Giulia Di Nunno

• Banos, David; Cordoni, Francesco; Di Nunno, Giulia; Di Persio, Luca & Røse, Elin Engen (2018). Stochastic systems with memory and jumps. Journal of Differential Equations.  ISSN 0022-0396.  s 1- 49 . doi: 10.1016/j.jde.2018.10.052
• Corcuera, José Manuel & Di Nunno, Giulia (2018). Kyle-Back's model with a random horizon. International Journal of Theoretical and Applied Finance.  ISSN 0219-0249.  21(2) . doi: 10.1142/S0219024918500164
• Baños, David Ruiz; Di Nunno, Giulia; Haferkorn, Hannes Hagen & Proske, Frank Norbert (2017). Stochastic functional differential equations and sensitivity to their initial path. arXiv.org.  ISSN 2331-8422. Full text in Research Archive.
• Bion-Nadal, Jocelyne & Di Nunno, Giulia (2017). Fully-dynamic risk-indifference prices and no-good-deal bounds. arXiv.org.  ISSN 2331-8422. Show summary

  The seller’s risk-indifference price evaluation is studied. We propose a dynamic risk-indifference pricing criteria derived from a fully- dynamic family of risk measures on the L_p-spaces for p ∈ [1, ∞]. The concept of fully-dynamic risk measures extends the one of dynamic risk measures by adding the actual possibility of changing the risk perspectives over time. The family is then characterised by a double time index. Our framework fits well the study of both short and long term investments. In this dynamic framework we analyse whether the risk-indifference pricing criterion actually provides a proper convex price system, for which time-consistency is guaranteed. It turns out that the analysis is quite delicate and necessitates an adequate setting. This entails the use of capacities and an extension of the whole price system to the Banach spaces derived by the capacity seminorms. Furthermore, we consider the relationship of the fully-dynamic risk- indifference price with no-good-deal bounds. Recall that no-good-deal pricing guarantees that not only arbitrage opportunities are excluded, but also all deals that are “too good to be true”. We shall provide necessary and sufficient conditions on the fully-dynamic risk measures so that the corresponding risk-indifference prices satisfy the no-good- deal bounds. The use of no-good-deal bounds also provides a method to select the risk measures and then construct a proper fully-dynamic risk-indifference price system in the L_2-spaces.

• Bion-Nadal, Jocelyne & Di Nunno, Giulia (2017). Representation of convex operators and their static and dynamic sandwich extensions. Journal of Convex Analysis.  ISSN 0944-6532.  24(4), s 1375- 1405
• Di Nunno, Giulia & Haferkorn, Hannes Hagen (2017). A maximum principle for mean-field SDEs with time change. Applied mathematics and optimization.  ISSN 0095-4616.  76(1), s 137- 176 . doi: 10.1007/s00245-017-9426-0 Full text in Research Archive.
• Di Nunno, Giulia & Vives, Josep (2017). A Malliavin-Skorohod calculus in L^0 and L^1 for additive and Volterra-type processes. Stochastics: An International Journal of Probability and Stochastic Processes.  ISSN 1744-2508.  89(1), s 142- 170 . doi: 10.1080/17442508.2016.1140767 Full text in Research Archive. Show summary

  In this paper we develop a Malliavin–Skorohod type calculus for additive processes in the L1 and L1 settings, extending the probabilistic interpretation of the Malliavin–Skorohod operators to this context. We prove calculus rules and obtain a generalization of the Clark–Hausmann–Ocone formula for random variables in L1. Our theory is then applied to extend the stochastic integration with respect to volatility modulated Lévy-driven Volterra processes recently introduced in the literature. Our work yields to substantially weaker conditions that permit to cover integration with respect to e.g. Volterra processes driven by alfa-stable processes with alfa < 2. The presentation focuses on jump type processes.

