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Employee Profile

Kristina Rognlien Dahl

Professor - Department of Economics

Biography

I am a Professor in Mathematics at BI Norwegian Business School. I have a Ph.D in Mathematics from the University of Oslo, where I specialized in stochastic analysis and mathematical finance.

My research covers different topics in the field of stochastic analysis: Stochastic optimal control theory, in particular in the non-Markovian case, study of existence and uniqueness of solutions of new kinds of SDEs and BSDEs and modelling based on these new stochastic processes.

I am interested in real-life applications of stochastic models, and how they can improve our understanding of many different fields. I have worked on stochastic models and stochastic control for hydropower management, modelling of infectious diseases, temperature modelling, economic games, financial mathematics, risk assessment at sea, modelling the deterioration of components in a system and connections between stochastic control and reinforcement learning.

Awards:

NRC Young Research Talent project, 2020.

H.R.H. the King of Norway's gold medal for best PhD thesis, 2016.

The Norwegian Computing Center's prize for best master thesis, 2012.

Publications

Dahl, Kristina Rognlien & Dordevic, Jasmina (2022)

Stochastic optimal control of pre-exposure prophylaxis for HIV infection

Mathematical Medicine and Biology, 39(3), s. 197- 225. Doi: 10.1093/imammb/dqac003

Dahl, Kristina Rognlien & Eyjolfsson, Heidar (2022)

Self-exciting jump processes and their asymptotic behaviour

Stochastics: An International Journal of Probability and Stochastic Processes Doi: 10.1080/17442508.2022.2028789 - Full text in research archive

Eggen, Mari Dahl; Dahl, Kristina Rognlien, Näsholm, Sven Peter & Mæland, Steffen (2022)

Stochastic Modeling of Stratospheric Temperature

Mathematical Geosciences, 54, s. 651- 678. Doi: 10.1007/s11004-021-09990-6 - Full text in research archive

This study suggests a stochastic model for time series of daily zonal (circumpolar) mean stratospheric temperature at a given pressure level. It can be seen as an extension of previous studies which have developed stochastic models for surface temperatures. The proposed model is a combination of a deterministic seasonality function and a Lévy-driven multidimensional Ornstein–Uhlenbeck process, which is a mean-reverting stochastic process. More specifically, the deseasonalized temperature model is an order 4 continuous-time autoregressive model, meaning that the stratospheric temperature is modeled to be directly dependent on the temperature over four preceding days, while the model’s longer-range memory stems from its recursive nature. This study is based on temperature data from the European Centre for Medium-Range Weather Forecasts ERA-Interim reanalysis model product. The residuals of the autoregressive model are well represented by normal inverse Gaussian-distributed random variables scaled with a time-dependent volatility function. A monthly variability in speed of mean reversion of stratospheric temperature is found, hence suggesting a generalization of the fourth-order continuous-time autoregressive model. A stochastic stratospheric temperature model, as proposed in this paper, can be used in geophysical analyses to improve the understanding of stratospheric dynamics. In particular, such characterizations of stratospheric temperature may be a step towards greater insight in modeling and prediction of large-scale middle atmospheric events, such as sudden stratospheric warming. Through stratosphere–troposphere coupling, the stratosphere is hence a source of extended tropospheric predictability at weekly to monthly timescales, which is of great importance in several societal and industry sectors.

Dahl, Kristina Rognlien & Eyjolfsson, Heidar (2021)

Self-Exciting Jump Processes as Deterioration Models

Castanier, Bruno; Cepin, Marko, Bigaud, David & Berenguer, Christophe (red.). Proceedings of the 31st European Safety and Reliability Conference

Agrell, Christian & Dahl, Kristina Rognlien (2021)

Sequential Bayesian optimal experimental design for structural reliability analysis

Statistics and computing, 31 Doi: 10.1007/s11222-021-10000-2 - Full text in research archive

Dahl, Kristina Rognlien & Huseby, Arne (2020)

Environmental contours and optimal design

Baraldi, Piero; Di Maio, Francesco P. & Zio, Enrico (red.). e-proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference (ESREL2020 PSAM15)

Dahl, Kristina Rognlien (2020)

