New method recalibrates VaR for option books, reducing forecast errors.
problem Inaccurate VaR forecasts due to missing operational choices.
method Marking-aware sequential VaR recalibration targeting normalized book-level loss.
result Sequential VaR recalibration improves VaR performance across different markets and options.
RNN-HAR model improves VaR forecasting with long-memory and non-linear dynamics.
problem Efficiently forecasting Value at Risk (VaR) with long-memory and non-linear realized volatility.
method Loss-based generalized Bayesian inference with Sequential Monte Carlo for model estimation and prediction.
result RNN-HAR model consistently outperforms other VaR forecasting models.
VAR-GPs solve continual learning by updating posteriors sequentially.
problem Catastrophic forgetting in sequential learning tasks.
method Sparse inducing point approximations and auto-regressive variational distribution.
result VAR-GPs prevent catastrophic forgetting and outperform baselines.
This paper improves risk control for financial markets by calibrating VaR forecasts using conformal methods.
problem Nonstationary and regime-dependent losses in financial markets.
method Regime-weighted conformal risk control (RWC) for VaR forecasting.
result RWC improves regime-conditional stability in some settings with modest conservativeness changes.
Temporal VAE improves VaR estimation for financial portfolios.
problem Estimating VaR for large asset portfolios in finance.
method Temporal VAE with annealing regularization to avoid posterior collapse.
result Temporal VAE outperforms classical VaR estimation methods on real data.
The purpose of this paper is to propose a time-varying vector autoregressive model (TV-VAR) for forecasting multivariate time series. The model is casted into a state-space form that allows flexible description and analysis. The volatility covariance matrix of the time series is modelled via inverted Wishart and singul…
A new model captures financial asset returns' tail behaviors and outperforms GARCH family.
problem Capturing the dynamic tail behaviors of financial asset returns.
method Combines LSTM with a novel parametric quantile function.
result Out-of-sample forecasts of conditional quantiles or VaR outperform GARCH family.
BAWS adapts window size for financial risk forecasting.
problem Adaptive selection of look-back window for financial risk modeling.
method Data-driven online learning method using bootstrap-based adaptive window selection (BAWS).
result BAWS improves risk forecasting, especially in data with structural changes.
We consider calculation of capital requirements when the underlying economic scenarios are determined by simulatable risk factors. In the respective nested simulation framework, the goal is to estimate portfolio tail risk, quantified via VaR or TVaR of a given collection of future economic scenarios representing factor…
Improved model error correction online with neural networks in 4D-Var.
problem Reconstructing dynamics of imperfectly observed physical models.
method Weak-constraint 4D-Var framework with online neural network training.
result Online model error correction yields more accurate results than offline.
This paper concerns sequential computation of risk measures for financial data and asks how, given a risk measurement procedure, we can tell whether the answers it produces are `correct'. We draw the distinction between `external' and `internal' risk measures and concentrate on the latter, where we observe data in real…
Causality graphs are routinely estimated in social sciences, natural sciences, and engineering due to their capacity to efficiently represent the spatiotemporal structure of multivariate data sets in a format amenable for human interpretation, forecasting, and anomaly detection. A popular approach to mathematically for…
This paper estimates VaR for corn and soybean markets using jump processes.
problem Quantifying potential losses in commodity portfolios under market conditions.
method Modeling VaR for a diversified portfolio of corn and soybean positions with standard Brownian motions and jump processes.
result Compared VaR values in markets with and without jumps, providing insights for risk management.
The paper proposes a new portfolio optimization model that includes VaR risk measure.
problem Computational hardness of portfolio optimization models with VaR as a risk measure.
method Formulated as a Mixed-Integer Quadratic Programming (MIQP) problem, the model minimizes variance with constraints on expected return and VaR.
result The proposed Mean-Variance-VaR portfolios outperform traditional Mean-Variance and Mean-VaR portfolios in out-of-sample performance.
Several well-established benchmark predictors exist for Value-at-Risk (VaR), a major instrument for financial risk management. Hybrid methods combining AR-GARCH filtering with skewed-t residuals and the extreme value theory-based approach are particularly recommended. This study introduces yet another VaR predictor, …
Study improves dividend discount model using VAR process.
problem Improving dividend discount models for better predictions.
method Introduced a Gordon growth model based on Vector Autoregressive Process (VAR).
result Two Propositions related to the new model.
This paper compares VaR estimation methods under tail misspecification, finding importance sampling underestimates VaR.
problem Tail misspecification in VaR estimation.
method Importance sampling and moment-based VaR bracketing.
result Importance sampling underestimates VaR under heavy-tailed returns, while moment-based methods are robust.
