Követés
Mikko S. Pakkanen
Mikko S. Pakkanen
Reader in Data Science and Quantitative Finance, Imperial College London
E-mail megerősítve itt: imperial.ac.uk - Kezdőlap
Cím
Hivatkozott rá
Hivatkozott rá
Év
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
1902022
Hybrid scheme for Brownian semistationary processes
M Bennedsen, A Lunde, MS Pakkanen
Finance and Stochastics 21, 931-965, 2017
1682017
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
762022
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
60*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
592013
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
272014
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
262022
Limit theorems for power variations of ambit fields driven by white noise
MS Pakkanen
Stochastic Processes and their Applications, 2014
252014
Brownian semistationary processes and conditional full support
MS Pakkanen
International Journal of Theoretical and Applied Finance 14 (04), 579-586, 2011
232011
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
20*2022
Functional limit theorems for generalized variations of the fractional Brownian sheet
MS Pakkanen, A Réveillac
Bernoulli 22 (3), 1671-1708, 2016
192016
Microfoundations for diffusion price processes
MS Pakkanen
Mathematics and financial economics 3, 89-114, 2010
182010
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
17*2023
The short-term predictability of returns in order book markets: A deep learning perspective
L Lucchese, MS Pakkanen, AED Veraart
International Journal of Forecasting 40 (4), 1587-1621, 2024
152024
Discretization of L\'evy semistationary processes with application to estimation
M Bennedsen, A Lunde, MS Pakkanen
arXiv preprint arXiv:1407.2754, 2014
132014
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
112021
Intrinsic randomness in epidemic modelling beyond statistical uncertainty
MJ Penn, DJ Laydon, J Penn, C Whittaker, C Morgenstern, O Ratmann, ...
Communications Physics 6 (1), 146, 2023
10*2023
A rendszer jelenleg nem tudja elvégezni a műveletet. Próbálkozzon újra később.
Cikkek 1–20