K-means algorithm improves financial market risk prediction accuracy.
problem High error rate and low precision in financial market risk prediction.
method Applied K-means algorithm in machine learning to financial market risk forecasting.
result Achieved a 94.61% accuracy rate in financial market risk prediction.
A new method forecasts financial tail risks by combining and weighting quantiles.
problem Reducing uncertainty in financial tail risk forecasting.
method Two-step procedure: quantile combination followed by ES computation.
result The proposed framework outperforms individual models and simple approaches.
Study enhances financial forecasting with machine learning and fuzzy MCDM.
problem Increasing financial uncertainty and market complexity.
method Integrates machine learning (XGBoost, LSTM, GNN) and intuitionistic fuzzy MCDM.
result High forecasting accuracy with low MAPE and narrow confidence intervals.
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.
Hybrid GARCH-GRU model improves volatility forecasting for financial assets.
problem Improving volatility and risk forecasting for financial assets.
method Combining GARCH models with GRU neural networks.
result Hybrid models produce more accurate volatility forecasts.
New method forecasts systemic risk with improved precision.
problem Improving the estimation of systemic risk measures.
method De-volatilizing observations and using extreme value theory for forecasting.
result Valid MES forecasts with good coverage in simulations and empirical applications.
Optimizes forecast distributions for financial risk management.
problem Improving risk management through better forecast distributions.
method Optimizes forecast distributions using scoring rules relevant to financial risk management.
result Tail-focused predictive distributions yield better outcomes in hedging strategies involving VIX futures.
Dynamic models improve CoVaR forecasts for financial system risks.
problem Improving forecasts of systemic risk measures like CoVaR.
method Two-step M-estimator using bivariate scoring functions for VaR and CoVaR.
result CoCAViaR models generate superior CoVaR predictions.
The study evaluates financial risk using copulas and statistical tests.
problem Validating bivariate forecasts in risk evaluation.
method Using copulas to characterize dependencies, applying statistical tests to validate forecasts, removing heteroskedasticity.
result A Student copula accurately describes financial time series dependencies.
The paper introduces a new method for forecasting financial risk using quantile-based modeling.
problem Forecasting Value-at-Risk (VaR) and Expected Shortfall (ES) for financial returns.
method Semiparametric approach using restricted quantile regression to model the conditional scale of financial returns.
result The method provides robust, distribution-free estimates of extreme losses and captures risk dynamics.
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.
The book chapter discusses tail risk analysis for financial data using extreme value statistics.
problem Serial dependence in financial time series complicates tail risk assessment.
method The approach involves unconditional and conditional quantile forecasting.
result Serial dependence impacts multivariate tail dependence.
Model predicts default risk based on company's financial forecasts and credit conditions.
problem Estimating the risk of a company defaulting on its financial obligations.
method Developed an equilibrium model linking interest rates to corporate performance and credit supply.
result Estimates idiosyncratic default risk and provides forward-looking probability of default (PD).
Deep neural networks forecast financial return distributions accurately.
problem Forecasting probability distributions of financial returns.
method Used 1D CNN and LSTM architectures with custom loss functions to optimize distribution parameters.
result LSTM with skewed Student's t distribution outperformed classical models in multiple evaluation metrics.
Combines VaR and ES forecasts for cryptocurrency market risk management.
problem Improving tail risk forecasts in financial markets.
method Proposes semiparametric and parametric combination frameworks.
result Combined forecasts outperform individual VaR and ES forecasts.
Paper proposes a hybrid model for VaR forecasting using SVR, GARCH, and KDE.
problem Inaccurate VaR estimates due to time-varying volatility and distributional characteristics.
method SVR-GARCH-KDE hybrid model combining nonlinear and nonparametric approaches.
result The SVR-GARCH-KDE hybrid outperforms benchmark models in VaR forecasting, especially for longer horizons.
Conditional forecasts of risk measures play an important role in internal risk management of financial institutions as well as in regulatory capital calculations. In order to assess forecasting performance of a risk measurement procedure, risk measure forecasts are compared to the realized financial losses over a perio…
Neural Lévy model improves risk and density forecasting for financial returns.
problem Financial returns exhibit heavy tails, volatility clustering, and jumps.
method Proposes a neural Lévy jump-diffusion framework that learns conditional drift, diffusion, jump intensity, and size distribution.
result Demonstrates improved calibration, sharper tail control, and risk reduction.
Improved Hawkes model forecasts extreme financial returns more accurately.
problem Forecasting extreme tail events in financial log-returns.
method 2T-POT Hawkes model with multiple exceedance thresholds.
result 2T-POT Hawkes model outperforms GARCH-EVT model in risk forecasting.
A new model forecasts financial risks using multiple realized measures.
problem Forecasting financial risks using multiple realized measures.
method Developed a semi-parametric joint VaR and ES forecasting framework using realized measures.
result The proposed model outperformed other models in forecasting financial risks.
Quantum machine learning boosts financial forecasting accuracy.
problem Churn prediction and credit risk assessment in finance.
method Used quantum and classical Determinantal Point Processes for churn prediction, and quantum neural networks for credit risk assessment.
result Significant improvement in precision for churn prediction (6% increase). Quantum models match classical performance with fewer parameters.
New vine copula method forecasts portfolio risk measures robust to market downturns.
problem Inaccurate risk measure estimation for financial portfolios due to lack of cross-dependency capture.
method Combines vine copulas with ARMA-GARCH models for marginal risk estimation.
result Portfolio is robust to American market downturns but not European market.
Foundation models improve volatility forecasting in finance.
problem Improving volatility forecasting in financial markets.
method Evaluation of TimesFM model, incremental fine-tuning, comparison with econometric benchmarks.
result Incremental fine-tuning improves forecast accuracy and outperforms traditional models.
Online surveillance detects systemic risk in financial markets.
problem Detecting and monitoring systemic risk in financial markets.
method Online monitoring procedures for multiple series, controlling for false rejections.
result Procedures allow timely detection of financial distress.
Improved forecasting of financial risk using Diffusion-Copula framework.
problem Capturing complex, asymmetric dependence structures in financial markets.
method Explicitly decouples marginal distribution learning from dependence structure using Mixture Density Networks and Classification-Diffusion Copula.
result Superior performance in forecasting systemic extremes of marginal and joint events.
FCOC framework improves financial volatility forecasting.
problem Tackles dual challenges of feature fidelity and model responsiveness in financial volatility forecasting.
method Synergizes fractal feature extraction and dynamic chaotic oscillation processing.
result Demonstrates profound and generalizable impact on S\&P 500 and DJI datasets.
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.
The paper develops a method to forecast financial risk multiple steps ahead using quantile time series and historical simulation.
problem Forecasting financial risk multiple steps ahead with accurate estimation of Value-at-Risk (VaR) and Expected Shortfall (ES).
method Quantile-based, semi-parametric historical simulation estimation of VaR and ES models, using quantile loss function and resampling.
result The proposed method accurately forecasts VaR and ES one and multiple steps ahead, superior to existing methods.
Enhances Transformers for better risk assessment in finance.
problem Transformer models lack sensitivity to extreme financial losses.
method Integrates Loss-at-Risk function with Value at Risk (VaR) and Conditional Value at Risk (CVaR).
result Improves risk prediction and management in financial datasets.
Enhanced multivariate GARCH model using LSTM for better volatility forecasting.
problem Limitations of traditional multivariate GARCH in capturing persistent volatility and co-movement.
method Integrates deep learning (LSTM) into multivariate GARCH models to capture nonlinear and dynamic dependence structures.
result Superior out-of-sample portfolio risk forecast compared to traditional methods.
Bayesian framework forecasts financial tail risks using realized volatility and nonlinear thresholds.
problem Forecasting financial tail risks using realized volatility and nonlinear thresholds.
method Bayesian Markov Chain Monte Carlo method for model estimation; nonlinear threshold regression specification.
result The proposed framework produces competitive tail risk forecasts compared to GARCH and Realized-GARCH models.
Bayesian framework improves financial risk management and compliance.
problem Uncertainty in financial risk forecasting and compliance.
method Integrated Bayesian analytics framework for precise uncertainty quantification.
result Proposed DLM model produces more accurate VaR estimates compared to baseline models.
M2VN forecasts financial volatility by fusing time series data with news embeddings.
problem Forecasting financial volatility with unstructured news data.
method Combines deep neural networks with open-source market features and news embeddings.
result M2VN outperforms existing models in financial volatility forecasting.
Unified GARCH-NN models improve financial volatility forecasting.
problem Improving financial volatility forecasting accuracy and efficiency.
method Embedding GARCH dynamics within recurrent neural networks (GRU and LSTM).
result Unified GARCH-NN models outperform classical GARCH and hybrid methods.
TimeMixer predicts global financial asset volatility, excelling in short-term forecasts.
problem Predicting volatility in global financial markets is challenging due to complexity and non-linear dynamics.
method Uses TimeMixer, a multiscale-mixing model for forecasting across different scales.
result TimeMixer performs exceptionally well in short-term volatility forecasting but less so in longer-term predictions.
Enhanced financial forecasting with supervised autoencoders for S&P 500 and cryptocurrencies.
problem Improving investment strategy performance in financial markets.
method Supervised autoencoders with noise augmentation and triple barrier labeling.
result Supervised autoencoders with balanced parameters significantly boost strategy effectiveness.
Paper proposes Multi-Transformer for more accurate stock volatility forecasts.
problem Accurate equity risk models needed for effective risk management.
method Introduces Multi-Transformer neural network architecture, adapted from Transformer models.
result Empirical results show Multi-Transformer leads to more accurate risk measures.
Enhanced financial forecasting using supervised autoencoders with noise augmentation and triple labeling.
problem Improving investment strategy performance on noisy financial data.
method Supervised autoencoders with noise augmentation and triple barrier labeling.
result Supervised autoencoders with balanced noise augmentation and bottleneck size significantly boost strategy effectiveness.
FinStressTS creates synthetic benchmarks for financial forecasting, revealing model weaknesses.
problem Limited failure attribution in real-world financial benchmarks.
method Synthetic benchmark with 30 diagnostic environments linked to six mechanism families.
result Model performance varies by mechanism type, with autoregressive models often outperforming Transformers.
The realized GARCH framework is extended to incorporate the two-sided Weibull distribution, for the purpose of volatility and tail risk forecasting in a financial time series. Further, the realized range, as a competitor for realized variance or daily returns, is employed in the realized GARCH framework. Further, sub-s…
Model forecasts market structure from financial networks using machine learning.
problem Predicting market correlation structure from financial networks.
method Dynamic Asset Graph (DAG), Dynamic Minimal Spanning Tree (DMST), Dynamic Threshold Networks (DTN).
result Model improves market structure forecasting by up to 40% over benchmarks.
Paper proposes a joint quantile regression for VaR and ES forecasting.
problem Forecasting Value at Risk (VaR) and Expected Shortfall (ES) of multiple assets simultaneously.
method Multivariate quantile regression framework with time-varying process for VaR and ES.
result The proposed method outperforms other models in risk measure forecasts.
Study shows awareness of reflexivity improves LLMs' financial forecasting accuracy.
problem Improving LLMs' ability to forecast financial markets during boom-bust cycles.
method Evaluated three LLMs under four conditions of reflexivity awareness in two market episodes.
result Reflexivity awareness improves forecasting accuracy differently across models and contexts.
QBVAR improves oil price forecasting across quantiles, especially for downside risk.
problem Forecasting oil prices across different quantiles for better risk assessment.
method Quantile Bayesian Vector Autoregression (QBVAR) model.
result QBVAR improves median forecasts by 2-5% and left-tail forecast improvements of 10-25% during crisis episodes.
We investigate the forecasting ability of the most commonly used benchmarks in financial economics. We approach the usual caveats of probabilistic forecasts studies -small samples, limited models and non-holistic validations- by performing a comprehensive comparison of 15 predictive schemes during a time period of over…
DBNs improve VaR forecasting compared to traditional models, but SVaR forecasts are conservative.
problem Forecasting VaR and SVaR using dynamic Bayesian networks.
method DBN framework applied to S&P 500 index returns, comparing to autoregressive models and historical simulation.
result DBNs achieve comparable VaR forecasting accuracy to historical simulation models, but SVaR forecasts remain conservative.
Transformer learns CoVaR from financial news, improving systemic risk forecasts.
problem Quantifying systemic financial risk using conditional Value-at-Risk (CoVaR).
method Transformer-based approach integrating financial news articles with market data.
result Transformer CoVaR improves out-of-sample forecasts and identifies market stress periods.
A new method for backtesting ES forecasts in banking.
problem Designing a model-free backtesting procedure for Expected Shortfall forecasts.
method Use e-values and e-processes to introduce backtest e-statistics for VaR and ES.
result The proposed method can be applied to various risk measures and statistical quantities.