This project uses Python and the GARCH(1,1) model to forecast the volatility of the CSI 1000 Index based on historical data. It includes data acquisition, preprocessing, model fitting, volatility ...

This repository contains analysis and implementation of various volatility forecasting models for financial time series. The project explores different GARCH-type models, moving window volatility, ...

We solve a stylised fact on a long memory process of the volatility cluster phenomena by using the Minkowski metric for GARCH(1,1) (generalised autoregressive conditional heteroskedasticity) under the ...

Abstract: This paper studies a modification of quasi-maximum likelihood estimator for GARCH (1, 1) process with the errors, whose squares have regularly varying tail probabilities with the exponent α, ...

This paper investigates the estimation of a 10-day value-at-risk (VaR) based on a data set of 250 daily values. The commonly used square-rootof-time rule, which scales the one-day 99% VaR with a ...

Abstract: We compare 330 GARCH-type models in terms of their ability to predict the conditional variance using out-of-sample data. Our question of interest is whether more sophisticated volatility ...

The paper investigated the problem of restoring missing values in time series data analysis. The aim of this study was to advance the imputation of missing values for some autoregressive moving ...