arXiv 24 Aug 2024 · Econometrics · publishedJournal of Econometrics (2025) · 3 citations (OpenAlex)
arXiv:2408.13437 · PDF · DOI · OpenAlex · Extracted main text
This paper introduces an econometric framework for analyzing cross-sectional dependence in the idiosyncratic volatilities of assets using high frequency data. We first consider the estimation of standard measures of dependence in the idiosyncratic volatilities such as covariances and correlations. Naive estimators of these measures are biased due to the use of the error-laden estimates of idiosyncratic volatilities. We provide bias-corrected estimators and the relevant asymptotic theory. Next, we introduce an idiosyncratic volatility factor model, in which we decompose the variation in idiosyncratic volatilities into two parts: the variation related to the systematic factors such as the market volatility, and the residual variation. Again, naive estimators of the decomposition are biased, and we provide bias-corrected estimators. We also provide the asymptotic theory that allows us to test whether the residual (non-systematic) components of the idiosyncratic volatilities exhibit cross-sectional dependence. We apply our methodology to the S&P 100 index constituents, and document strong cross-sectional dependence in their idiosyncratic volatilities. We consider two different sets of idiosyncratic volatility factors, and find that neither can fully account for the cross-sectional dependence in idiosyncratic volatilities. For each model, we map out the network of dependencies in residual (non-systematic) idiosyncratic volatilities across all stocks.
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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 | Jacod and Rosenbaum (2015) Estimation of Volatility Functionals: the Case of a $n$ Window | 1.000 | 15 | 5 | 100% |
| 2 | Vetter (2015) Estimation of Integrated Volatility of Volatility with Applications to Goodness-of-fit Testing | 1.000 | 10 | 3 | 100% |
| 3 | Herskovic, Kelly, Lustig, and Nieuwerburgh (2016) The Common Factor in Idiosyncratic Volatility: Quantitative Asset Pricing Implications | 1.000 | 8 | 3 | 100% |
| yacjacod14 | unmatched citation key yacjacod14 | 1.000 | 7 | 5 | 100% |
| 5 | Jacod and Rosenbaum (2013) Quarticity and Other Functionals of Volatility: Efficient Estimation | 1.000 | 5 | 3 | 100% |
| 6 | Li, Todorov, and Tauchen (2016) Inference Theory on Volatility Functional Dependencies | 0.928 | 4 | 4 | 100% |
| 7 | Kalnina and Xiu (2017) Nonparametric Estimation of the Leverage Effect: A Trade-off between Robustness and Efficiency | 0.928 | 4 | 3 | 100% |
| yackalninaxiu-FF | unmatched citation key yackalninaxiu-FF | 0.928 | 4 | 3 | 100% |
| 9 | Aẗ-Sahalia, Fan, and Li (2013) The Leverage Effect Puzzle: Disentangling Sources of Bias at High Frequency | 0.737 | 3 | 2 | 100% |
| 10 | Li, Todorov, and Tauchen (2017) Adaptive Estimation of Continuous-Time Regression Models using High-Frequency Data | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 48 scored citations. 2 of these could not be matched to a bibliography entry, so only the citation key is shown.