We propose several connectedness measures built from pieces of variance decompositions,
and we argue that they provide natural and insightful measures of connectedness. We also …

We study the accuracy of a variety of estimators of asset price variation constructed from
high-frequency data (“realized measures”), and compare them with a simple “realized …

We propose a new family of easy-to-implement realized volatility based forecasting models.
The models exploit the asymptotic theory for high-frequency realized volatility estimation to …

Connections among different assets, asset classes, portfolios, and the stocks of individual
institutions are critical in examining financial markets. Interest in financial markets implies …

Based on high-frequency data for more than fifty commodities, currencies, equity indices,
and fixed-income instruments spanning more than two decades, we document strong …

We examine the properties and forecast performance of multiplicative volatility specifications
that belong to the class of generalized autoregressive conditional heteroskedasticity–mixed …

We develop a GMM approach for estimation of log-normal stochastic volatility models driven
by a fractional Brownian motion with unrestricted Hurst exponent. We show that a parameter …

We propose a new decomposition of the traditional market beta into four semi betas that
depend on the signed covariation between the market and individual asset returns. We …

We introduce a blocking and regularization approach to estimate high-dimensional
covariances using highfrequency data. Assets are first grouped according to liquidity. Using …

Stock market volatility clusters in time, appears fractionally integrated, carries a risk
premium, and exhibits asymmetric leverage effects. At the same time, the volatility risk …