Decoupling the Short- and Long-Term Behavior of Stochastic Volatility
Multifactor Approximation of Rough Volatility Models
Mikkel Bennedsen,
Asger Lunde and
Mikko S Pakkanen
Journal of Financial Econometrics, 2022, vol. 20, issue 5, 961-1006
Abstract:
We introduce a new class of continuous-time models of the stochastic volatility of asset prices. The models can simultaneously incorporate roughness and slowly decaying autocorrelations, including proper long memory, which are two stylized facts often found in volatility data. Our prime model is based on the so-called Brownian semistationary process and we derive a number of theoretical properties of this process, relevant to volatility modeling. Applying the models to realized volatility measures covering a vast panel of assets, we find evidence consistent with the hypothesis that time series of realized measures of volatility are both rough and very persistent. Lastly, we illustrate the utility of the models in an extensive forecasting study; we find that the models proposed in this article outperform a wide array of benchmarks considerably, indicating that it pays off to exploit both roughness and persistence in volatility forecasting.
Keywords: : Brownian semistationary process; forecasting; high-frequency data; long memory; persistence; rough volatility; stochastic volatility (search for similar items in EconPapers)
JEL-codes: C22 C51 C53 C58 G17 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (13)
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