A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
CAESar improves risk forecasting by combining VaR and ES estimates.
Expected Shortfall (ES) is the average return on a risky asset conditional on the return being below some quantile of its distribution, namely its Value-at-Risk (VaR). The Basel III Accord, which will be implemented in the years leading up to 2019, places new attention on ES, but unlike VaR, there is little existing wo…
A new method tests Expected Shortfall by analyzing both duration and severity of VaR violations.
The paper introduces ESE scores for farmers to assess climate change risks.
A new realized conditional autoregressive Value-at-Risk (VaR) framework is proposed, through incorporating a measurement equation into the original quantile regression model. The framework is further extended by employing various Expected Shortfall (ES) components, to jointly estimate and forecast VaR and ES. The measu…
We introduce a novel regression framework which simultaneously models the quantile and the Expected Shortfall (ES) of a response variable given a set of covariates. This regression is based on a strictly consistent loss function for the pair quantile and ES, which allows for M- and Z-estimation of the joint regression …
A new formula reveals symmetries between mean excess and ES functions.
ES-Single uses ES to estimate gradients in unrolled graphs, reducing variance and improving performance.
The paper proposes efficient methods to learn VaR and ES using neural networks and Monte Carlo simulations.
Expected Shortfall (ES) in several variants has been proposed as remedy for the defi-ciencies of Value-at-Risk (VaR) which in general is not a coherent risk measure. In fact, most definitions of ES lead to the same results when applied to continuous loss distributions. Differences may appear when the underlying loss di…
ES-MAML uses Evolution Strategies for MAML, avoiding second derivative estimation.
The paper introduces a new method for forecasting financial risk using quantile-based modeling.
The study of higher order energy functionals was first proposed by Eells and Sampson in 1965 and, later, by Eells and Lemaire in 1983. These functionals provide a natural generalization of the classical energy functional. More precisely, Eells and Sampson suggested the investigation of the so-called -energy funct…
New framework forecasts ES using weighted quantiles.
Introduces Lambda Expected Shortfall as a risk measure generalizing ES.
This paper introduces a novel theoretically sound approach for the celebrated CMA-ES algorithm. Assuming the parameters of the multi variate normal distribution for the minimum follow a conjugate prior distribution, we derive their optimal update at each iteration step. Not only provides this Bayesian framework a justi…
A new method for calculating ES from VaR under Solvency II.
This paper introduces novel backtests for the risk measure Expected Shortfall (ES) following the testing idea of Mincer and Zarnowitz (1969). Estimating a regression framework for the ES stand-alone is infeasible, and thus, our tests are based on a joint regression for the Value at Risk and the ES, which allows for dif…
The paper proposes a mixed-frequency quantile regression model for VaR and ES forecasting.
Contextual policy search (CPS) is a class of multi-task reinforcement learning algorithms that is particularly useful for robotic applications. A recent state-of-the-art method is Contextual Covariance Matrix Adaptation Evolution Strategies (C-CMA-ES). It is based on the standard black-box optimization algorithm CMA-ES…
Expected Shortfall (ES) has been widely accepted as a risk measure that is conceptually superior to Value-at-Risk (VaR). At the same time, however, it has been criticised for issues relating to backtesting. In particular, ES has been found not to be elicitable which means that backtesting for ES is less straightforward…
Paper introduces new risk norms based on ES with flexible distortion functions.
This paper presents analytical solutions to the problem of how to calculate sensible VaR (Value-at-Risk) and ES (Expected Shortfall) contributions in the CreditRisk+ methodology. Via the ES contributions, ES itself can be exactly computed in finitely many steps. The methods are illustrated by numerical examples.
Finite energy solutions of 4-harmonic and ES-4-harmonic maps are trivial.
New axioms justify ES without NRC, linking it to mean-ES portfolio selection.
Small hypersphere is unstable in both 4-harmonic and ES-4-harmonic settings.
This paper extends exponential smoothing to distributional time series using Wasserstein distance.
Investigates diversification quotient based on VaR and ES for portfolio models.
We introduce new forecast encompassing tests for the risk measure Expected Shortfall (ES). The ES currently receives much attention through its introduction into the Basel III Accords, which stipulate its use as the primary market risk measure for the international banking regulation. We utilize joint loss functions fo…
We propose minimum regret search (MRS), a novel acquisition function for Bayesian optimization. MRS bears similarities with information-theoretic approaches such as entropy search (ES). However, while ES aims in each query at maximizing the information gain with respect to the global maximum, MRS aims at minimizing the…
Due to their prevalence, time series forecasting is crucial in multiple domains. We seek to make state-of-the-art forecasting fast, accessible, and generalizable. ES-RNN is a hybrid between classical state space forecasting models and modern RNNs that achieved a 9.4% sMAPE improvement in the M4 competition. Crucially, …
Unified framework for ensemble sampling in nonlinear contextual bandits with provable regret bounds.
A new ES method improves reinforcement learning speed and accuracy.
Develops a new framework for joint portfolio risk forecasting.
Off-policy learning is powerful for reinforcement learning. However, the high variance of off-policy evaluation is a critical challenge, which causes off-policy learning falls into an uncontrolled instability. In this paper, for reducing the variance, we introduce control variate technique to $\math…
Evolution Strategies (ES) emerged as a scalable alternative to popular Reinforcement Learning (RL) techniques, providing an almost perfect speedup when distributed across hundreds of CPU cores thanks to a reduced communication overhead. Despite providing large improvements in wall-clock time, ES is data inefficient whe…
Evolution Strategies (ES) are a powerful class of blackbox optimization techniques that recently became a competitive alternative to state-of-the-art policy gradient (PG) algorithms for reinforcement learning (RL). We propose a new method for improving accuracy of the ES algorithms, that as opposed to recent approaches…
ES reduces high-probability regret in stochastic linear bandits.
ES improves training efficiency by dynamically selecting data samples.
Paper proposes a joint quantile regression for VaR and ES forecasting.
DBNs improve ES and SES estimation for market risk, but tail behavior remains challenging.
A new method for backtesting ES forecasts in banking.
ES-MLP combines Graph-MLP with edge splitting for node classification on both homophilic and heterophilic graphs.
This paper revisits the Bayesian CMA-ES and provides updates for normal Wishart. It emphasizes the difference between a normal and normal inverse Wishart prior. After some computation, we prove that the only difference relies surprisingly in the expected covariance. We prove that the expected covariance should be lower…
Bayesian LSTM model improves VaR and ES forecasting accuracy.
We explore the use of Evolution Strategies (ES), a class of black box optimization algorithms, as an alternative to popular MDP-based RL techniques such as Q-learning and Policy Gradients. Experiments on MuJoCo and Atari show that ES is a viable solution strategy that scales extremely well with the number of CPUs avail…
ES-VAE models skeletal pose trajectories by removing nuisance factors.
This study improves tail risk forecasting by integrating overnight information into semi-parametric models.