Forward-backward stochastic differential equation games with delay and noisy memory

Stochastic Analysis and Applications, 38(4), s. 708- 729. Doi: 10.1080/07362994.2020.1713810 - Full text in research archive

Dahl, Kristina Rognlien & Stokkereit, Espen (2019)

A duopoly preemption game with two alternative stochastic investment choices

Afrika Matematika, 30(3-4), s. 663- 680. Doi: 10.1007/s13370-019-00674-3 - Full text in research archive

Dahl, Kristina Rognlien (2018)

Management of a hydropower system via convex duality

Mathematical Methods of Operations Research, 89, s. 43- 71. Doi: 10.1007/s00186-018-00656-4 - Full text in research archive

Dahl, Kristina Rognlien & Huseby, Arne (2018)

Buffered environmental contours

Haugen, Stein; Barros, Anne, van Gulijk, Coen, Kongsvik, Trond & Vinnem, Jan Erik (red.). Safety and Reliability – Safe Societies in a Changing World. Proceedings of ESREL 2018, June 17-21, 2018, Trondheim, Norway

The main idea of this paper is to use the notion of buffered failure probability from probabilistic structural design, first introduced by Rockafellar and Royset (2010), to introduce buffered environmental contours. Classical environmental contours are used in structural design in order to obtain upper bounds on the failure probabilities of a large class of designs. The purpose of buffered failure probabilities is the same. However, in contrast to classical environmental contours, this new concept does not just take into account failure vs. functioning, but also to which extent the system is failing. For example, this is relevant when considering the risk of flooding: We are not just interested in knowing whether a river has flooded. The damages caused by the flooding greatly depends on how much the water has risen above the standard level.

Dahl, Kristina Rognlien & Øksendal, Bernt (2017)

Singular recursive utility

Stochastics: An International Journal of Probability and Stochastic Processes, 89(6-7), s. 994- 1014. Doi: 10.1080/17442508.2017.1303067 - Full text in research archive

Dahl, Kristina Rognlien (2017)

A convex duality approach for pricing contingent claims under partial information and short selling constraints

Stochastic Analysis and Applications, 35(2), s. 317- 333. Doi: 10.1080/07362994.2016.1255147 - Full text in research archive

Dahl, Kristina Rognlien; Mohammed, Salah-Eldin, Øksendal, Bernt & Røse, Elin Engen (2016)

Optimal control of systems with noisy memory and BSDEs with Malliavin derivatives

Journal of Functional Analysis, 271(2), s. 289- 329. Doi: 10.1016/j.jfa.2016.04.031 - Full text in research archive

Dahl, Kristina Rognlien & Stokkereit, Espen (2015)

Stochastic maximum principle with Lagrange multipliers and optimal consumption with Lévy wage

Afrika Matematika, 27(3-4), s. 555- 572. Doi: 10.1007/s13370-015-0360-5 - Full text in research archive

Dahl, Kristina Rognlien (2013)

Pricing of Claims in Discrete Time with Partial Information

Applied Mathematics and Optimization, 68(2), s. 145- 155. Doi: 10.1007/s00245-013-9200-x - Full text in research archive

Dahl, Kristina Rognlien (2018)

Kvinner i matematikk: 16 matematikere, 16 portretter. Fotoutstilling med intervju ved Realfagsbiblioteket, UiO.

Realfagsbiblioteket, UiO [Fagblad]

Dahl, Kristina Rognlien (2017)

Intervju med nrk.no i forbindelse med uvanlig lavt antall flyulykker i 2017.

nrk.no [Internett]

Dahl, Kristina Rognlien; Huseby, Arne Bang & Helvig Havgar, Marius (2022)

Optimal Reinsurance Contracts under Conditional Value-at-Risk

Leva, Maria Chiara; Patelli, Edoardo, Podofillini, Luca & Wilson, Simon (red.). Proceedings of the 32nd European Safety and Reliability Conference (ESREL 2022)

Eggen, Mari Dahl; Dahl, Kristina Rognlien, Näsholm, Sven Peter & Mæland, Steffen (2022)

Stochastic Modeling of Stratospheric Temperature

[Academic lecture]. European Geosciences Union General Assembly.

