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Camilla Damian*, Rüdiger Frey
Publication: Scientific journal › Journal article › peer-review
In this paper, we focus on filtering and parameter estimation in stochastic volatility models when observations arise from high-frequency data. We are particularly interested in rough volatility models where spot volatility is driven by fractional Brownian motion with Hurst index (Formula presented.). Since volatility is not directly observable, we rely on particle filtering techniques for statistical inference regarding the current level of volatility and the parameters governing its dynamics. In order to obtain numerically efficient and recursive algorithms, we use the fact that a fractional Brownian motion can be represented through a superposition of Markovian semimartingales (Ornstein-Uhlenbeck processes). We analyze the performance of our approach on simulated data and we compare it to similar studies in the literature. The paper concludes with an empirical case study, where we apply our methodology to high-frequency data of a liquid stock.
Original language | English |
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Pages (from-to) | 1493-1508 |
Journal | Quantitative Finance |
Volume | 24 |
Issue number | 10 |
Early online date | Sept 2024 |
DOIs | |
Publication status | Published - 2024 |
Publication: Working/Discussion Paper › Working Paper/Preprint