The following pages link to Risk estimation via regression (Q2795869):
Displaying 37 items.
- MCMC design-based non-parametric regression for rare event. application to nested risk computations (Q515537) (← links)
- Multilevel Monte Carlo methods and lower-upper bounds in initial margin computations (Q777908) (← links)
- Methods for computing numerical standard errors: review and application to value-at-risk estimation (Q1669699) (← links)
- Replicating portfolio approach to capital calculation (Q1691451) (← links)
- Numerical solutions to dynamic portfolio problems with upper bounds (Q1789606) (← links)
- The risk inflation criterion for multiple regression (Q1896246) (← links)
- Inference for conditional value-at-risk of a predictive regression (Q1996776) (← links)
- Non-nested estimators for the central moments of a conditional expectation and their convergence properties (Q2060336) (← links)
- An efficient estimation of nested expectations without conditional sampling (Q2095139) (← links)
- Efficient estimation of a risk measure requiring two-stage simulation optimization (Q2103034) (← links)
- Machine learning with kernels for portfolio valuation and risk management (Q2120539) (← links)
- A least-squares Monte Carlo approach to the estimation of enterprise risk (Q2153521) (← links)
- Sample recycling method -- a new approach to efficient nested Monte Carlo simulations (Q2155860) (← links)
- Financial network connectedness and systemic risk during the COVID-19 pandemic (Q2166079) (← links)
- Green nested simulation via likelihood ratio: applications to longevity risk management (Q2172053) (← links)
- A guide to Monte Carlo simulation concepts for assessment of risk-return profiles for regulatory purposes (Q2219611) (← links)
- Two-stage nested simulation of tail risk measurement: a likelihood ratio approach (Q2681447) (← links)
- Risk prediction with machine learning and regression methods (Q2875754) (← links)
- ON THE CALCULATION OF RISK MEASURES USING LEAST-SQUARES MONTE CARLO (Q2986664) (← links)
- Risk Tuning with Generalized Linear Regression (Q3168990) (← links)
- Online Risk Monitoring Using Offline Simulation (Q3386770) (← links)
- Kernel Smoothing for Nested Estimation with Application to Portfolio Risk Measurement (Q4604901) (← links)
- Efficient exposure computation by risk factor decomposition (Q4619510) (← links)
- Stochastic approximation schemes for economic capital and risk margin computations (Q4967869) (← links)
- Nested Monte Carlo simulation in financial reporting: a review and a new hybrid approach (Q5014496) (← links)
- Technical Note—Bootstrap-based Budget Allocation for Nested Simulation (Q5080667) (← links)
- Unbiased Deep Solvers for Linear Parametric PDEs (Q5093244) (← links)
- Using Smooth Transition Regressions to Model Risk Regimes (Q5139574) (← links)
- Efficient Nested Simulation for Conditional Tail Expectation of Variable Annuities (Q5139810) (← links)
- Measures of Residual Risk with Connections to Regression, Risk Tracking, Surrogate Models, and Ambiguity (Q5258948) (← links)
- Technical note—Constructing confidence intervals for nested simulation (Q6054413) (← links)
- A machine learning approach to portfolio pricing and risk management for high‐dimensional problems (Q6054432) (← links)
- Kernel quantile estimators for nested simulation with application to portfolio value-at-risk measurement (Q6066180) (← links)
- Computation of conditional expectations with guarantees (Q6159022) (← links)
- Deep xVA Solver: A Neural Network–Based Counterparty Credit Risk Management Framework (Q6159074) (← links)
- How many inner simulations to compute conditional expectations with least-square Monte Carlo? (Q6176176) (← links)
- Economic Representative Scenarios for Variable Annuity Dynamic Hedging of GMMB and GMDB (Q6549255) (← links)
