Risk Everywhere: Modeling and Managing Volatility

By Tim Bollerslev, Benjamin Hood, J. Huss, Lasse Heje Pedersen. 2018

317 citations recorded by the source corpus.

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 within and across asset classes in panel-based estimation of new realized volatility models results in superior out-of-sample risk forecasts, compared to forecasts from existing models and more conventional procedures that do not incorporate the information in the high-frequency intraday data and/or the similarities in the volatilities. A utility-based framework designed to evaluate the economic gains from risk modeling highlights the interplay between parsimony of model specification, transaction costs, and speed of trading in the practical implementation of the different risk models.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/8816171c0acfe3cf2027ca3101c6032818026b6d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

  1. Jiawen Luo
  2. Chenghan Hou
  3. Alan Moreira
  4. Qiang Ji
  5. Tyler Muir
  6. Tony Klein
  7. Elie Bouri
  8. David Roubaud

In Finance

  1. Alan Moreira
  2. Chenghan Hou
  3. Jiawen Luo
  4. Qiang Ji
  5. Tyler Muir
  6. Tony Klein

Citations in the corpus