Øyvind Grotmol, Martin Jullum, Kjersti Aas, Michael Scheuerer
arXiv 23 Mar 2022 · Statistics — Applications
arXiv:2203.12402 · PDF · DOI · OpenAlex · Extracted main text
Quantifying both historic and future volatility is key in portfolio risk management. This note presents and compares estimation strategies for volatility estimation in an estimation universe consisting on 28 629 unique companies from February 2010 to April 2021, with 858 different portfolios. The estimation methods are compared in terms of how they rank the volatility of the different subsets of portfolios. The overall best performing approach estimates volatility from direct entity returns using a GARCH model for variance estimation.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Ø. Grotmol, M. Scheuerer, K. Aas, and M. Jullum (2021) Exabel’s factor model, 2021 self | 0.644 | 2 | 2 | 100% |
| 2 | M. G. Kendall (1938) A new measure of rank correlation | 0.644 | 2 | 2 | 100% |
| 3 | T. Bollerslev (1986) Generalized autoregressive conditional heteroskedasticity | 0.511 | 2 | 1 | 100% |
| 4 | A. Agresti (2010) Analysis of ordinal categorical data, volume 656 | 0.405 | 1 | 1 | 100% |
| 5 | N. J. Higham (2002) Computing the nearest correlation matrix—a problem from finance | 0.405 | 1 | 1 | 100% |
Showing the top 5 of 5 scored citations.