Stochastics and statistics
Stochastic analysis and statistical modeling are key methodologies for analyzing random interactions in today's information systems. Our research group applies and develops methods in probability theory and mathematical statistics related to applications in communication and transportation networks, engineering, and natural sciences. The main research areas are: stochastic networks and queueing systems, asymptotic statistics, and random spatial structures.
We teach courses in probability and statistics at all levels. Many of the offered MSc courses are eligible as a basis for an SHV degree in insurance mathematics. PhD education in stochastics and statistics is coordinated by the Finnish Doctoral Education Network in Stochastics and Statistics (FDNSS).
MSc/PhD courses 2016-2017
BSc courses 2016-2017
Basic courses 2016-2017
Affiliated postdocs: Steven Flores (University of Helsinki), Giancarlo Pastor (VTT Research Centre), Lauri Viitasaari (University of Saarbrücken).
Affiliated PhD students: Mikko Kuronen (University of Jyväskylä), Matias Leppisaari (Model IT), Eveliina Peltola (University of Helsinki), Mika Sirviö (Insurance Centre), Tarja Sirén (Financial Supervisory Authority).
Affiliated docents: Kari Eloranta, Teemu Pennanen (King's College London), Karl Sigman (Columbia University).
Aalto Stochastics Wiki
Giancarlo Pastor Figueroa 1.3.2016–31.8.2017
- 29.8. 15:15 Mikko Pakkanen (Imperial College): Rough volatility: empirical evidence and efficient simulation method (further info) – M3 (M234)
In their recent, yet already seminal, paper (arXiv:1410.3394) Jim Gatheral, Thibault Jaisson, and Mathieu Rosenbaum suggested that financial market volatility should be modelled by stochastic processes that are rougher than Brownian motion. In the first part of my talk, I will present some new (corroborative) empirical evidence of the roughness of volatility, based on ultra-high-frequency financial data. The second part will be more methodological and introduce the so-called hybrid scheme for simulation of rough stochastic processes. In particular, the hybrid scheme can be used to make Monte Carlo-based option pricing in rough volatility models more efficient. The results of this talk have been obtained in collaboration with Mikkel Bennedsen and Asger Lunde.
- J Barral, A Kupiainen, M Nikula, E Saksman, C Webb. Basic properties of critical lognormal multiplicative chaos. The Annals of Probability 43(5):2205-2249, 2015.
- P Aalto, L Leskelä. Information spreading in a large population of active transmitters and passive receivers. SIAM Journal on Applied Mathematics 75(5):1965–1982, 2015.
- E Azmoodeh, L Viitasaari. Rate of convergence for discretization of integrals with respect to fractional Brownian motion. Journal of Theoretical Probability, 28(1):396-422, 2015.
- C Hongler, K Kytölä. Ising interfaces and free boundary conditions. Journal of the American Mathematical Society 26:1107–1189, 2013.
- P Ilmonen, D Paindaveine. Semiparametrically efficient inference based on signed ranks in symmetric independent component models. Annals of Statistics 39(5):2448–2476, 2011.
Helena Aro (Etera)
Ehsan Azmoodeh (University of Luxembourg)
Milla Kibble (FIMM Institute for Molecular Medicine Finland)
Matti Kiiski (ETH Zürich)
Ilkka Mellin (University teacher emeritus)
Igor Morlanes (Stockholm University)
Amitava Mukherjee (XLRI- Xavier School of Management)
Ari-Pekka Perkkiö (Technische Universität Berlin)
Heikki Tikanmäki (Finnish Centre for Pensions)
Esko Valkeila (1951–2012)
Page content by: webmaster-math [at] list [dot] aalto [dot] fi