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Concept

Tail value at risk

Related publications (19)

Peak Value-at-Risk Estimation for Stochastic Differential Equations using Occupation Measures

Matteo Raphael Tacchi

This paper proposes an algorithm to upper-bound maximal quantile statistics of a state function over the course of a Stochastic Differential Equation (SDE) system execution. This chance-peak problem is posed as a nonconvex program aiming to maximize the Va ...

IEEE2023

Chance-Constrained Trajectory Planning With Multimodal Environmental Uncertainty

Maryam Kamgarpour, Kai Ren

We tackle safe trajectory planning under Gaussian mixture model (GMM) uncertainty. Specifically, we use a GMM to model the multimodal behaviors of obstacles' uncertain states. Then, we develop a mixed-integer conic approximation to the chance-constrained t ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Gradient-based optimisation of the conditional-value-at-risk using the multi-level Monte Carlo method

Fabio Nobile, Sundar Subramaniam Ganesh

In this work, we tackle the problem of minimising the Conditional-Value-at-Risk (CVaR) of output quantities of complex differential models with random input data, using gradient-based approaches in combination with the Multi-Level Monte Carlo (MLMC) method ...

2023

Multi-Level Monte Carlo Methods for Uncertainty Quantification and Risk-Averse Optimisation

Sundar Subramaniam Ganesh

This work aims to study the effects of wind uncertainties in civil engineering structural design. Optimising the design of a structure for safety or operability without factoring in these uncertainties can result in a design that is not robust to these per ...

EPFL2022

On Approximations of Data-Driven Chance Constrained Programs over Wasserstein Balls

Daniel Kuhn, Wolfram Wiesemann

Distributionally robust chance constrained programs minimize a deterministic cost function subject to the satisfaction of one or more safety conditions with high probability, given that the probability distribution of the uncertain problem parameters affec ...

2023

Quantifying uncertain system outputs via the multi-level Monte Carlo method --- distribution and robustness measures

Fabio Nobile, Sebastian Krumscheid, Sundar Subramaniam Ganesh, Quentin Olivier Martin Ayoul--Guilmard

In this work, we consider the problem of estimating the probability distribution, the quantile or the conditional expectation above the quantile, the so called conditional-value-at-risk, of output quantities of complex random differential models by the MLM ...

2023

A contagion process with self-exciting jumps in credit risk applications

Puneet Pasricha

The modeling of the probability of joint default or total number of defaults among the firms is one of the crucial problems to mitigate the credit risk since the default correlations significantly affect the portfolio loss distribution and hence play a sig ...

TAYLOR & FRANCIS LTD2022

Essays in Banking and Financial Regulation

Susanne Johanna Petronella Léonie Vissers

This thesis examines how banks choose their optimal capital structure and cash reserves in the presence of regulatory measures. The first chapter, titled Bank Capital Structure and Tail Risk, presents a bank capital structure model in which bank assets ...

EPFL2021

Spatial Risk Measures and Rate of Spatial Diversification

Erwan Fabrice Koch

An accurate assessment of the risk of extreme environmental events is of great importance for populations, authorities and the banking/insurance/reinsurance industry. Koch (2017) introduced a notion of spatial risk measure and a corresponding set of axioms ...

2019

Stochastic approximation methods for PDE constrained optimal control problems with uncertain parameters

Matthieu Claude Martin

This thesis work focuses on optimal control of partial differential equations (PDEs) with uncertain parameters, treated as a random variables. In particular, we assume that the random parameters are not observable and look for a deterministic control which ...

EPFL2019

Adversarial Analytics

Viet Anh Nguyen

Adversarial learning is an emergent technique that provides better security to machine learning systems by deliberately protecting them against specific vulnerabilities of the learning algorithms. Many adversarial learning problems can be cast equivalently ...

EPFL2019

Energy and Reserve Dispatch with Distributionally Robust Joint Chance Constraints

Daniel Kuhn, Viet Anh Nguyen

We develop a two-stage stochastic program for energy and reserve dispatch of a joint power and gas system with a high penetration of renewables. Data-driven distributionally robust chance constraints ensure that there is no load shedding and renewable spil ...

2021

Multilevel Monte Carlo Approximation of Functions

Fabio Nobile, Sebastian Krumscheid

Many applications across sciences and technologies require a careful quantification of nondeterministic effects to a system output, for example, when evaluating the system’s reliability or when gearing it towards more robust operation conditions. At the he ...

2018

Data-Driven Chance Constrained Programs over Wasserstein Balls

Daniel Kuhn, Wolfram Wiesemann

We provide an exact deterministic reformulation for data-driven chance constrained programs over Wasserstein balls. For individual chance constraints as well as joint chance constraints with right-hand side uncertainty, our reformulation amounts to a mixed ...

2024

Risk-Based Human-Aware Multi-Robot Coordination in Dynamic Environments Shared with Humans

Alcherio Martinoli, Zeynab Talebpour

In this paper, we propose a risk-based coordination method for the Multi-Robot Task Allocation (MRTA) problem in human-populated environments. We introduce risk-based bids that incorporate human trajectory prediction uncertainties and furthermore, social c ...

2018

Model Uncertainty And Scenario Aggregation

Damir Filipovic, Mathieu Jacques David Cambou

This paper provides a coherent method for scenario aggregation addressing model uncertainty. It is based on divergence minimization from a reference probability measure subject to scenario constraints. An example from regulatory practice motivates the defi ...

Wiley-Blackwell2017

Robust Portfolio Optimization

Zheng Wei Yap

Since the 2008 Global Financial Crisis, the financial market has become more unpredictable than ever before, and it seems set to remain so in the forseeable future. This means an investor faces unprecedented risks, hence the increasing need for robust port ...

EPFL2017

Robust Bounds In Multivariate Extremes

Sebastian Engelke

Extreme value theory provides an asymptotically justified framework for estimation of exceedance probabilities in regions where few or no observations are available. For multivariate tail estimation, the strength of extremal dependence is crucial and it is ...

Institute of Mathematical Statistics2017

Urban governance in earthquake struck medium-sized Haitian cities

Abigaïl-Laure Sarah Irène Kern

This paper aims to provide an insight into the urban governance of four medium-sized Haitian cities hit by the earthquake of 2010, bearing in mind two affirmations of J. Pierre on urban governance; namely, that it is the better analytical model for the con ...

2015