PTMC is a proposed Monte Carlo estimator that generates market-outcome distributions by simulating continuous double-auction interactions among persona-conditioned neural-policy bots whose heterogeneity is drawn from a learned distribution.
Andersen, Tim Bollerslev, Francis X
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Matrix-HAR model with multi-horizon lags and renewable generation inputs improves one-week forecasts of realized covariation and spread risk premia versus standard backward-looking volatility methods in electricity markets.
citing papers explorer
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Persona-Trained Monte Carlo: Estimating Market-Outcome Distributions via Swarms of Persona-Conditioned Neural Policy Bots in a Limit Order Book
PTMC is a proposed Monte Carlo estimator that generates market-outcome distributions by simulating continuous double-auction interactions among persona-conditioned neural-policy bots whose heterogeneity is drawn from a learned distribution.
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Forecasting of volatility and risk premia in electricity markets
Matrix-HAR model with multi-horizon lags and renewable generation inputs improves one-week forecasts of realized covariation and spread risk premia versus standard backward-looking volatility methods in electricity markets.