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 betas to multi-factor functional measures of covariation. We then show how the resulting granular beta functions may...

We propose a new decomposition of the traditional market beta into four semibetas depending on the signed covariation between the market and individual asset returns. Consistent with the pricing implications from a mean-semivariance framework, we show that higher semibetas defined by negative...

I provide a selective review of recent developments in financial econometrics related to measuring, modeling, forecasting and pricing “good” and “bad” volatilities based on realized variation type measures constructed from high-frequency intraday data. An especially appealing feature of...

We rely on a unique set of high-frequency factors to robustly estimate an intraday Stochastic Discount Factor (SDF). Exploiting the precisely timed jumps in the estimated SDF together with real-time newswire data, we identify and precise the news that is priced. We find that news related to...

This paper proposes a generalization of the class of realized semivariance and semicovariance measures introduced by Barndorff-Nielsen, Kinnebrock and Shephard (2010) and Bollerslev, Li, Patton and Quaedvlieg (2020a) to allow for a finer decomposition of realized (co)variances. The new "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 improve the accuracy of the forecasts. By allowing the parameters of the models to vary explicitly with the...

Current practice largely follows restrictive approaches to market risk measurement, such as historical simulation or RiskMetrics. In contrast, we propose exible methods that exploit recent developments in nancial econometrics and are likely to produce more accurate risk assessments, treating...

Based on a unique high-frequency dataset for more than fifty commodities, currencies, equity indices, and fixed income instruments spanning more than two decades, we document strong similarities in realized volatilities patterns across assets and asset classes. Exploiting these similarities...

Betas from return regressions are commonly used to measure systematic financial market risks. "Good" beta measurements are essential for a range of empirical inquiries in finance and macroeconomics. We introduce a novel econometric framework for the nonparametric estimation of time-varying betas...

We propose a novel and easy-to-implement framework for forecasting correlation risks based on a large set of salient realized correlation features and the sparsity-encouraging LASSO technique. Considering the universe of S&P 500 stocks, we find that the new approach manifests in statistically...