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Mikko S. Pakkanen
Mikko S. Pakkanen
Reader in Data Science and Quantitative Finance, Imperial College London
Verified email at imperial.ac.uk - Homepage
Title
Cited by
Cited by
Year
Decoupling the short-and long-term behavior of stochastic volatility
M Bennedsen, A Lunde, MS Pakkanen
Journal of Financial Econometrics 20 (5), 961-1006, 2022
1832022
Hybrid scheme for Brownian semistationary processes
M Bennedsen, A Lunde, MS Pakkanen
Finance and Stochastics 21, 931-965, 2017
1642017
Turbocharging Monte Carlo pricing for the rough Bergomi model
R McCrickerd, MS Pakkanen
Quantitative Finance 18 (11), 1877-1886, 2018
892018
State-dependent Hawkes processes and their application to limit order book modelling
M Morariu-Patrichi, MS Pakkanen
Quantitative Finance 22 (3), 563-583, 2022
692022
A GMM approach to estimate the roughness of stochastic volatility
AE Bolko, K Christensen, MS Pakkanen, B Veliyev
Journal of Econometrics 235 (2), 745-778, 2023
56*2023
Asymptotic theory for Brownian semi-stationary processes with application to turbulence
JM Corcuera, E Hedevang, MS Pakkanen, M Podolskij
Stochastic processes and their applications 123 (7), 2552-2574, 2013
562013
Pathwise large deviations for the rough Bergomi model
A Jacquier, MS Pakkanen, H Stone
Journal of Applied Probability 55 (4), 1078-1092, 2018
512018
Stochastic integrals and conditional full support
MS Pakkanen
Journal of Applied Probability 47 (3), 650-667, 2010
462010
Assessing relative volatility/intermittency/energy dissipation
OE Barndorff-Nielsen, MS Pakkanen, J Schmiegel
Electronic Journal of Statistics 8 (2), 1996-2021, 2014
242014
Limit theorems for power variations of ambit fields driven by white noise
MS Pakkanen
Stochastic Processes and their Applications, 2014
232014
Brownian semistationary processes and conditional full support
MS Pakkanen
International Journal of Theoretical and Applied Finance 14 (04), 579-586, 2011
232011
Deep hedging: Continuous reinforcement learning for hedging of general portfolios across multiple risk aversions
P Murray, B Wood, H Buehler, M Wiese, M Pakkanen
Proceedings of the Third ACM International Conference on AI in Finance, 361-368, 2022
212022
Functional limit theorems for generalized variations of the fractional Brownian sheet
MS Pakkanen, A Réveillac
Bernoulli 22 (3), 1671-1708, 2016
182016
VAE: a stochastic process prior for Bayesian deep learning with MCMC
S Mishra, S Flaxman, T Berah, M Pakkanen, H Zhu, S Bhatt
Statistics and Computing 32 (96), 1-16, 2022
17*2022
Microfoundations for diffusion price processes
MS Pakkanen
Mathematics and financial economics 3, 89-114, 2010
172010
Unifying incidence and prevalence under a time-varying general branching process
MS Pakkanen, X Miscouridou, MJ Penn, C Whittaker, T Berah, S Mishra, ...
Journal of Mathematical Biology 87 (2), 35, 2023
16*2023
Discretization of L\'evy semistationary processes with application to estimation
M Bennedsen, A Lunde, MS Pakkanen
arXiv preprint arXiv:1407.2754, 2014
132014
The short-term predictability of returns in order book markets: a deep learning perspective
L Lucchese, MS Pakkanen, AED Veraart
International Journal of Forecasting, 2024
112024
Deep hedging: learning to remove the drift under trading frictions with minimal equivalent near-martingale measures
H Buehler, P Murray, MS Pakkanen, B Wood
arXiv preprint arXiv:2111.07844, 2021
102021
Hybrid marked point processes: Characterization, existence and uniqueness
M Morariu-Patrichi, MS Pakkanen
Market Microstructure and Liquidity 4 (03n04), 1950007, 2018
102018
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Articles 1–20