We analyse high-frequency realized volatility dynamics and spillovers between centralized
crypto exchanges that offer spot and derivative contracts for bitcoin against the US dollar or …

Various parametric volatility models for financial data have been developed to incorporate
high-frequency realized volatilities and better capture market dynamics. However, because …

The conditional variance, skewness, and kurtosis play a central role in time series analysis.
These three conditional moments (CMs) are often studied by some parametric models but …

Even moderate amounts of zero returns in financial data, associated with stale prices, are
heavily detrimental for reliable jump inference. We harness staleness-robust estimators to …

The higher order dynamics of individual stocks is investigated. We show that classical
powers correlation analysis can lead to a spurious assessment of the volatility persistence or …

Time series containing non‐negligible portion of possibly dependent zeros, whereas the
remaining observations are positive, are considered. They are regarded as GARCH …

The paper considers simultaneous nonparametric inference for a wide class of M-regression
models with time-varying coefficients. The covariates and errors of the regression model are …

Generalized autoregressive conditionally heteroskedastic (GARCH) processes are widely
used for modelling financial returns, with their extremal properties being of interest for …

We review recent articles that focus on the main issues identified in high-frequency financial
data analysis. The issues to be addressed include nonstationarity, low signal-to-noise ratios …

This article deals with the problem of estimating the volatility of a financial security in a
market with frictions. We propose a microstructural model with time-varying fundamental …