Volatility forecasting is an important aspect of finance as it dictates many decisions of market
players. A snapshot of state-of-the-art neural network–based financial volatility forecasting …

In this work, I studied whether news media sentiments have an impact on Bitcoin volatility. In
doing so, I applied three different range-based volatility estimates along with two different …

This article exploits a new spillover directional measure proposed by Diebold and Yilmaz
(2009, 2012) to investigate the dynamic spillover of return and volatility between oil and …

Volatility is not directly observable and must be estimated. Estimator based on daily close
data is imprecise. Range-based volatility estimators provide significantly more precision, but …

One of the notable features of bitcoin is its extreme volatility. The modeling and forecasting
of bitcoin volatility are crucial for bitcoin investors' decision-making analysis and risk …

We propose a comprehensive treatment of the leverage effect, ie the relationship between
returns and volatility of a specific asset, focusing on energy commodities futures, namely …

We analyze the impact of the introduction of the French Tobin tax on the turnover and
measures of the liquidity and volatility of the affected stocks with nonparametric tests on …

We suggest a simple and general way to improve the GARCH volatility models using the
intraday range between the highest and the lowest price to proxy volatility. We illustrate the …

Recent research shows that the vast majority of scientific studies published in leading
finance journals fails scientific replication (Hou, Xue, and Zhang, 2020; Harvey, Liu, and …

A popular self-normalization (SN) approach in time series analysis uses the variance of a
partial sum as a self-normalizer. This is known to be sensitive to irregularities such as …