Jeremy Maletz
Jeremy Maletz describes how Susquehanna uses prediction markets, swaps, and insurance to price and take on risks, from elections and sports to data-center policy exposure. Institutional hedging could broaden participation and support project financing, but cash funding, limited liquidity, and difficult-to-model correlations constrain the business. Maletz says the firm currently holds most OTC risk and that data-center risk-transfer requests can reach $10 billion.
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Read the Full SummaryKey Takeaways
Susquehanna’s prediction-markets business grew from macro work and election trading, while sports markets benefit from existing regulated markets and latent demand for a better product. Maletz says non-sports businesses are growing faster, but sports remains larger overall.
Institutional block trades can exceed visible screen liquidity: Maletz says a $50 million customer trade may not be constrained by the screen because Susquehanna can hold the position rather than immediately hedge it. He says the firm currently holds most OTC trade risk, while greater market liquidity could let it lay off more.
Maletz describes pricing unusual risks as an information-gathering exercise rather than simply balancing order flow: for an exposure without established research, such as goat risk, the firm consults informed people and its own researchers to establish a fair price. In a policy-coverage example, a buyer paid $50,000 for $500,000 of coverage, with the two sides putting up $50,000 and $450,000 and the winning side receiving $500,000 at resolution.
Maletz says clients can take on risk through exchange-traded prediction contracts, off-exchange swaps, or regulated insurance; exchange execution offers transparency and clearing, while swaps can sometimes receive more favorable capital treatment. Prediction-market positions require cash because portfolio margin is not yet available, making trades—especially long-dated ones—more expensive, whereas swap margin is negotiated and some swaps require no collateral.
For data-center moratorium exposure, Maletz describes swaps with a binary policy trigger and payouts linked to chip, compute, or power prices; a Texas governor-election contract can be a correlated but imperfect proxy. Hedge funds may combine election and moratorium contracts or use a bank-created basket, and existing Texas election exposure could lead the firm to quote a worse price on an additional moratorium position.
MARKETS
“Because as head of macro, we had quite a bit of focus around elections…”—
“The biggest ones being that there was already a market for sports through all the regulated operators in certain states that existed, and this huge amount of sort of latent…”—
“Like most of those are positions that we're gonna hold for the entirety…”—

Susquehanna Prediction Markets
Maletz traces Susquehanna’s prediction-market business to election-focused macro trading and describes sports as its largest, more established market.
OTC Risk Underwriting
The institutional business differs from automated market making because Susquehanna underwrites large, often bespoke exposures and usually holds the resulting risk.
Pricing Unusual Risks
Maletz says the firm’s culture centers on determining fair value, and contrasts novel, difficult-to-standardize exposures with conventional insurance products.
Swaps, Insurance, And Exchanges
Prediction-market contracts, swaps, and insurance each offer different trade-offs in transparency, capital treatment, regulation, and client access.
Goat Herder Trade
Maletz describes how Susquehanna priced and structured the goat-herder hedge, emphasizing fair terms, proof of concept, and the potential for later trading.
Election And Data Center Hedges
Maletz distinguishes broad election exposure from targeted business risks, noting that data-center operators focus on concrete policy threats such as moratoriums.
Sports Risk And Correlations
Sports-linked businesses can hedge media, sponsorship, and promotion exposures, while Susquehanna manages positions over time and assesses sparse, complex correlations.
Data Center Credit Risk
Maletz says data-center and AI-related exposures could require very large risk capacity, and argues that partnerships and risk transfer could improve project financing.
Closing Credits
Maletz thanks the hosts, and Levine closes the episode with newsletter, listener-email, and production information.
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