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.

169,341 papers · 148 categories

Trend · papers per month

count % of corpus

12 results for “GARCH-family”

Stylized Facts and Simulating Long Range Financial Dataq-fin.ST

New method simulates stock prices with long-range data accurately.

problem Simulating long-range daily stock-price data accurately.

method Developed an R-program to simulate data that closely matches stylized facts.

result The program accurately reproduces changes in unconditional variance.

stock prices financial data stylized facts GARCH-family unconditional variance

2016-12-15abs ↗pdf ↗

Variational Inference for GARCH-family Modelsstat.ML

Variational Inference shows promise for Bayesian GARCH model estimation.

problem Bayesian estimation of GARCH-family models using Monte Carlo sampling.

method Variational Inference as an alternative to Monte Carlo sampling.

result Variational Inference is a reliable and competitive method for Bayesian learning in GARCH-like models.

Variational Inference GARCH models Bayesian estimation Monte Carlo sampling S&P 500

2023-10-05abs ↗pdf ↗

Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learningq-fin.RM

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.

quantile regression financial asset returns tail dynamics LSTM GARCH

2020-10-16abs ↗pdf ↗

Time-varying volatility in Bitcoin market and information flow at minute-level frequencyq-fin.ST

The study analyzes Bitcoin market volatility using GARCH models and external information.

problem Modeling time-varying volatility in Bitcoin market.

method Combines GARCH models with a mixture of distribution hypothesis using external information.

result The simplest GARCH(1,1) model performs best in predicting volatility with external signal.

Bitcoin volatility GARCH Mixture of Distribution external information

2020-04-01abs ↗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 ↗

Quantile Correlations: Uncovering temporal dependencies in financial time seriesq-fin.GN

We conduct an empirical study using the quantile-based correlation function to uncover the temporal dependencies in financial time series. The study uses intraday data for the S\&P 500 stocks from the New York Stock Exchange. After establishing an empirical overview we compare the quantile-based correlation function to…

2015-07-17abs ↗pdf ↗

Time series copula models using d-vines and v-transformsstat.ME

New copula models capture volatility and directionality in financial time series.

problem Modeling financial return series with volatility and serial correlation.

method Stationary d-vine copula processes with v-transforms for stochastic volatility and directionality.

result Models can rival and sometimes outperform GARCH family models.

financial time series copula models volatility directionality d-vine

2020-06-19abs ↗pdf ↗

Order book dynamics in liquid markets: limit theorems and diffusion approximationsq-fin.TR

We propose a model for the dynamics of a limit order book in a liquid market where buy and sell orders are submitted at high frequency. We derive a functional central limit theorem for the joint dynamics of the bid and ask queues and show that, when the frequency of order arrivals is large, the intraday dynamics of the…

2012-02-28abs ↗pdf ↗

Price change prediction of ultra high frequency financial data based on temporal convolutional networkq-fin.CP

Paper uses TCN with attention to predict UHF stock price changes.

problem Predicting discrete dynamic distribution of UHF stock price changes.

method Classified price changes, used TCN with attention mechanism.

result TCN and TCN (attention) models outperform GARCH and LSTM models.

TCN attention UHF stock price prediction dynamic distribution

2021-07-01abs ↗pdf ↗

Conditional heteroskedasticity in crypto-asset returnsq-fin.ST

This study examines crypto-asset returns and finds strong evidence of non-Gaussian innovations.

problem Examining the time series properties of cryptocurrencies.

method Used GARCH models, Kolmogorov tests, Khmaladze's martingale transformation, and maximum likelihood estimation.

result Strong evidence of non-Gaussian innovations in crypto-asset returns, contradicting previous assumptions.

cryptocurrency GARCH non-Gaussian Kolmogorov test Khmaladze transformation

2018-04-21abs ↗pdf ↗

A Hybrid Gaussian Process Regression Framework for Stable Volatility-Covariance Estimation: Evidence from Global Equity Indicesq-fin.RM

Paper proposes a new GPR-HS framework for accurate VCV estimation in global equity indices.

problem Accurate forecasting of Volatility-Covariance Matrix (VCV) for regulatory processes.

method Hybrid Gaussian Process Regression-Historical Simulation (GPR-HS) framework.

result GPR-HS framework achieves regulatory compliance and outperforms static VaR benchmarks.

Gaussian Process Regression Volatility-Covariance Matrix Value-at-Risk Expected Shortfall Historical Simulation

2026-05-17abs ↗pdf ↗

Benchmarking Deep Sequential Models on Volatility Predictions for Financial Time Seriescs.LG

Study deep sequential models for volatility prediction in financial markets.

problem Volatility prediction in financial time series.

method Empirical study of deep sequential models (CNN, RNN) vs traditional models.

result Dilated neural models outperform GARCH and stochastic models.

volatility prediction deep learning financial markets convolutional neural networks recurrent neural networks

2018-11-08abs ↗pdf ↗