We apply machine learning models to forecast intraday realized volatility (RV), by exploiting commonality in intraday volatility via pooling stock data together, and by incorporating a proxy for the market volatility. Neural networks dominate linear regressions and tree models in terms of...

The aim of this study is to investigate the volatility spillover connectedness between NFTs attention and financial markets. This paper firstly proposes a new direct proxy for the public's attention in the NFT market: the non-fungible tokens attention index (NFTsAI), based on 590m news stories...

In this paper, we examine the herding behaviour of the Chinese renewable energy sector using both static and time-varying coefficient models. Examining daily data from January 05, 2015 to April 29, 2022, we find strong evidence of herding behaviour changing over time in this market. We find that...

This paper proposes a novel quantile vector autoregressive extended joint connectedness framework to examine realized volatilities spillovers between oil and precious metals commodities using daily data from May 1st, 2006 until June 18th, 2021. Our findings suggest that crude oil is the main net...

We examine the efficacy with which the CBOE Volatility Index (VIX) predicts future (30 day forward) S&P realized volatility. We find that the VIX’s accuracy is about 63%. An alternative framework that we present, built on asymptotic distribution theory, predicts this volatility with an...

This study examines the nexus between precious metals (gold and silver) and oil (crude oil and heating oil) realized volatilities introducing a novel quantile extended joint connectedness framework combining quantile vector autoregression (White et al., 2015) with the extended joint...

This study proposes a GARCH copula quantile regression model to capture the downside and upside tail dependence between oil price change and stock market returns at different risk levels. In the model, ten copulas are provided to measure the nonlinearity of the tail dependence with the marginal...

This study examines the effect of oil price uncertainty (OPU) on corporate innovation productivity. Using firm-level innovation data of US firms, we find that an increase in OPU causes a reduction in future innovation outcomes, proxied by the number of patents and subsequent citations. This...

In this paper we consider the Markov Switching GARCH model suggested by Haas, Mittnik, and Paolella (2004) and obtain the functional central limit theorems for and . The functional central limit theorem for is also obtained