This study suggests a stochastic model for time series of daily zonal (circumpolar) mean stratospheric temperature at a given pressure level. It can be seen as an extension of previous studies which have developed stochastic models for surface temperatures. The proposed model is a combination of a deterministic seasonality function and a Lévy-driven multidimensional Ornstein–Uhlenbeck process, which is a mean-reverting stochastic process. More specifically, the deseasonalized temperature model is an order 4 continuous-time autoregressive model, meaning that the stratospheric temperature is modeled to be directly dependent on the temperature over four preceding days, while the model’s longer-range memory stems from its recursive nature. This study is based on temperature data from the European Centre for Medium-Range Weather Forecasts ERA-Interim reanalysis model product. The residuals of the autoregressive model are well represented by normal inverse Gaussian-distributed random variables scaled with a time-dependent volatility function. A monthly variability in speed of mean reversion of stratospheric temperature is found, hence suggesting a generalization of the fourth-order continuous-time autoregressive model. A stochastic stratospheric temperature model, as proposed in this paper, can be used in geophysical analyses to improve the understanding of stratospheric dynamics. In particular, such characterizations of stratospheric temperature may be a step towards greater insight in modeling and prediction of large-scale middle atmospheric events, such as sudden stratospheric warming. Through stratosphere–troposphere coupling, the stratosphere is hence a source of extended tropospheric predictability at weekly to monthly timescales, which is of great importance in several societal and industry sectors.

Eggen, Mari Dahl; Dahl, Kristina Rognlien, Näsholm, Sven Peter & Mæland, Steffen (2021)

Stochastic modelling of stratospheric temperature

[Academic lecture]. European Geosciences Union General Assembly Symposium 2021.

Dahl, Kristina Rognlien & Eyolfsson, Heidar (2021)

Self-exciting jump processes as deterioration models

[Academic lecture]. ESREL 2021.

Dahl, Kristina Rognlien & Huseby, Arne (2020)

Environmental contours and optimal design

[Academic lecture]. ESREL 2020.

Dahl, Kristina Rognlien (2020)

The SCROLLER project and a subproject: Optimal design

[Academic lecture]. PhD gathering.

Dahl, Kristina Rognlien (2020)

The SCROLLER project A Stochastic ContROL approach to machine Learning with applications to Environmental Risk models

[Academic lecture]. Seminar at UiO.

Dahl, Kristina Rognlien (2020)

FBSDE games with delay & noisy memory

[Academic lecture]. Seminar at UiO.

Dahl, Kristina Rognlien (2018)

Buffered environmental contours

[Academic lecture]. ECSADES project meeting.

Dahl, Kristina Rognlien (2018)

An introduction to binary system analysis

[Academic lecture]. Section 3 seminar.

Dahl, Kristina Rognlien (2018)

Buffered environmental contours

[Academic lecture]. ESREL 2018.

Dahl, Kristina Rognlien (2017)

Management of a hydropower system via convex duality

[Academic lecture]. MMR 2017.

Dahl, Kristina Rognlien (2016)

Information and memory in stochastic optimal control

[Academic lecture]. Disputasforelesning.

Dahl, Kristina Rognlien & Øksendal, Bernt (2015)

Singular recursive utility

[Academic lecture]. Second conference on Stochastics of Environmental and Financial Economics.

Dahl, Kristina Rognlien (2013)

Duality methods for pricing contingent claims

[Academic lecture]. AMaMeF2013.

Dahl, Kristina Rognlien (2013)

Duality methods for pricing contingent claims

[Academic lecture]. Stokastisk analyse seminar UiO.

Dahl, Geir & Dahl, Kristina Rognlien (2012)

Linear optimization and mathematical finance

[Report]. University of Oslo.

Academic Degrees
Year Academic Department Degree
2016 Universitetet i Oslo PhD
Work Experience
Year Employer Job Title
2022 - Present BI Norwegian Business School Professor
2020 - 2022 University of Oslo Assosiate professor
2016 - 2020 University of Oslo Tenure track assosiate professor
2012 - 2016 University of Oslo PhD candidate