Study improves financial risk assessment using ARMA-APARCH-EVT models with HACs.
problem Improving risk assessment in financial portfolios.
method ARMA-APARCH-EVT-HAC model for volatility and extreme value forecasting.
result Empirical analysis shows the model's effectiveness in international stock market data.
Paper proposes a new sparsity scheme for high-dimensional VAR models.
problem Estimation of high-dimensional VAR models with sparsity assumptions.
method Regularized estimation procedures for sparse VAR models.
result Threholding extends consistency properties of regularized estimators.
This thesis examines the accuracy of scaling VaR estimates for longer holding periods.
problem The accuracy of VaR estimates for longer holding periods using the square root of time rule.
method Examined VaR scaling for longer holding periods using empirical analysis.
result Scaling can provide good estimates of VaR but may lead to significant losses over time.
Linear attention in Transformers can be interpreted as dynamic VAR models.
problem Misalignment between Transformers and autoregressive forecasting objectives.
method Interpreting linear attention as VAR, rearranging MLP, attention, and flow.
result SAMoVAR improves performance, interpretability, and efficiency.
Investment strategy for DC pension plan with inflation risk and tail VaR constraint.
problem Maximizing terminal wealth for pension member with tail VaR constraint.
method Lagrange method and quantile optimization techniques.
result Optimal investment strategy and output in closed-form derived.
A new risk measure, the lambda value at risk (Lambda VaR), has been recently proposed from a theoretical point of view as a generalization of the value at risk (VaR). The Lambda VaR appears attractive for its potential ability to solve several problems of the VaR. In this paper we propose three nonparametric backtestin…
VaR-CPO optimizes VaR-constrained RL problems with conservative policy updates.
problem Optimizing VaR-constrained reinforcement learning problems.
method Combines Cantelli's inequality and trust-region framework for efficient and conservative optimization.
result Achieves zero constraint violations during training in feasible environments.
Pricing and hedging rainbow options using Bayesian MS-VAR process.
problem Pricing and hedging rainbow options under varying economic conditions.
method Bayesian Markov-Switching Vector Autoregressive (MS-VAR) process to model regime-switching economic variables.
result Model provides a simpler and more economic variable-dependent approach for rainbow options pricing and hedging.
Paper investigates Lambda Value-at-Risk under ambiguity and risk sharing.
problem Investigates Lambda Value-at-Risk under ambiguity and risk sharing.
method Establishes equivalence of robust ΛVaR and traditional ΛVaR under ambiguity sets, analyzes properties, derives explicit formulas, and explores risk sharing. result Unified and extended the concept of Value-at-Risk under ambiguity, derived explicit formulas for specific ambiguity sets, and explored risk sharing.
The study challenges the reliability of VaR due to market randomness.
problem Reliability and accuracy of VaR predictions are compromised by market randomness.
method Introduces market-based probabilities of price and return, dependent on trade values and volumes.
result Market-based price volatility is more accurate than frequency-based VaR predictions.
Study uses copulas and DCC-GARCH for multivariate risk analysis of VaR and CVaR.
problem Multivariate risk analysis for Value at Risk (VaR) and Conditional Value at Risk (CoVaR).
method Copulas and Dynamic Conditional Correlation (DCC)-GARCH models applied to historical financial data.
result Comparison of different copula families for goodness-of-fit and effectiveness.
Introduces Lambda Expected Shortfall as a risk measure generalizing ES.
problem Lack of a comprehensive risk measure that generalizes ES and Lambda-VaR.
method Introduces Lambda-ES, a new risk measure with explicit formula and properties.
result Lambda-ES is the smallest quasi-convex and law-invariant risk measure dominating Lambda-VaR.
Bayesian approach improves portfolio optimization using VaR and CVaR.
problem Optimizing portfolio weights using VaR and CVaR for risk management.
method Bayesian perspective, posterior predictive distribution, observed data.
result Bayesian approach yields more accurate optimal portfolio weights.
New method uses G-expectation for financial risk measurement.
problem Measuring uncertainty in financial time series.
method Introducing G-normal distribution, applying max-mean estimators, and using autoregressive models.
result G-VaR model outperforms other VaR predictors in risk prediction.
Paper proposes a copula method to generate unfavorable VaR scenarios.
problem Creating unfavorable VaR scenarios for insurance models.
method Patchwork copulas to create unfavorable VaR scenarios with given marginal distributions.
result Demonstrated with a 19-dimensional real-life insurance losses data set.
