Leverage in prediction markets poses significant challenges due to the risk of sudden price movements that can wipe out a financier's capital. When a market resolves instantly to zero, the fair fee charged to traders effectively negates any advantage from using leverage. For instance, if a market like “Will Trump Fire Powell?” experiences a sudden shift in prices, the financier faces immediate losses, as there’s no time to liquidate positions. This is why platforms like Polymarket and Kalshi don't typically offer leverage. To make leverage viable, financiers need to predict potential losses from sudden price jumps and pass those costs onto traders as an upfront fee. A common misconception is that simply calculating the expected losses based on current probabilities will yield useful leverage. In reality, this approach often results in returns that are no better than unleveraged positions. The key to effective leverage lies in understanding the market's price movements, distinguishing between continuous price changes (creep) and abrupt shifts (jumps). Financiers must account for both types of risks, as creep allows for reaction time while jumps do not. Estimating these risks over the entire life of a position is impractical, especially as market conditions can fluctuate greatly near significant events, like elections. Instead, using short epochs—small time windows where conditions are relatively stable—offers a practical solution. By treating market variables as constants within these epochs, financiers can accurately calculate the probabilities of liquidation and expected losses, allowing them to set a fair, rolling fee. The article also highlights advanced modeling techniques for creep and jumps, suggesting that a well-designed epoch-based system could enhance market efficiency. This includes concepts like block-level auctions to manage news-driven arbitrage, potentially reducing losses and fees for traders. For those looking to explore the math behind these ideas, a detailed report is available from Messari.