Mikkel Bennedsen, Kim Christensen, Peter Christensen
arXiv 19 Mar 2024 · Econometrics · publishedJournal of Econometrics (2026) · 1 citations (OpenAlex)
arXiv:2403.12653 · PDF · DOI · OpenAlex · Extracted main text
We develop a framework for composite likelihood inference of parametric continuous-time stationary Gaussian processes. We derive the asymptotic theory of the associated maximum composite likelihood estimator. We implement our approach on a pair of models that has been proposed to describe the random log-spot variance of financial asset returns. A simulation study shows that it delivers good performance in these settings and improves upon a method-of-moments estimation. In an application, we inspect the dynamic of an intraday measure of spot variance computed with high-frequency data from the cryptocurrency market. The empirical evidence supports a mechanism, where the short- and long-term correlation structure of stochastic volatility are decoupled in order to capture its properties at different time scales.
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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 | Bolko, Christensen, Pakkanen, and Veliyev (2023) A GMM approach to estimate the roughness of stochastic volatility | 1.000 | 10 | 5 | 100% |
| 2 | Gatheral, Jaisson, and Rosenbaum (2018) Volatility is rough | 1.000 | 6 | 5 | 100% |
| 3 | Fukasawa, Takabatake, and Westphal (2022) Consistent estimation for fractional stochastic volatility model under high-frequency asymptotics | 1.000 | 6 | 4 | 100% |
| 4 | Shi, Yu, and Zhang (2024) On the spectral density of fractional Ornstein-Uhlenbeck processes | 1.000 | 5 | 4 | 100% |
| 5 | Wang, Xiao, and Yu (2023) Modeling and forecasting realized volatility with the fractional Ornstein-Uhlenbeck process | 0.941 | 12 | 6 | 83% |
| 6 | Bennedsen, Lunde, and Pakkanen (2022) Decoupling the short- and long-term behavior of stochastic volatility | 0.928 | 10 | 7 | 80% |
| 7 | Andersen, Bollerslev, Diebold, and Labys (2003) Modeling and forecasting realized volatility | 0.928 | 4 | 3 | 100% |
| 8 | Wang, Xiao, Yu, and Zhang (2025) Maximum likelihood estimation of fractional Ornstein-Uhlenbeck process with discretely sampled data | 0.928 | 4 | 3 | 100% |
| 9 | Hosking (1996) Asymptotic distributions of the sample mean, autocovariances, and autocorrelations of long-memory time series | 0.894 | 7 | 4 | 71% |
| 10 | Davis and Yau (2011) Comments on pairwise likelihood in time series models | 0.874 | 6 | 2 | 100% |
Showing the top 10 of 68 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Optimal Estimation for General Gaussian Processes | 0.511 | 2 | 2 |