Cross-Platform Arbitrage: Pricing Insights From SettleRisk

Executive Summary

In the rapidly evolving landscape of prediction markets, cross-platform arbitrage presents a lucrative opportunity for traders seeking to capitalize on price discrepancies between markets like Polymarket and Kalshi. This deep dive explores how SettleRisk's resolution risk scoring API can empower traders to make informed decisions, optimize their strategies, and maximize profits through cross-platform arbitrage.

Core Concept

Cross-platform arbitrage involves simultaneously taking opposite positions on the same event across different prediction platforms to lock in a risk-free profit. The key to success in this strategy lies in accurately assessing and comparing the resolution risk on each platform, which can significantly impact the potential for profit.

SettleRisk provides a deterministic, closed-form scoring system that assigns a resolution risk score ranging from 0 to 100 across LOW, MEDIUM, HIGH, and CRITICAL tiers. Our API outputs include a risk premium, capital lockup cost, and fair spread, enabling traders to price in risk effectively.

Worked Example

To illustrate the application of SettleRisk in cross-platform arbitrage, consider an event with the following resolution risk scores:

| Platform | Resolution Risk Score | Risk Tier | |----------|----------------------|-----------| | Polymarket | 45 | HIGH | | Kalshi | 35 | HIGH |

The higher the risk score, the higher the potential for settlement delay and dispute, affecting the arbitrage opportunity. Using SettleRisk's API, traders can calculate the fair spread and risk premium for each platform:

from settlerisk import SettleRiskClient

# Initialize SettleRisk client
client = SettleRiskClient("your_api_key")

# Get risk scores for Polymarket and Kalshi
polymarket_score = client.get_score(event_id="poly_event_id")
kalshi_score = client.get_score(event_id="kalshi_event_id")

# Calculate fair spread and risk premium
poly_premium = polymarket_score.risk_premium
kalshi_premium = kalshi_score.risk_premium

# arbitrage_potential = poly_premium - kalshi_premium

import { SettleRiskClient } from "settlerisk";

// Initialize SettleRisk client
const client = new SettleRiskClient("your_api_key");

// Get risk scores for Polymarket and Kalshi
const polymarketScore = await client.getScore("poly_event_id");
const kalshiScore = await client.getScore("kalshi_event_id");

// Calculate fair spread and risk premium
const polyPremium = polymarketScore.riskPremium;
const kalshiPremium = kalshiScore.riskPremium;

// arbitragePotential = polyPremium - kalshiPremium;

Implementation Notes

When implementing cross-platform arbitrage strategies, consider the following:

1. Platform Bases: Polymarket uses a base of 12, while Kalshi uses a base of 8. Adjust your calculations to account for these differences.
2. Settlement Delays: SettleRisk models settlement delay as a lognormal distribution. Use this model to estimate p50, p90, and p99 settlement times for more accurate risk assessments.
3. API Versioning: SettleRisk's scoring system is version-stamped, ensuring reproducibility and allowing traders to track changes over time.

Failure Modes

Common failure modes in cross-platform arbitrage include:

1. Oracle Failure: Rely on multiple oracle feeds to mitigate the risk of misinformation.
2. Subjective Judgment: Understand the committee's resolution process to anticipate subjective outcomes.
3. Platform-Specific Risks: Each platform may have unique risks that are not captured by a generic resolution risk score.

Checklist

Before executing a cross-platform arbitrage trade, use this checklist:

1. Obtain resolution risk scores from SettleRisk for both platforms.
2. Calculate the fair spread and risk premium for each platform.
3. Adjust for platform bases and settlement delay probabilities.
4. Monitor for changes in risk scores and platform-specific events.
5. Execute trades with a clear understanding of potential failure modes.

Sources + Further Reading

For a comprehensive understanding of resolution risk and its implications for arbitrage, explore our in-depth methodology here. Additionally, learn more about the pricing engine and its outputs here. To get started with SettleRisk, check out our SDK documentation for Python and TypeScript.