We survey the nascent literature on machine learning in the study of financial markets. We
highlight the best examples of what this line of research has to offer and recommend …

I provide a selective review of recent developments in financial econometrics related to
measuring, modeling, forecasting, and pricing “good” and “bad” volatilities based on …

We establish new results for estimation and inference in financial durations models, where
events are observed over a given time span, such as a trading day, or a week. For the …

We forecast the realized covariance matrix of asset returns in the US equity market by
exploiting the predictive information of graphs in volatility and correlation. Specifically, we …

Time series of realized covariance matrices can be modeled in the conditional
autoregressive Wishart model family via dynamic correlations or via dynamic covariances …

We address the construction of Realized Variance (RV) forecasts by exploiting the
hierarchical structure implicit in available decompositions of RV. We propose a post …

This paper examines how realized variances predict cryptocurrency returns in the cross-
section using intraday data. We find that cryptocurrencies with higher variances exhibit lower …

Forecasting and Managing Correlation Risks Page 1 Overview Data and Variables Estimation
Methodology Out-of-sample Forecast Performance Applications Robustness Conclusion …

We propose new refined measures of the local covariation between the return on an asset
and a risk factor. Our proposed" granular betas" generalize the notion of up-and down-side …

In this study, we propose a dynamic partial (co) variance forecasting model (DPCFM) by
introducing a dynamic model averaging (DMA) approach into a partial (co) variance …