Bayesian VAR model discovers Granger causality with uncertainty-aware binary graphs.
problem Discovering Granger causal relations from multivariate time-series data.
method Bayesian Vector AutoRegression with factorised Granger-Causal Graphs.
result Our method achieves better performance, especially in low-data regimes.
New property shows VaR subadditivity for comonotonic loss variables.
problem Understanding VaR subadditivity and comonotonicity.
method Analyzes VaR subadditivity and comonotonicity relationship.
result VaR subadditivity holds for comonotonic loss variables.
Investigates VaR behavior for sums of one-sided random variables, showing impossibilities and conditions for super-additivity.
problem Investigates the behavior of Value-at-Risk (VaR) for sums of one-sided random variables.
method Analyzes the extremal aggregation behavior of VaR, introduces structural conditions for super-additivity.
result Characterizes when VaR is fully super-additive and provides unified framework for various dependence structures.
In this paper, we introduce two alternative extensions of the classical univariate Value-at-Risk (VaR) in a multivariate setting. The two proposed multivariate VaR are vector-valued measures with the same dimension as the underlying risk portfolio. The lower-orthant VaR is constructed from level sets of multivariate di…
Value at risk (VaR) is a risk measure that has been widely implemented by financial institutions. This paper measures the correlation among asset price changes implied from VaR calculation. Empirical results using US and UK equity indexes show that implied correlation is not constant but tends to be higher for events i…
Study bounds VAR model's circuit complexity, showing it's limited to TC^0 circuits.
problem Understanding the limitations of the Visual AutoRegressive model.
method Established circuit complexity bounds for the VAR model.
result VAR model is equivalent to a TC^0 threshold circuit with hidden dimension ≤ O(n).
GARCH-UGH improves VaR estimation for financial risk management.
problem Dynamic estimation of extreme VaR in financial time series.
method AR-GARCH filtering followed by a bias-reduced extreme value estimator.
result GARCH-UGH estimates are more accurate than conventional methods.
Algorithm finds near-optimal VaR portfolios using MILP, improving risk management.
problem Computing optimal VaR portfolios is hard due to non-convexity and combinatorial nature.
method Formulates VaR portfolio problem as MILP, uses alternate formulations for guarantees.
result Near-optimal VaR portfolios with near-optimality guarantees.
This paper studies a Value-at-Risk (VaR)-regulated optimal portfolio problem of the equity holders of a participating life insurance contract. In a setting with unhedgeable mortality risk and complete financial market, the optimal solution is given explicitly for contracts with mortality risk using a martingale approac…
Bayesian econometrics improves nowcasting during pandemics.
problem Improving nowcasting during extreme economic events like pandemics.
method Bayesian econometric methods using non-parametric mixed frequency VARs with additive regression trees.
result Significant improvements in nowcasting performance compared to linear models.
The paper explores capital allocation using Euler formula with VaR and ES, revealing non-monotonicity and providing estimation methods.
problem Non-monotonicity in VaR-based capital allocation and the need for consistent risk measures.
method Use of Euler formula, Value-at-Risk (VaR), Expected shortfall (ES), simulation, and Markov chain Monte Carlo.
result Capital allocation with VaR is not monotonous, and consistent risk measures are crucial.
Investigates diversification quotient based on VaR and ES for portfolio models.
problem Quantifying diversification of portfolios using VaR and ES.
method Introduced and analyzed DQ based on VaR and ES for elliptical and MRV distributions.
result Explicit formulas and portfolio optimization problems for VaR and ES DQ are derived.
Multivariate time-series modeling and forecasting is an important problem with numerous applications. Traditional approaches such as VAR (vector auto-regressive) models and more recent approaches such as RNNs (recurrent neural networks) are indispensable tools in modeling time-series data. In many multivariate time ser…
A new model forecasts Value-at-Risk using NIG distribution and dynamic scores.
problem Forecasting Value-at-Risk (VaR) in financial markets.
method Proposes a parametric forecasting model based on the normal inverse Gaussian distribution (NIG) incorporating intraday information.
result The model outperforms traditional GARCH models, especially in high-risk scenarios.
The paper develops a new model-free formula for option initial margins.
problem Calculating initial margins for option portfolios is complex and risky.
method The authors derive a new approximation formula for VaR without assuming a model.
result The new formula performs better than existing methods in simulations.
Optimal Volt/VAR control rules designed using deep learning.
problem Designing optimal Volt/VAR control rules for DERs to regulate voltage fluctuations.
method Formulated as a deep learning problem, where a DNN emulates Volt/VAR dynamics and optimizes rule parameters.
result DNN-based optimization outperforms MINLP in efficiency and accuracy.