• Baños, David Ruiz; Cordoni, Francesco; Di Nunno, Giulia; Di Persio, Luca & Elin, Røse (2016). Stochastic systems with memory and jumps. arXiv.org.  ISSN 2331-8422.
• Di Nunno, Giulia & Karlsen, Erik Hove (2016). Hedging under worst-case-scenario in a market driven by time-changed Lévy noises, In Mark Podolskij; Robert Stelzer; Steen Thorbjørnsen & Almut E. D. Veraart (ed.),  The Fascination of Probability, Statistics and their Applications. In honour of Ole E. Barndorff-Nielsen.  Springer Science+Business Media B.V..  ISBN 978-3-319-25824-9.  Chapter 22.  s 465 - 499 Show summary

  In an incomplete market driven by time-changed Lévy noises we consider the problem of hedging a financial position coupled with the underlying risk of model uncertainty. Then we study hedging under worst-case-scenario. The proposed strategies are not necessarily self-financing and include the interplay of a cost process to achieve the perfect hedge at the end of the time horizon. The hedging problem is tackled in the framework of stochastic differential games and it is treated via backward stochastic differential equations. Two different information flows are considered and the solutions compared.

• Di Nunno, Giulia; Mishura, Yuliya & Ralchenko, Kostiantyn (2016). Fractional calculus and pathwise integration for Volterra processes driven by Lévy and martingale noise. Fractional Calculus and Applied Analysis.  ISSN 1311-0454.  19(6), s 1356- 1392 . doi: 10.1515/fca-2016-0071 Show summary

  We introduce a pathwise integration for Volterra processes driven by Lévy noise or martingale noise. These processes are widely used in applications to turbulence, signal processes, biology, and in environmental finance. Indeed they constitute a very flexible class of models, which include fractional Brownian and Lévy motions and it is part of the so-called ambit fields. A pathwise integration with respect of such Volterra processes aims at producing a framework where modelling is easily understandable from an information perspective. The techniques used are based on fractional calculus and in this there is a bridging of the stochastic and deterministic techniques. The present paper aims at setting the basis for a framework in which further computational rules can be devised. Our results are general in the choice of driving noise. Additionally we propose some further details in the relevant context subordinated Wiener processes.

• Di Nunno, Giulia & Zhang, Tusheng (2016). Approximations of stochastic partial differential equations. The Annals of Applied Probability.  ISSN 1050-5164.  26(3), s 1443- 1466 . doi: 10.1214/15-AAP1122
• Benth, Fred Espen; Di Nunno, Giulia; Khedher, Asma & Schmeck, Maren Diane (2015). Pricing of Spread Options on a Bivariate Jump Market and Stability to Model Risk. Applied Mathematical Finance.  ISSN 1350-486X.  22(1), s 28- 62 . doi: 10.1080/1350486X.2014.948708
• Di Nunno, Giulia & Karlsen, Erik Hove (2015). Hedging under worst-case-scenario in a market driven by time-changed Lévy noises. Preprint series (Universitetet i Oslo. Matematisk institutt).  ISSN 0806-2439.
• Di Nunno, Giulia; Khedher, Asma & Vanmaele, Michèle (2015). Robustness of Quadratic Hedging Strategies in Finance via Backward Stochastic Differential Equations with Jumps. Applied mathematics and optimization.  ISSN 0095-4616.  72(3), s 353- 389 . doi: 10.1007/s00245-014-9283-z
• Di Nunno, Giulia & Vives, Josep (2015). A Malliavin-Skorohod calculus in L^0 and L^1 for additive and Volterra-type processes. Preprint series (Universitetet i Oslo. Matematisk institutt).  ISSN 0806-2439.
• Bion-Nadal, Jocelyne & Di Nunno, Giulia (2014). Representation of convex operators and their static and dynamic sandwich extension. Preprint series (Universitetet i Oslo. Matematisk institutt).  ISSN 0806-2439.  (4) Full text in Research Archive.
• Corcuera, Jose Manuel; Di Nunno, Giulia; Farkas, Gergely & Øksendal, Bernt (2014). A continuous auction model with insiders and random time of information release. Preprint series (Universitetet i Oslo. Matematisk institutt).  ISSN 0806-2439.  (3) Full text in Research Archive.
• Di Nunno, Giulia & Sjursen, Steffen A. Søreide (2014). BSDEs driven by time-changed Lévy noises and optimal control. Stochastic Processes and their Applications.  ISSN 0304-4149.  124(4), s 1679- 1709 . doi: 10.1016/j.spa.2013.12.010
• Di Nunno, Giulia & Sjursen, Steffen A. Søreide (2014). Information and optimal investment in defaultable assets. International Journal of Theoretical and Applied Finance.  ISSN 0219-0249.  17(8) . doi: 10.1142/S0219024914500502
• Di Nunno, Giulia & Zhang, Tusheng (2014). Approximations of Stochastic Partial Differential Equations. Preprint series (Universitetet i Oslo. Matematisk institutt).  ISSN 0806-2439.  (1) Full text in Research Archive.
• Benth, Fred Espen; Di Nunno, Giulia & Khedher, Asma (2013). A note on convergence of option prices and their Greeks for Lévy models. Stochastics: An International Journal of Probability and Stochastic Processes.  ISSN 1744-2508.  85(6), s 1015- 1039 . doi: 10.1080/17442508.2012.736994
• Di Nunno, Giulia & Bion-Nadal, Jocelyne (2013). Dynamic no-good-deal pricing measures and extension theorems for linear operators on L-infinity. Finance and Stochastics.  ISSN 0949-2984.  17(3), s 587- 613 . doi: 10.1007/s00780-012-0195-y
• Di Nunno, Giulia & Sjursen, Steffen A. Søreide (2013). On Chaos Representation and Orthogonal Polynomials for the Doubly Stochastic Poisson Process, In  Seminar on Stochastic Analysis, Random Fields and Applications VII.  Birkhäuser Verlag.  ISBN 978-3-0348-0545-2.  Del 1 - kap 2.  s 23 - 54
• Benth, Fred Espen; Di Nunno, Giulia & Khedher, Asma (2012). Computation of Greeks in multifactor models with applications to power and commodity markets. Journal of Energy Markets.  ISSN 1756-3615.  5(4), s 3- 31
• Benth, Fred Espen; Di Nunno, Giulia & Khedher, Asma (2011). Robustness of option prices and their deltas in markets modelled by jump-diffusions. Communications on Stochastic Analysis.  ISSN 0973-9599.  5(2), s 285- 307
• Di Nunno, Giulia & Bion-Nadal, Jocelyne (2011). Extension theorems for linear operators on L_\infty and application to price systems. Preprint series (Universitetet i Oslo. Matematisk institutt).  ISSN 0806-2439.  4
• Di Nunno, Giulia & Eide, Inga Baadshaug (2011). LOWER AND UPPER BOUNDS OF MARTINGALE MEASURE DENSITIES IN CONTINUOUS TIME MARKETS. Mathematical Finance.  ISSN 0960-1627.  21(3), s 475- 492 . doi: 10.1111/j.1467-9965.2010.00442.x
• Di Nunno, Giulia; Pamen, Olivier Menoukeu; Øksendal, Bernt & Proske, Frank Norbert (2011). A general maximum principle for anticipative stochastic control and applications to insider trading, In Giulia Di Nunno & Bernt Øksendal (ed.),  Advanced Mathematical Methods for Finance.  Springer.  ISBN 978-3-642-18411-6.  Chapter.  s 181 - 221
• Di Nunno, Giulia; Øksendal, Bernt; Menoukeu Pamen, Olivier & Proske, Frank Norbert (2011). Uniqueness of Decompositions of Skorohod-Semimartingales. Infinite Dimensional Analysis Quantum Probability and Related Topics.  ISSN 0219-0257.  14(1), s 15- 24 . doi: 10.1142/S0219025711004274
• Benth, Fred Espen; Di Nunno, Giulia & Khedher, Asma (2010). Lévy Models Robustness and Sensitivity, In Habib Ouerdiane & A Barhoumi (ed.),  QUANTUM PROBABILITY AND INFINITE DIMENSIONAL ANALYSIS - Proceedings of the 29th Conference.  World Scientific.  ISBN 978-981-4295-42-0.  Kapitel.  s 153 - 184
• Benth, Fred Espen; Di Nunno, Giulia & Khedher, Asma (2010). Robustness of option prices and their deltas in markets modelled by jump-diffusions. Preprint series (Universitetet i Oslo. Matematisk institutt).  ISSN 0806-2439.  (2)
• Corcuera, José Manuel; Di Nunno, Giulia; Farkas, Gergely & Øksendal, Bernt (2010). Kyle-Back's model with Lévy noise. Preprint series (Universitetet i Oslo. Matematisk institutt).  ISSN 0806-2439.  26

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• Celledoni, Elena; Di Nunno, Giulia; Ebrahimi-Fard, Kurusch & Munthe-Kaas, Hans (ed.) (2018). Computation and Combinatorics in Dynamics, Stochastics and Control. Springer.  ISBN 978-3-030-01592-3.  100 s. Show summary

  The Abel Symposia volume at hand contains a collection of high-quality articles written by the world’s leading experts, and addressing all mathematicians interested in advances in deterministic and stochastic dynamical systems, numerical analysis, and control theory. In recent years we have witnessed a remarkable convergence between individual mathematical disciplines that approach deterministic and stochastic dynamical systems from mathematical analysis, computational mathematics and control theoretical perspectives. Breakthrough developments in these fields now provide a common mathematical framework for attacking many different problems related to differential geometry, analysis and algorithms for stochastic and deterministic dynamics. In the Abel Symposium 2016, which took place from August 16-19 in Rosendal near Bergen, leading researchers in the fields of deterministic and stochastic differential equations, control theory, numerical analysis, algebra and random processes presented and discussed the current state of the art in these diverse fields. The current Abel Symposia volume may serve as a point of departure for exploring these related but diverse fields of research, as well as an indicator of important current and future developments in modern mathematics.

• Benth, Fred Espen & Di Nunno, Giulia (ed.) (2016). Stochastics of Environmental and Financial Economics. Springer Science+Business Media B.V..  ISBN 978-3-319-23424-3.  360 s.
• Di Nunno, Giulia & Øksendal, Bernt (ed.) (2011). Advanced Mathematical Methods for Finance. Springer.  ISBN 978-3-642-18411-6.  536 s.
• Di Nunno, Giulia & Øksendal, Bernt (ed.) (2011). Advanced Mathematical Methods for Finance. Springer.  ISBN 978-3-642-18411-6.  536 s.

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• Di Nunno, Giulia (2018). Kyle-Backs equilibrium model with a random time of information release. Show summary

  The continuous-time version of Kyle (1985)) developed by Back (1992)) is here studied. In Back’s model there is asymmetric information in the market in the sense that there is an insider having infor- mation on the real value of the asset. We extend this model by assuming that the fundamental value evolves with time and that it is announced at a future random time. First we consider the case when the release time of information is predictable to the insider and then when it is not. The goal of the paper is to study the structure of equilibrium, which is described by the optimal insider strategy and the competitive market prices given by the market makers. We provide necessary and sufficient conditions for the optimal insider strategy under general dynamics for the asset demands. Moreover, we study the behavior of the price pressure and the market efficiency. In particular we find that when the random time is not predictable, there can be equilibrium without market efficiency. Furthermore, for the two cases of release time and for classes of pricing rules, we provide a characterization of the equilibrium. The talk is based on joint works with: Jose Manuel Corcuera, Bernt Øksendal, Gergely Farkas

• Di Nunno, Giulia (2018). Sandwich extensions of linear and convex operators and their applications. Show summary

  We propose some extension theorems for linear and convex operators from $L_p$ spaces to $L_p$ spaces that preserve both minoring and majoring stochastic bounds. We shall see how these extension theorems are applied within the context of pricing in mathematical finance. We discuss in fact the concept of a pricing system, which is in fact a family of such operators that has to preserve some reasonable conditions, including the time-consistency to guarantee that the fundamental economic criteria of the non-arbitrage principle is maintained. Various properties and form of pricing operators are presented. This talk is based on a series of works with various co-authors: Sergio Albeverio (U. Bonn), Jocelyne Bion-Nadal (CNRS Ecole Polytechnique), Inga B. Eide (now at Finanstilsynet), Yuri Rozanov (CNR Milano).

• Di Nunno, Giulia (2018). Stochastic systems with memory, robustness and sensitivity. Show summary

  Stochastic systems with memory naturally appear in life science, economy, and finance. We take the modelling point of view of stochastic functional delay equations and we study these structures. We study the case when the driving noises admit jumps provid- ing results on existence and uniqueness of strong solutions, estimates for the moments and the fundamental tools of calculus. We study the robustness of the solution to the change of noises. Specifically, we consider the noises with infinite activity jumps versus an adequately corrected Gaussian noise. In the case of Brownian driving noise, we consider evaluations based on these models (e.g. the prices of some financial products) and the risks connected to the choice of these models. In particular we focus on the impact of the initial condition on the evaluations. This problem is known as the analysis of sensitivity to the initial condition and, in the terminology of finance, it is referred to as the Delta. In this work the initial condition is represented by the relevant past history of the stochastic functional differential equation. This naturally leads to the redesign of the definition of Delta. We suggest to define it as a functional directional derivative, this is a natural choice. For this we study a repre- sentation formula which allows for its computation without requiring that the evaluation functional is differentiable. This feature is particularly relevant for applications. Our techniques make use of stochastic calculus via regularisations, Malliavin/Skorohod calculus and functional derivatives. The presentation is based on joint works with: D.R. Banos, F. Cordoni, L. Di Persio, H.H. Haferkorn, F. Proske, E.E. Røse.

• Di Nunno, Giulia (2017). Control of an economic with specialised sectors: a maximum principle approach for mean-field SDEs with time change. Show summary

  We consider an economy with specialised sectors modelled by a mean- eld SDE driven by time change Levy noises. Time change is a powerful technique for generating noises and providing exible models. In this study we fo- cus on time changed Brownian and Poisson ran- dom measures. We study the existence and uniqueness of the solution to a general mean- eld SDEs. Moreover we consider a mean field stochastic control problem for mean field controlled dynamics. In the application this corresponds to a centralised control for the economy under consideration. We present a necessary and sufficient maximum principle to solve the optimal control problem. For this we study existence and uniqueness of solutions to mean field BSDEs in the context of time change. This is based on joint work with Hannes Haferkorn

• Di Nunno, Giulia (2017). Dynamic risk indifference pricing. Show summary

  We deal with dynamic pricing from the seller's perspective in an incomplete market and we focus on risk indifference pricing. We propose a fully-dynamic risk-indifference criteria, in which a whole family of risk measures is considered. This is based on the concept of fully-dynamic risk measures which extends the one of dynamic risk measures by adding the actual possibility of changing the risk perspectives over time. Our framework fits well the study of both short and long term investments. In the dynamic framework we analyse whether the risk indifference criterion actually provides a proper convex price system. Furthermore, we consider the relationship of the fully-dynamic risk-indifference price with no-good-deal bounds. Recall that no-good-deal pricing guarantees that not only arbitrage opportunities are excluded, but also all deals that are “too good to be true”. We shall provide necessary and sufficient conditions on the fully-dynamic risk measure so that the corresponding risk-indifference prices satisfy the no-good-deal bounds. As it turns out, no-good-deal bounds also provide a method to select the risk measures that provide a proper fully-dynamic risk-indifference price system. The presentation is based on joint works with Jocelyne Bion-Nadal.

• Di Nunno, Giulia (2017). Dynamic risk indifference pricing. Show summary

  We deal with dynamic pricing of financial products in an incomplete market and we focus on risk indifference pricing. This can be seen as an alternative to the classical utility indifference pricing in which the performance is written in terms of evaluation of risks instead of utility. We propose a fully-dynamic risk-indifference criteria, in which a whole family of risk measures is considered. This is based on the concept of fully-dynamic risk measures which extends the one of dynamic risk measures by adding the actual possibility of changing the perspective on how to measure risk over time. Our framework fits well the study of both short and long term investments. Risk indifference pricing has been studied in the terms of how to find a solution and express the actual price under different assumptions on the underlying dynamics and information flows. Typically the price is evaluated at time of the initial investment t=0. We are interested in studying the pricing criterion in its time evolution in a setting free from specific choices of underlying dynamics. In the dynamic framework we analyse whether the risk indifference criterion actually provides a proper convex price system. Furthermore, we consider the relationship of the fully-dynamic risk-indifference price with no-good-deal bounds. Recall that no-good-deal pricing guarantees that not only arbitrage opportunities are excluded, but also all deals that are “too good to be true”. We shall provide necessary and sufficient conditions on the fully-dynamic risk measure so that the corresponding risk-indifference prices satisfy the no-good-deal bounds. As it turns out, no-good-deal bounds also provide a method to select the risk measures that provide a proper fully-dynamic risk-indifference price system. The presentation is based on joint works with Jocelyne Bion-Nadal.

• Di Nunno, Giulia (2017). Fully-dynamic risk indifference pricing. Show summary

  We deal with dynamic pricing of financial products in an incomplete market and we focus on risk-indifference pricing. This can be seen as an alternative to the classical utility-indifference pricing in which the performance is written in terms of evaluation of risks instead of utility. We propose a fully-dynamic risk-indifference criteria, in which a whole family of risk measures is considered. This is based on the concept of fully-dynamic risk measures which extends the one of dynamic risk measures by adding the actual possibility of changing the perspective on how to measure risk over time. Our framework fits well the study of both short and long term investments. Risk-indifference pricing has been studied from the point of view of how to find a solution under different assumptions on the underlying dynamics and information flows. Typically the price is also evaluated at the time of the initial investment t=0. We are interested in studying the risk-indifference pricing criterion in its time evolution in a setting free from specific choices of underlying dynamics. In the dynamic framework we analyse whether the risk-indifference criterion actually provides a proper convex price system. Furthermore, we consider the relationship of the fully-dynamic risk-indifference price with no-good-deal bounds. Recall that no-good-deal pricing guarantees that not only arbitrage opportunities are excluded, but also all deals that are “too good to be true”. We shall provide necessary and sufficient conditions on the fully-dynamic risk measure so that the corresponding risk-indifference prices satisfy the no-good-deal bounds. As it turns out, no-good-deal bounds also provide a method to select the risk measures that provide a proper fully-dynamic risk-indifference price system. Based on joint work with Jocelyne Bion-Nadal

• Di Nunno, Giulia (2017). Fully-dynamic risk-indifference pricing with no-good-deal bounds. Show summary

  We deal with the pricing of claims in an incomplete market and we focus on risk-indifference pricing techniques. We propose a fully-dynamic risk-indifference criteria, in which a whole family of risk measures is considered. This is based on the concept of fully-dynamic risk measures which extends the one of dynamic risk measures by adding the actual possibility of changing the risk perspectives over time. In this talk we shall explain what risk-indifference pricing is, convex risk measures and how these are going to be used. We shall use techniques of functional analysis mixed with probability theory to study these price operators and see if they fulfill the criteria to be proper convex prices. We restrict the presentation to the L2-framework where we can exploit the dynamic no-good- deal bounds to obtain a complete characterization of these dynamic prices. We shall recall that no-good-deal prices are those prices that guarantee that in the market there are no arbitrage opportunities and there are ”no deals that are too good to be true”. The presentation is based on joint works with Jocelyne Bion-Nadal

• Di Nunno, Giulia (2017). Introduction to Levy Processes and Applications to Finance.
• Di Nunno, Giulia (2017). Malliavin Calculus for Lévy processes and Time-Change.
• Di Nunno, Giulia (2017). Mathematics, Modelling, Time and Chaos.
• Di Nunno, Giulia (2017). On the integration with respect to Volterra processes: fractional calculus and approximation. Show summary

  In the first part we discuss a pathwise integration for Volterra processes driven by L\'evy noise or martingale noise based on fractional calculus. We obtain an integral with respect to a non-semimartingale process. Different from other recent approaches, this integration provides a framework for modelling where it is easy to keep track of the information flow. Then it can be attractive for applications, for example in finance and energy finance, where information is often linked to portfolio management and control. Then we study an approximation of the integrals introduced above based on the perturbation of the noise, which provides a semimartingale approximation of the integral. This turns to be interesting for simulations. This is based on joint work with Yuliya Mishura, Kostia Ralchenko, Erik H. Karlsen

• Di Nunno, Giulia & Isaksen, Karoline Kvellestad (2017, 08. mars). Det er vanskeligere for kvinner å komme seg opp og fram i matematikken. Intervju til: Professor Berit Stensønes (CAS gruppeleder 2016/17, NTNU), Professor Giulia Di Nunno (CAS gruppeleder 2014/15, UiO), Professor Knut Liestøl (Styreleder for Forskningsrådet BALANSE project, UiO), Professor Geir Ellingsrud (CAS Styreleder, UiO).  Forskning.no.
• Di Nunno, Giulia (2016). A Malliavin-Skorohod calculus in L^0 and L^1 for additive and Volterra-type processes.
• Di Nunno, Giulia (2016). Risk indifference pricing and dynamic no-good-deal bounds.
• Di Nunno, Giulia (2016). Sensitivity analysis in a market with memory. A join work with D.R.Banos, H.Haferkorn, f. Proske.
• Di Nunno, Giulia (2016). Sensitivity analysis in a market with memory. Work in collaboration with D.R. Banos, H. Haferkorn, F. Proske..
• Di Nunno, Giulia (2016). Series of Lectures on Levy Processes and Applications to Finance.
• Di Nunno, Giulia (2016). Stochastic systems with memory and jumps.
• Di Nunno, Giulia (2015). Dynamic no good deal bounds: linear and convex price systems.
• Di Nunno, Giulia (2015). Dynamic no good deal bounds: linear and convex price systems.
• Di Nunno, Giulia (2015). Intensive course: Malliavin Calculus for Levy Processes.
• Aarønæs, Lars; Benth, Fred Espen & Di Nunno, Giulia (2014, 01. oktober). Hvordan beregner vi framtida?. [Tidsskrift].  GLIMT - CAS Informasjonsblad.
• Di Nunno, Giulia (2014). A continuous auction model with insiders.
• Di Nunno, Giulia (2014). A continuous auction model with insiders and information release.
• Di Nunno, Giulia (2014). Optimal portfolio problems with price dynamics driven by time-changed Levy noises.
• Di Nunno, Giulia (2014). Optimal portfolios in markets driven by time-changed Levy noises.
• Di Nunno, Giulia (2014). Time-changed Levy processes and hedging formulae.
• Di Nunno, Giulia (2013). BSDEs driven by time-changed Levy noises and optimal control. Based on joint work with Steffen Sjursen.
• Di Nunno, Giulia (2013). BSDEs driven by time-changed Levy noises and optimal control.Based on joint work with Steffen Sjursen.
• Di Nunno, Giulia (2013). Backward stochastic differential equations with applications to dynamic risk measures. Series of 5 lectures.
• Di Nunno, Giulia (2013). Introduction to stochastic calculus and stochastic differential equations. Series of 8 lectures.
• Di Nunno, Giulia (2013). Market with memory: pricing and sensitivity analysis. Based on joint work with Frank Proske and David Banos.
• Di Nunno, Giulia (2013). Quadratic Hedging via Backward Stochastic Differential Equations with Jumps. Based on joint work with Asma Khedher and Michele Vanmaele.
• Di Nunno, Giulia (2013). Robustness of BSDEs and applications to quadratic hedging. Based on joint work with Asma Khedher and Michele Vanmaele.
• Di Nunno, Giulia (2013). Robustness of Quadratic Hedging Strategies in Finance via Backward Stochastic Differential Equations with Jumps. Based on joint work with Asma Khedher and Michele Vanmaele.
• Di Nunno, Giulia (2013). Robustness of quadratic hedging strategies to model risk. Based on joint work with Asma Khedher and Michele Vanmaele.
• Di Nunno, Giulia; Khedher, Asma & Vanmaele, Michèle (2013). Robustness of quadratic hedging strategies in finance via backward stochastic differential equations with jumps. Preprint series: Pure mathematics. 9. Full text in Research Archive.
• Sjursen, Steffen A. Søreide & Di Nunno, Giulia (2013). BSDES DRIVEN BY TIME-CHANGED LEVY NOISES AND OPTIMAL CONTROL. Preprint series (Universitetet i Oslo. Matematisk institutt). 1. Full text in Research Archive.
• Benth, Fred Espen; Di Nunno, Giulia; Khedher, Asma & Schmeck, Maren Diane (2012). Pricing of spread options on a bivariate jump market and stability to model risk. Preprint series (Universitetet i Oslo. Matematisk institutt). 2. Full text in Research Archive.
• Benth, Fred Espen; Di Nunno, Giulia; Khedher, Asma & Schmeck, Maren Diane (2012). Spread options and stability to model risk.
• Di Nunno, Giulia (2012). Aspects of Malliavin Calculus.
• Di Nunno, Giulia (2012). Sensitivity analysis and computation of the Greeks.
• Di Nunno, Giulia & Bion-Nadal, Jocelyne (2012). Dynamic no good deal bounds and pricing measures.
• Di Nunno, Giulia & Bion-Nadal, Jocelyne (2012). Dynamic no good deal pricing measures.
• Di Nunno, Giulia; L'Aurora, Edoardo; Moschetta, Marina; Proske, Frank Norbert & Ruiz-Banos, David (2012). Market with memory and sensitivity to the past.
• Di Nunno, Giulia; L'Aurora, Edoardo; Moschetta, Marina; Proske, Frank Norbert & Ruiz-Banos, David (2012). Market with memory: pricing and sensitivity analysis.
• Di Nunno, Giulia & Sjursen, Steffen A. Søreide (2012). Doubly stochastic Poisson random fields: from integral representations to BSDEs.
• Di Nunno, Giulia & Sjursen, Steffen A. Søreide (2012). Integral representations and BSDEs driven by doubly stochastic Poisson processes.
• Di Nunno, Giulia & Sjursen, Steffen A. Søreide (2012). On chaos representation and orthogonal polynomials for the doubly stochastic Poisson process. Preprint series (Universitetet i Oslo. Matematisk institutt). 1. Full text in Research Archive.
• Sjursen, Steffen A. Søreide & Di Nunno, Giulia (2012). On chaos representation and orthogonal polynomials for the doubly stochastic Poisson process.
• Di Nunno, Giulia & Bion-Nadal, Jocelyne (2011). Dynamic no-good-deal bounds and no-good-deal pricing measures.
• Di Nunno, Giulia & Bion-Nadal, Jocelyne (2011). Extension theorems for linear operators and dynamic no-good-deal pricing measures.
• Di Nunno, Giulia & Bion-Nadal, Jocelyne (2011). Extension theorems for operators and application to pricing.
• Di Nunno, Giulia & Sjursen, Steffen A. Søreide (2011). Doubly stochastic Poisson random fields: theory and applications to finance.
• Di Nunno, Giulia & Sjursen, Steffen A. Søreide (2011). Information and optimal investment in defaultable assets.
• Di Nunno, Giulia & Sjursen, Steffen A. Søreide (2011). On stochastic calculus with respect to doubly stochastic Poisson random fields.
• Di Nunno, Giulia & Sjursen, Steffen A. Søreide (2011). Orthogonal polynomials and stochastic calculus for doubly stochastic Poisson random fields.
• Benth, Fred Espen; Di Nunno, Giulia & Khedher, Asma (2010). A note on convergence of option prices and their Greeks for Lévy models. Preprint series (Universitetet i Oslo. Matematisk institutt). 18.
• Di Nunno, Giulia (2010). Information in optimal portfolio choices.Based on joint work with: T. Meyer-Brandis, B. Øksendal, F. Proske, and S. Sjursen.
• Di Nunno, Giulia (2010). Minimal Variance Hedging in incomplete markets. Stochastic differentiation and the Clark-Ocone formula.
• Di Nunno, Giulia (2010). Minimal Variance Hedging in incomplete markets:stochastic differentiation and the Clark-Ocone formula.
• Di Nunno, Giulia (2010). Price and sensitivity robustness to model risk. Based on joint work with F.E. Benth and A. Khedher.
• Di Nunno, