Written by Spencer Hulse
Prediction markets are entering a more consequential phase.
What began primarily as a way to trade expectations about elections, court decisions, economic releases and policy outcomes is raising a harder financial question: can those positions become collateral for loans?
Kunal Gaurav believes the answer depends on whether lenders are prepared to model risks that conventional crypto-collateral systems were not designed to measure.
Gaurav is Founder and Principal Researcher at Gazillion Labs, where he leads research into prediction-market collateral, liquidation risk and decentralized lending infrastructure. His work examines what happens when bounded event-linked prices cross liquidation thresholds before resolution, related markets react to the same information shock and visible liquidity disappears during forced selling.
The research combines stochastic modeling, market microstructure, execution analysis and distributionally robust risk. Rather than treating liquidation as a rule applied only after a price decline, Gaurav models the full chain: how bounded event-linked prices evolve, when collateral first reaches the liquidation boundary, how shocks propagate across related markets and how much value can actually be recovered from the order book.
His broader quantitative work spans alpha generation and decay estimation, low-latency trading, multi-leg arbitrage, modified jump-diffusion modeling, execution algorithms and proprietary market-impact models for execution and liquidation analysis. These capabilities now form part of Gazillion Labs’ collateral framework, where price dynamics, liquidation timing and executable recovery are modeled together.
When prediction-market collateral must be liquidated, the lender faces an order-book execution problem. Recovery depends on available bids, order size, market depth, replenishment, urgency and the impact created by the liquidation itself. Gaurav’s proprietary impact models estimate stressed recovery rather than assuming that the full position can be sold at the displayed market price.
Gaurav holds a master’s degree in Applied Mathematics and Statistics from Stony Brook University, with a concentration in quantitative finance. He also earned the Certificate in Quantitative Finance from the CQF Institute at Fitch Learning, with training spanning stochastic calculus, derivatives, volatility, portfolio construction, machine learning and quantitative risk.
He is an IEEE Senior Member and an Associate Member of Sigma Xi, The Scientific Research Honor Society. He has also served as a judge for the AITEX Summit Spring 2026: From Concept to Creation, evaluating artificial-intelligence and intelligent-systems projects across innovation, technical execution and practical value.
His current research addresses what may become one of digital finance’s defining risk questions: how should lenders value, stress and liquidate collateral whose price is controlled by discrete real-world events?
Why prediction-market collateral requires its own price model
Prediction-market contracts are traded assets. Why can they not be modeled like ordinary crypto collateral?
Their prices follow a different economic structure.
A prediction-market position reflects the evolving probability of a real-world outcome, and its price is bounded by the contract’s payoff range. Unlike an ordinary asset price, it cannot rise or fall without limit. At the same time, a court ruling, election development, regulatory announcement or economic release can cause the price to move abruptly within those bounds.
A suitable model must therefore represent bounded prices, ordinary market fluctuations, sudden event-driven jumps and changing directional pressure as new information is absorbed.
Gaurav’s framework uses a custom stochastic differential equation designed for prediction-market prices. The process keeps the modeled price within its economically admissible range while incorporating continuous variation, discrete jumps, self-exciting event arrivals and adaptive drift.
“The bounded payoff is not a minor technical detail,” Gaurav said. “It changes the price dynamics, the liquidation geometry and the way stress should be modeled. The process has to respect the contract’s economic limits while remaining responsive to discrete information.”
This custom price process provides the foundation for the first-passage, cascade and liquidation components that follow.
The loss can occur before the event resolves
What is the central contribution of your first research paper?
Gaurav’s paper, “First-Passage Default Modeling for Prediction Market Collateral: Self-Exciting Dynamics and Adaptive Drift,” focuses on the difference between terminal risk and first-breach risk.
A terminal model asks whether collateral will cover the loan when the prediction contract finally resolves. But an automated lending system does not necessarily wait until that date. It liquidates when the collateral first crosses a required boundary.
A position can recover later and still have caused an earlier liquidation. By the time the final outcome is known, the borrower’s collateral may already have been sold and the lender may already have realized a shortfall.
The economically relevant quantity is therefore not only the probability of impairment at maturity. It is the probability that collateral reaches the liquidation boundary at any time before resolution.
“The path matters because liquidation happens along the path,” Gaurav said. “A model can look safe at final settlement while missing the event that actually produces the loss.”
Modeling shocks that reinforce one another
Why do you use a Hawkes process rather than independent jumps?
Independent-jump models assume that one event does not change the likelihood of another. Prediction markets can behave differently.
One political, judicial or regulatory development can trigger reactions across several connected contracts. The initial move can cause liquidations. Those liquidations consume bids, widen spreads and increase the risk of further forced selling.
Gaurav uses a multivariate Hawkes process to model that sequence. One shock temporarily raises the arrival intensity of further shocks in the same market and in economically related markets.
The framework evaluates the spectral radius of the cross-excitation matrix to determine whether the effects of a disturbance are expected to decay or whether feedback among connected markets can become self-reinforcing.
That distinction is important for collateral portfolios. Several positions may appear diversified because they ask different questions while still depending on the same election, court proceeding or policy outcome.
A short historical sample may not reveal that relationship. Gaurav’s framework therefore combines statistical dependence with the economic and semantic connections among markets.
Updating the model as information changes
Where does Kalman filtering enter the framework?
Prediction-market conditions are not static.
Expected price direction, event intensity and liquidity can change as new information arrives and the resolution date approaches. Parameters estimated from an earlier period can quickly become unreliable.
Kalman filtering allows the framework to update hidden state estimates as new observations arrive. Instead of treating drift as fixed, the model adapts its estimate of expected movement over time.
That is particularly useful when a market shifts from a quiet regime into an active event cascade.
The objective is not merely to fit historical prices. It is to maintain a live estimate of the risk state that can feed directly into collateral and liquidation decisions.
A probability surface for live collateral risk
You have created a probability surface analogous to an options volatility surface. What does it show?
Options markets use volatility surfaces to organize implied volatility across strikes and maturities.
Gazillion Labs applies a related visual concept to collateral risk: a real-time default-risk surface, represented as Π(s, Λ). The surface maps the current state of the position and the intensity of event activity into a first-breach default probability.
The analogy is useful, but the surfaces serve different purposes.
An implied-volatility surface is inferred from option prices. Gaurav’s default-risk surface is produced by the calibrated collateral model and estimates the probability that the position will cross its liquidation boundary before resolution.
In one strict-portfolio illustration, the modeled default probability increases from approximately 1.83% under baseline event intensity to about 7% during an active cascade.
The surface converts a complex stochastic model into an operating risk tool. A lending protocol can query it as market conditions change and use the output to adjust borrowing limits, collateral haircuts, margin requirements and risk premiums.
Market impact inside the collateral framework
How does your market-microstructure research enter the prediction-market model?
It enters directly through liquidation.
A quoted collateral value is not the same as the amount a lender can recover by selling the position.
Gaurav’s proprietary market-impact models estimate how liquidation size, available depth, urgency and order-book replenishment affect realized execution prices. The models evaluate the likely recovery from forced selling, including cases in which several borrowers liquidate identical or related positions after the same event.
As the strongest bids are consumed, later portions of a large sale execute at progressively worse prices. If the order book does not replenish fast enough, realized recovery can fall materially below the displayed value.
This is not a secondary adjustment. It is part of the collateral valuation itself.
“The risk model and the execution model cannot be separated,” Gaurav said. “If recoverable value depends on how the order interacts with the book, then market impact belongs inside the collateral framework.”
His current work uses market microstructure not only to evaluate trading performance, but to determine whether a collateral position can actually be converted into cash under stress.
When alpha and execution research meet collateral design
How did your broader quantitative work shape this architecture?
