The Teichmuller space Teich(S) of a surface S in genus g>1 is a real submanifold of the quasifuchsian space QF(S). We show that the determinant of the Laplacian det'(Delta) on Teich(S) has a unique holomorphic extension to QF(S).
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9 results for “Delta-One”
Abstract: A comprehensive list of trading strategies and market insights.
problem Understanding trading strategies and market insights for implementation.
method Broad categorization into Delta-One and Derivative strategies based on trading expertise.
result Demonstrates how to cater to diverse market participants' preferences.
The Dirac operator d+delta on the Hodge complex of a Riemannian manifold is regarded as an annihilation operator A. On a weighted space L_mu^2 Omega, [A,A*] acts as multiplication by a positive constant on excited states if and only if the logarithm of the measure density of mu satisfies a pair of equations. The equati…
Model quantifies uncertainty's impact on European option prices.
problem Uncertainty in market volatility risk affects option pricing.
method Hamilton-Jacobi-Bellman framework and finite element method.
result Dependence of Delta on uncertainty is nonlinear and varied.
Optimal portfolios for fat-tailed risks using a new tail risk measure.
problem Optimizing portfolios for pension funds and insurance liabilities with extreme risk sensitivity.
method Developed a new tail risk measure (Extreme Deviation, XD) and optimized portfolios based on this measure.
result Optimal portfolios maximize return per unit of XD, balancing hedging and risk contributions.
Proposes a neural network for high-dimensional American option pricing.
problem High-dimensional American option pricing and hedging.
method Deep neural network framework based on backward stochastic differential equations.
result The framework yields prices and deltas on the entire spacetime.
Models to price long term loans in the securities lending business are developed. These longer horizon deals can be viewed as contracts with optionality embedded in them. This insight leads to the usage of established methods from derivatives theory to price such contracts. Numerical simulations are used to demonstrate…
Uncertainty-Aware Deep Hedgingq-fin.CP
Improved deep hedging with ensemble uncertainty quantification.
problem Uncertainty in deep hedging models hinders their deployment.
method Trained an ensemble of LSTM networks to quantify uncertainty in deep hedging under Heston volatility and proportional transaction costs.
result The ensemble's disagreement provides a strong predictive confidence measure for hedge performance.
MIMONets: Multiple-Input-Multiple-Output Neural Networks Exploiting Computation in Superpositioncs.LG
MIMONets speed up neural network inference by processing multiple inputs in parallel.
problem Reducing computational cost in neural network inference for large datasets.
method Proposes MIMONets, which augment neural network architectures with variable binding mechanisms to handle multiple inputs in superposition.
result Achieves significant speedups (2-4x) with minimal accuracy loss, demonstrating adaptability across different architectures.