The purpose of this research was to examine the dynamics volatility spillover between energy and environmental, social, sustainable indices. COVID19 prompted select April 2019 March 2022 as a sample period, respective data (Daily Prices) Nifty Energy ESG indices were obtained from National Stock Exchange India Limited. outcomes study confirmed that daily returns 100 not normally distributed rea...

In this paper, we demonstrate that most of Tokyo stock return data sets have volatility persistence and it is due to a parameter change in underlying GARCH models. For testing for a parameter change, we use the cusum test, devised by Lee et al. (2003), based on the residuals from GARCH models. A simulation study shows that a parameter change in GARCH models can mislead analysts to choose an IGA...

We present a new approach to generalised autoregressive conditional het-eroscedasitic (GARCH) modelling for asset returns. Instead of attempting to choose a speciic distribution for the errors, as in the usual GARCH model formulation, we use a nonparametric distribution to estimate these errors. This takes into account the common problems encountered in nancial time series, for example, asymmet...

We present a new approach to generalised autoregressive conditional heteroscedasitic (GARCH) modelling for asset returns. Instead of attempting to choose a speciic distribution for the errors, as in the usual GARCH model formulation, we use a nonparametric distribution to estimate these errors. This takes into account the common problems encountered in nan-cial time series, for example, asymmet...

Heart Rate Variability (HRV) series exhibit long memory and time-varying conditional variance. This work considers the Fractionally Integrated AutoRegressive Moving Average (ARFIMA) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors. ARFIMA-GARCH models may be used to capture and remove long memory and estimate the conditional volatility in 24 h HRV recordings. Th...

It is well known in the literature that the joint parameter estimation of the Smooth Autoregressive – Generalized Autoregressive Conditional Heteroskedasticity (STAR-GARCH) models poses many numerical challenges with unknown causes. This paper aims to uncover the root of the numerical difficulties in obtaining stable parameter estimates for a class of three-regime STAR-GARCH models using Quasi-...

Yingfu Xie. Maximum Likelihood Estimation and Forecasting for GARCH, Markov Switching, and Locally Stationary Wavelet Processes. Doctoral Thesis. ISSN 1652-6880, ISBN 978-91-85913-06-0. Financial time series are frequently met both in daily life and the scientific world. It is clearly of importance to study the financial time series, to understand the mechanism giving rise to the data, and/or p...

As a versatile investment tool in energy markets for speculators and hedgers, the Goldman Sachs Commodity Index (GSCI) futures are quite well known. Therefore, this paper proposes a hybrid model incorporating ARCH family models and ANN model to forecast GSCI futures price. Empirical results show that the hybrid ARCH(1)-M-ANN model is superior to ARIMA, ARCH(1),GARCH(1,1), EGARCH(1,1) and ARIMA-...

The goal of this study was to identify a reliable GARCH model for modeling and forecasting each economic variable in Nigeria, including the price crude oil, consumer index, exchange rate, inflation rate. Monthly secondary data simulated sets were that used. Between January 2004 December 2020, are covered. Beta Volatility Coefficient (BVC) proposed detecting volatility research data. Using metho...

Recently, there has been a growing interest in the methods addressing volatility in computational finance and econometrics. Peiris et al. [8] have introduced doubly stochastic volatility models with GARCH innovations. Random coefficient autoregressive sequences are special case of doubly stochastic time series. In this paper, we consider some doubly stochastic stationary time series with GARCH ...