33 citations · 35 across the 13 of their papers we have counts for
7 papers · 1 filter
Simulating Stress Laws under Extremal Dependence: Characterizing What Generative Models Must Preserve
Mantu Gupta, Anand Deo
We study stress-scenario generation for systems driven by multivariate heavy-tailed risk factors. Within regions where several financial losses are simultaneously extreme, stress a…
An Extreme Value Perspective on Learning Stress Laws
Mantu Gupta, Anand Deo
We introduce Self-Similar Generative Estimation (SS-GEN), a method for simulating multivariate tail events and estimating rare-event probabilities in both heavy and light-tailed se…
Generating Plausible Stress Scenarios via Large Deviations
Anand Deo
Financial stress tests based on handpicked scenarios can mislead risk management by overlooking genuinely dangerous configurations or overemphasising shocks that are too implausibl…
EVT-Based Rate-Preserving Distributional Robustness for Tail Risk Functionals
Anand Deo
Risk measures such as Conditional Value-at-Risk (CVaR) focus on extreme losses, where scarce tail data makes model error unavoidable. To hedge misspecification, one evaluates worst…
Importance Sampling for Minimization of Tail Risks: A Tutorial
Anand Deo, Karthyek Murthy
This paper provides an introductory overview of how one may employ importance sampling effectively as a tool for solving stochastic optimization formulations incorporating tail ris…
Combining Retrospective Approximation with Importance Sampling for Optimising Conditional Value at Risk
Anand Deo, Karthyek Murthy, Tirtho Sarker
This paper investigates the use of retrospective approximation solution paradigm in solving risk-averse optimization problems effectively via importance sampling (IS). While IS ser…
Efficient Black-Box Importance Sampling for VaR and CVaR Estimation
Anand Deo, Karthyek Murthy
This paper considers Importance Sampling (IS) for the estimation of tail risks of a loss defined in terms of a sophisticated object such as a machine learning feature map or a mixe…