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.
Reverse sensitivity analysis for risk models under various stresses.
DBNs improve ES and SES estimation for market risk, but tail behavior remains challenging.
New AI models improve financial hedging by reducing shortfall and tail risk.
Machine learning improves financial stress testing in Indian markets.
Study on systemic risk in European insurance sector, showing insurer connections during stress.
LLM generates coherent macroeconomic stress scenarios for portfolio risk assessment.
It is well known that Expected Shortfall (also called Average Value-at-Risk) is a convex risk measure, i. e. Expected Shortfall of a convex linear combination of arbitrary risk positions is not greater than a convex linear combination with the same weights of Expected Shortfalls of the same risk positions. In this shor…
Expectile bears some interesting properties in comparison to the industry wide expected shortfall in terms of assessment of tail risk. We study the relationship between expectile and expected shortfall using duality results and the link to optimized certainty equivalent. Lower and upper bounds of expectile are derived …
We refine Expected Shortfall by controlling different tail portions, offering tailored risk assessments.
We offer a simplified proof for Expected Shortfall's dual representation.
RL-CVaR model improves insurance reserving under economic stress.
The expectile can be considered as a generalization of quantile. While expected shortfall is a quantile based risk measure, we study its counterpart -- the expectile based expected shortfall -- where expectile takes the place of quantile. We provide its dual representation in terms of Bochner integral. Among other prop…
Investigates a new measure PELVE_n for risk assessment.
This paper introduces a new systemic risk measure, JMES, and its associated contribution measures.
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…
The problem of estimation error of Expected Shortfall is analyzed, with a view of its introduction as a global regulatory risk measure.
Investigates risk measures for DC pension decumulation.
Submodularity is studied for convex risk measures, including Expected Shortfall.
Introduces Lambda Expected Shortfall as a risk measure generalizing ES.
A new tail-shape index based on Value at Risk and Expected Shortfall.
Financial institutions have to allocate so-called "economic capital" in order to guarantee solvency to their clients and counter parties. Mathematically speaking, any methodology of allocating capital is a "risk measure", i.e. a function mapping random variables to the real numbers. Nowadays "value-at-risk", which is d…
Study shows equivalence of four risk constraints in non-concave optimization problems.
New method optimizes risk estimation for financial losses.
This paper introduces new risk measures for evaluating losses with varying time horizons.
Study improves accuracy of risk measures using advanced algorithms.
New SigSwap model for path-dependent financial risk.
We present the Shortfall Deviation Risk (SDR), a risk measure that represents the expected loss that occurs with certain probability penalized by the dispersion of results that are worse than such an expectation. SDR combines Expected Shortfall (ES) and Shortfall Deviation (SD), which we also introduce, contemplating t…
We propose a new backtesting framework for Expected Shortfall that could be used by the regulator. Instead of looking at the estimated capital reserve and the realised cash-flow separately, one could bind them into the secured position, for which risk measurement is much easier. Using this simple concept combined with …
CAESar improves risk forecasting by combining VaR and ES estimates.
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.
In the world of modern financial theory, portfolio construction has traditionally operated under at least one of two central assumptions: the constraints are derived from a utility function and/or the multivariate probability distribution of the underlying asset returns is fully known. In practice, both the performance…
For a linear combination of random variables, fix some confidence level and consider the quantile of the combination at this level. We are interested in the partial derivatives of the quantile with respect to the weights of the random variables in the combination. It turns out that under suitable conditions on the join…
The contour map of estimation error of Expected Shortfall (ES) is constructed. It allows one to quantitatively determine the sample size (the length of the time series) required by the optimization under ES of large institutional portfolios for a given size of the portfolio, at a given confidence level and a given esti…
A new method tests Expected Shortfall by analyzing both duration and severity of VaR violations.
Combines VaR and ES forecasts for cryptocurrency market risk management.
In this paper, we generalize the parametric delta-VaR method from portfolios with normally distributed risk factors to portfolios with elliptically distributed ones. We treat both the expected shortfall and the Value-at-Risk of such portfolios. Special attention is given to the particular case of a multivariate t-distr…
In this note, we comment on the relevance of elicitability for backtesting risk measure estimates. In particular, we propose the use of Diebold-Mariano tests, and show how they can be implemented for Expected Shortfall (ES), based on the recent result of Fissler and Ziegel (2015) that ES is jointly elicitable with Valu…
New risk measures adjust for tail risk inadequacies.
Study asymptotic properties of generalized shortfall risk measures for heavy-tailed risks.
Optimal retirement timing and consumption under shortfall risk management
We show that coherent risk measures are ineffective in curbing the behaviour of investors with limited liability or excessive tail-risk seeking behaviour if the market admits statistical arbitrage opportunities which we term -arbitrage for a risk measure . We show how to determine analytically whether such -ar…
We introduce and compare new variability measures based on risk quantiles.
We discuss the coherence properties of Expected Shortfall (ES) as a financial risk measure. This statistic arises in a natural way from the estimation of the "average of the 100p % worst losses" in a sample of returns to a portfolio. Here p is some fixed confidence level. We also compare several alternative representat…
New axioms justify ES without NRC, linking it to mean-ES portfolio selection.
This paper attempts to provide a decision-theoretic foundation for the measurement of economic tail risk, which is not only closely related to utility theory but also relevant to statistical model uncertainty. The main result is that the only risk measures that satisfy a set of economic axioms for the Choquet expected …
Efficiently simulates risk budgeting portfolios using novel algorithms.
Analyzes how uncertainty in financial networks affects stability.
This paper is dedicated to the consistency of systemic risk measures with respect to stochastic dependence. It compares two alternative notions of Conditional Value-at-Risk (CoVaR) available in the current literature. These notions are both based on the conditional distribution of a random variable Y given a stress eve…