Gaurav’s quantitative research has focused on finding and executing signals under real market constraints.
That work includes alpha generation, alpha-decay estimation, regime detection, low-latency strategies, market-neutral and multi-leg arbitrage, modified jump-diffusion models and execution-sensitive strategy design.
The connection to collateral risk is direct.
An alpha signal can appear profitable before transaction costs, latency, queue position and market impact are included. A collateral position can appear safe before jump risk, stressed liquidity and execution impact are included.
Both problems require the model to move beyond theoretical value and estimate what remains after interaction with the market.
At Gazillion Labs, execution algorithms, order-book microstructure and proprietary impact models are embedded directly in the liquidation framework. They determine how quickly collateral can be sold, how market depth changes during execution and how much of the quoted value remains recoverable under stress.
Why automation is necessary but not enough
Does this argument imply that automated liquidation is a problem?
No. Automated liquidation is essential in a practical lending system.
Once collateral rules, borrowing limits and liquidation thresholds have been defined correctly, an algorithm can monitor positions and execute those rules consistently.
The limitation is not automation. It is the model that determines when and how automation acts.
An algorithm can submit an order immediately, but it cannot create buyers, prevent an event-driven gap or make an unrealistic quoted valuation recoverable.
The price model, impact model and liquidation rule must therefore be designed together.
Stress testing losses that history never recorded
Why is conventional historical stress testing insufficient?
The problem is not only that prediction markets often have short histories.
Risk managers can calculate value at risk, expected shortfall and related measures from available observations. They can also assign more weight to severe events that already occurred.
But reweighting the historical sample does not automatically create losses, liquidity conditions or joint market moves that were never present in that sample.
Gaurav’s second paper, “Optimal Transport Stress Testing and Fund-Level Risk Management for DeFi Lending Against Prediction Market Collateral,” addresses that limitation.
The framework defines an uncertainty set containing distributions that lie within a specified Wasserstein distance of the empirical data. The Wasserstein radius determines how far alternative distributions may depart from the observed sample and can be selected through calibration, sensitivity analysis and the institution’s chosen degree of conservatism.
Within that defined set, the model identifies the distribution producing the most adverse risk outcome. Subject to the model assumptions, this produces a mathematical upper bound on stressed risk over the specified range of plausible distributions.
The uncertainty concerns the unknown true distribution, not the radius itself. The radius is an explicit model parameter that controls the breadth of the stress test.
The purpose is not to forecast one exact crisis. It is to test whether the lending system remains defensible when the actual loss distribution differs from the limited history available to the model.
From research to an operating lending system
How do these components work together?
Gazillion Labs is developing a purpose-built risk architecture for lending against prediction-market collateral.
The stochastic model describes how bounded collateral prices evolve. The first-passage model identifies when liquidation occurs. The Hawkes process captures clustered event shocks. Spectral-radius analysis evaluates whether those shocks decay or reinforce one another. Kalman filtering updates the risk state as information changes. The probability surface translates those estimates into operating risk metrics. The proprietary impact model estimates liquidation recovery. Optimal-transport stress testing evaluates adverse distributions beyond the observed sample.
Together, these components are intended to answer the questions a lender must resolve before accepting the asset: Which markets qualify as collateral? How much can safely be borrowed? How should haircuts change as an event approaches? What value can actually be recovered? Which contracts share hidden exposure? How large could stressed loss become when historical evidence is incomplete?
The framework links those answers to collateral eligibility, borrowing limits, haircuts, risk premiums and liquidation decisions.
Building before leverage scales
Gaurav does not argue that prediction-market positions should never become collateral.
His position is that tradability begins the analysis rather than completes it.
A quoted price must be connected to early-liquidation risk, bounded event-driven price dynamics, stressed execution, concentration, oracle reliability and settlement uncertainty before it can support a defensible loan.
“Prediction markets can become important financial instruments,” Gaurav said. “But the lending architecture has to reflect how these assets actually behave before leverage is built on top of them.”
