to 70 points
  • Linear payout

Trading Strategy

Expected Value Calculation

For linear scalar markets, calculate expected value based on your probability distribution:

EV = ∫ Payout(x) × Probability(x) dx

In practice:

  1. Estimate the probability distribution of the outcome
  2. Calculate the expected payout
  3. Compare to current market price
  4. Buy if expected payout > price

Hedging

Scalar markets allow fine-tuned hedging:

  • Buy contracts at multiple price points
  • Create custom payout profiles
  • Hedge specific ranges of outcomes

Advantages

  • Granular forecasting: Capture full probability distribution
  • Better for continuous variables: GDP, temperature, scores
  • Hedging flexibility: Trade specific ranges

Challenges

  • Complex pricing: Harder to understand fair value
  • Lower liquidity: Less popular than binary markets
  • Distribution uncertainty: Need to forecast entire distribution

Platform Differences

Different platforms implement scalar markets differently:

  • Some use threshold payouts (simplified)
  • Some use linear payouts (true scalar)
  • Some allow trading on range buckets

Related Terms

  • Binary Market
  • Categorical Market
  • Expected Value
  • Implied Probability
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