Risk Management & Tail Risk

Risk management research divides into measurement and mechanism. The measurement literature — VaR and expected shortfall estimation, extreme value theory, drawdown analysis — asks how to quantify what can go wrong in a portfolio. The mechanism literature — systemic risk, contagion networks, stress testing — asks how losses propagate between institutions and markets.

The practical test for measurement papers is backtesting honesty: a VaR model is only as good as its violation rate and the independence of its violations, and papers that report Kupiec or Christoffersen tests on out-of-sample data outrank those that stop at in-sample fit. For tail-risk work specifically, be wary of estimators tuned to one crisis; the ranking below rewards papers validated across multiple stress regimes. This is also where regulation (Basel’s shift from VaR to expected shortfall) keeps the academic work unusually connected to practice.

Explainers from the corpus: risk measures beyond VaR →

Dynamic Risk in the U.S. Banking System: An Analysis of Sentiment, Policy Shocks, and Spillover Effects

The 2023 U.S. banking crisis propagated not through direct financial linkages but through a high-frequency, information-based contagion channel. This paper moves beyond exploration analysis to test the “too-similar-to-fail” hypothesis, arguing that risk spillovers were driven by perceived similariti

Learning to Hedge Swaptions

This paper investigates the deep hedging framework, based on reinforcement learning (RL), for the dynamic hedging of swaptions, contrasting its performance with traditional sensitivity-based rho-hedging. We design agents under three distinct objective functions (mean squared error, downside risk, an

General Equilibrium Amplification and Crisis Vulnerability: Cross-Crisis Evidence from Global Banks

This paper develops a continuous framework for analyzing financial contagion that incorporates both geographic proximity and interbank network linkages. The framework characterizes stress propagation through a master equation whose solution admits a Feynman-Kac representation as expected cumulative

Distributional regression for seasonal data: an application to river flows

Risk assessment in casualty insurance, such as flood risk, traditionally relies on extreme-value methods that emphasizes rare events. These approaches are well-suited for characterizing tail risk, but do not capture the broader dynamics of environmental variables such as moderate or frequent loss ev

Robust and Efficient Deep Hedging via Linearized Objective Neural Network

Deep hedging represents a cutting-edge approach to risk management for financial derivatives by leveraging the power of deep learning. However, existing methods often face challenges related to computational inefficiency, sensitivity to noisy data, and optimization complexity, limiting their practic

Utilizing Effective Dynamic Graph Learning to Shield Financial Stability from Risk Propagation

Financial risks can propagate across both tightly coupled temporal and spatial dimensions, posing significant threats to financial stability. Moreover, risks embedded in unlabeled data are often difficult to detect. To address these challenges, we introduce GraphShield, a novel approach with three k

Tail Risk Analysis for Financial Time Series

This book chapter illustrates how to apply extreme value statistics to financial time series data. Such data often exhibits strong serial dependence, which complicates assessment of tail risks. We discuss the two main approches to tail risk estimation, unconditional and conditional quantile forecast

Distributionally Robust Optimization as a Scalable Framework to Characterize Extreme Value Distributions

The goal of this paper is to develop distributionally robust optimization (DRO) estimators, specifically for multidimensional Extreme Value Theory (EVT) statistics. EVT supports using semi-parametric models called max-stable distributions built from spatial Poisson point processes. While powerful, t

Analyzing selected cryptocurrencies spillover effects on global financial indices: Comparing risk measures using conventional and eGARCH-EVT-Copula approaches

This study examines the interdependence between cryptocurrencies and international financial indices, such as MSCI World and MSCI Emerging Markets. We compute the value at risk, expected shortfall (ES), and range value at risk (RVaR) and investigate the dynamics of risk spillover. We employ a hybrid

Financial Default Prediction via Motif-preserving Graph Neural Network with Curriculum Learning

User financial default prediction plays a critical role in credit risk forecasting and management. It aims at predicting the probability that the user will fail to make the repayments in the future. Previous methods mainly extract a set of user individual features regarding his own profiles and beha

Tail risk forecasting with semi-parametric regression models by incorporating overnight information

This research incorporates realized volatility and overnight information into risk models, wherein the overnight return often contributes significantly to the total return volatility. Extending a semi-parametric regression model based on asymmetric Laplace distribution, we propose a family of RES-CA

Attention-based Dynamic Multilayer Graph Neural Networks for Loan Default Prediction

Whereas traditional credit scoring tends to employ only individual borrower- or loan-level predictors, it has been acknowledged for some time that connections between borrowers may result in default risk propagating over a network. In this paper, we present a model for credit risk assessment leverag

TransCORALNet: A Two-Stream Transformer CORAL Networks for Supply Chain Credit Assessment Cold Start

This paper proposes an interpretable two-stream transformer CORAL networks (TransCORALNet) for supply chain credit assessment under the segment industry and cold start problem. The model aims to provide accurate credit assessment prediction for new supply chain borrowers with limited historical data

Optimal probabilistic forecasts for risk management

This paper explores the implications of producing forecast distributions that are optimized according to scoring rules that are relevant to financial risk management. We assess the predictive performance of optimal forecasts from potentially misspecified models for i) value-at-risk and expected shor

Optimal collective investment under regulatory uncertainty

We study optimal collective investment subject to a Value-at-Risk (VaR)-type constraint that accounts for uncertainty about future regulatory requirements. The constraint incorporates several possible wealth thresholds and imposes a minimum weighted average probability of meeting them. We characteri

Incorporating data drift to perform survival analysis on credit risk

Survival analysis has become a standard approach for modelling time to default by time-varying covariates in credit risk. Unlike most existing methods that implicitly assume a stationary data-generating process, in practise, mortgage portfolios are exposed to various forms of data drift caused by ch

Robust Bayesian Dynamic Programming for On-policy Risk-sensitive Reinforcement Learning

We propose a novel framework for risk-sensitive reinforcement learning (RSRL) that incorporates robustness against transition uncertainty. We define two distinct yet coupled risk measures: an inner risk measure addressing state and cost randomness and an outer risk measure capturing transition dynam

Black-Litterman and ESG Portfolio Optimization

We introduce a simple portfolio optimization strategy using ESG data with the Black-Litterman allocation framework. ESG scores are used as a bias for Stein shrinkage estimation of equilibrium risk premiums used in assigning Black-Litterman asset weights. Assets are modeled as multivariate affine nor

Robust MCVaR Portfolio Optimization with Ellipsoidal Support and Reproducing Kernel Hilbert Space-based Uncertainty

This study introduces a portfolio optimization framework to minimize mixed conditional value at risk (MCVaR), incorporating a chance constraint on expected returns and limiting the number of assets via cardinality constraints. A robust MCVaR model is presented, which presumes ellipsoidal support for

Approaches for modelling the term-structure of default risk under IFRS 9: A tutorial using discrete-time survival analysis

Under the International Financial Reporting Standards (IFRS) 9, credit losses ought to be recognised timeously and accurately. This requirement belies a certain degree of dynamicity when estimating the constituent parts of a credit loss event, most notably the probability of default (PD). It is noto

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