To be or not to be: Roughness or long memory in volatility?

Mikkel Bennedsen, Kim Christensen and Peter Christensen

Papers from arXiv.org

Abstract: We develop a framework for composite likelihood estimation 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 have 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 empirical investigation, we inspect the dynamic of an intraday measure of the spot log-realized variance computed with high-frequency data from the cryptocurrency market. The 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. This is further backed by an analysis of the associated spot log-trading volume.

Date: 2024-03, Revised 2026-01
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-rmg
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Citations: View citations in EconPapers (3)

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