This paper analyses herding in cryptocurrency markets in the time of the COVID-19
pandemic. We employ a combination of quantitative methods to hourly prices of the four …

Predicting volatility is a critical activity for taking risk-adjusted decisions in asset trading and
allocation. In order to provide effective decision-making support, in this paper we investigate …

In this article, we investigate the impacts of jump intensity on the volatility of futures in the oil
futures market using the heterogeneous autoregressive model of realized range-based …

In this paper, we examine the herd behaviour of the Chinese renewable energy sector using
both static and time-varying coefficient models. Examining daily data from January 05, 2015 …

Despite their effectiveness, linear models for realized variance neglect measurement errors
on integrated variance and exhibit several forms of misspecification due to the inherent …

The COVID-19 vaccine rollout expects to mitigate the severe negative impacts of the
pandemic on global financial markets. Our study provides supporting evidence for this …

The purpose of this paper is to investigate the role of regime switching in the prediction of
the Chinese stock market volatility with international market volatilities. Our work is based on …

Advances in the various realized GARCH models have proven effective in taking account of
the bias in realized volatility (RV) introduced by microstructure noise and non-trading hours …

This article develops a novel nonparametric time-varying auto-regressive distributed lag
model to estimate the persistence of inflation. The local linear estimation method is used to …

The source code of the package tvReg is publicly available for download from the
Comprehensive R Archive Network. The five basic functions in this package are the tvLM …