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Found 7 papers, 2 papers with code
Intrinsic Randomness in Epidemic Modelling Beyond Statistical Uncertainty
Uncertainty can be classified as either aleatoric (intrinsic randomness) or epistemic (imperfect knowledge of parameters).
Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversions
We present a method for finding optimal hedging policies for arbitrary initial portfolios and market states.
Deep Hedging: Learning to Remove the Drift under Trading Frictions with Minimal Equivalent Near-Martingale Measures
We present a machine learning approach for finding minimal equivalent martingale measures for markets simulators of tradable instruments, e. g. for a spot price and options written on the same underlying.
Unifying incidence and prevalence under a time-varying general branching process
We also show that the incidence integral equations that arise from both of these specific models agree with the renewal equation used ubiquitously in infectious disease modelling.
Deep Hedging: Learning Risk-Neutral Implied Volatility Dynamics
We present a numerically efficient approach for learning a risk-neutral measure for paths of simulated spot and option prices up to a finite horizon under convex transaction costs and convex trading constraints.
A GMM approach to estimate the roughness of stochastic volatility
We show that a parameter estimator based on the integrated variance is consistent and, under stronger conditions, asymptotically normally distributed.
State-dependent Hawkes processes and their application to limit order book modelling
Additionally, we find that the endogeneity of the order flow, measured by the magnitude of excitation, is also state-dependent, being more pronounced in disequilibrium states of the limit order book.
