On-device research index

arXiv research

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.

168,695 papers · 148 categories

Trend · papers per month

count % of corpus

7 results for “AR-GARCH”

GARCH-UGH: A bias-reduced approach for dynamic extreme Value-at-Risk estimation in financial time seriesstat.AP

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.

VaR estimation dynamic risk management AR-GARCH extreme value financial time series

2021-04-20abs ↗pdf ↗

Understanding Carbon Trade Dynamics: A European Union Emissions Trading System Perspectivestat.AP

Study analyzes EU ETS carbon market dynamics, revealing inefficiencies and anomalies.

problem Inefficiencies and anomalies in EU ETS trading and pricing mechanisms.

method Empirical analysis using AR-GARCH model and weighted network analysis.

result Heterogeneous and sometimes counter-intuitive elasticities in price-volume relationships.

EU ETS carbon market price dynamics network analysis AR-GARCH

2025-10-25abs ↗pdf ↗

Improving Value-at-Risk prediction under model uncertaintyq-fin.RM

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, …

2018-05-10abs ↗pdf ↗

Compounding Effects in Leveraged ETFs: Beyond the Volatility Drag Paradigmq-fin.ST

Leveraged ETFs can outperform their targets in certain market conditions, contrary to the volatility drag hypothesis.

problem The long-term performance decay of leveraged ETFs due to volatility drag.

method Unified framework incorporating AR(1) and AR-GARCH models, continuous-time regime switching, and flexible rebalancing frequencies.

result Return dynamics, including return autocorrelation, volatility clustering, and regime persistence, determine LETF performance.

Leveraged ETFs Volatility Drag Return Dynamics Return Autocorrelation Volatility Clustering

2025-04-28abs ↗pdf ↗

CoVaR with volatility clustering, heavy tails and non-linear dependenceq-fin.RM

The paper estimates CoVaR with various models for financial risk analysis.

problem Estimating conditional value-at-risk with financial time series data.

method Fitting multivariate parametric models and copula functions to capture stylized facts of equity returns.

result Backtesting shows that certain models provide better risk estimates than others.

CoVaR multivariate models financial risk GJR-GARCH copulas

2020-09-22abs ↗pdf ↗

Empirical estimator of diversification quotientq-fin.RM

This paper improves the Diversification Quotient (DQ) for better risk management.

problem Improving portfolio diversification measurement.

method Empirical estimation of DQ using VaR and ES, with asymptotic properties verified.

result Empirical DQ estimators are more robust and have better asymptotic properties.

Diversification Quotient Portfolio Optimization Value at Risk Expected Shortfall Elliptical Distributions

2025-06-25abs ↗pdf ↗

COMEX Copper Futures Volatility Forecasting: Econometric Models and Deep Learningq-fin.MF

The study compares econometric and deep learning models for forecasting COMEX copper futures volatility.

problem Forecasting volatility of COMEX copper futures across different time intervals.

method Econometric models (GARCH, HAR) and deep learning models (RNN, LSTM, GRU) applied to daily and hourly data.

result Deep learning models outperform econometric models in hourly data, but HAR remains the best overall for daily data.

COMEX copper futures volatility forecasting econometric models deep learning GARCH

2024-09-12abs ↗pdf ↗