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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

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6 results for “GARCH-LSTM”

Forecasting High-Dimensional Covariance Matrices of Asset Returns with Hybrid GARCH-LSTMsq-fin.ST

Hybrid GARCH-LSTM models predict covariance matrices better than GARCH alone.

problem Predicting covariance matrices of high-dimensional asset returns.

method Combining GARCH processes with neural networks to forecast volatilities and correlations.

result The hybrid model outperforms both equally weighted portfolios and univariate GARCH models.

covariance matrices asset returns GARCH-LSTM neural networks volatility prediction

2021-08-25abs ↗pdf ↗

Unified GARCH-Recurrent Neural Network in Financial Volatility Forecastingq-fin.ST

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.

GARCH Recurrent Neural Networks Volatility Forecasting Financial Markets Value-at-Risk

2025-04-13abs ↗pdf ↗

GARCH-Informed Neural Networks for Volatility Prediction in Financial Marketsq-fin.CP

GINN combines GARCH and LSTM for better volatility prediction.

problem Accurate prediction of financial market volatility.

method Physics-Informed Neural Networks (PINN) hybrid model combining GARCH and LSTM.

result GINN outperforms GARCH and LSTM individually in volatility prediction metrics.

volatility prediction GARCH LSTM deep learning financial markets

2024-09-30abs ↗pdf ↗

From GARCH to Neural Network for Volatility Forecastq-fin.ST

Study bridges GARCH and NN models for volatility forecasting.

problem Lack of interaction between GARCH and NN approaches for volatility forecasting.

method Established equivalence between GARCH and NN models, introduced GARCH-NN approach.

result GARCH-NN approach enhances volatility forecasting compared to standalone models.

Volatility Forecasting GARCH Models Neural Networks Equivalence Relationship Stochastic Processes

2024-01-29abs ↗pdf ↗

Stock Volatility Prediction using Time Series and Deep Learning Approachq-fin.CP

The study predicts stock volatility using LSTM and GARCH models.

problem Accurately predicting stock price volatility is challenging.

method Multiple volatility models (GARCH, GJR-GARCH, EGARCH, LSTM) applied to three sectors.

result LSTM outperformed other models in pharma sector volatility prediction.

stock volatility time series deep learning GARCH LSTM

2022-10-05abs ↗pdf ↗

The Hybrid Forecast of S&P 500 Volatility ensembled from VIX, GARCH and LSTM modelsq-fin.TR

Study improves S&P 500 volatility forecasting using hybrid models.

problem Improving accuracy of S&P 500 volatility predictions.

method Hybrid LSTM-GARCH models, including VIX index.

result Hybrid models outperform traditional GARCH model.

S&P 500 Volatility Forecasting GARCH LSTM

2024-07-23abs ↗pdf ↗