This paper proposes an enhanced approach to modeling and forecasting volatility using
high frequency data. Using a forecasting model based on Realized GARCH with multiple …

We propose a decomposition of the realized covariance matrix into components based on
the signs of the underlying high‐frequency returns, and we derive the asymptotic properties …

The volatility of financial asset returns displays pronounced variation over the trading day.
Our goal is nonparametric inference for the average intraday volatility pattern, viewed as a …

In this paper, we propose a nonparametric way to test the hypothesis that time-variation in
intraday volatility is caused solely by a deterministic and recurrent diurnal pattern. We …

Outlying observations can bias regression estimates, requiring the use of outlier-robust
estimators. Comparing robust estimates to those obtained using ordinary least squares …

We quantify how co-jumps impact correlations in currency markets. To disentangle the
continuous part of quadratic covariation from co-jumps, and study the influence of co-jumps …

We propose methods to infer jumps of a semi-martingale, which describes long-term price
dynamics based on discrete, noisy, high-frequency observations. Different to the classical …

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 …

We propose a bootstrap method for estimating the distribution (and functionals of it such as
the variance) of various integrated covariance matrix estimators. In particular, we first adapt …

We develop an efficient mixed-scale estimator for jump regressions using high-frequency
asset returns. A fine time scale is used to accurately identify the locations of large rare jumps …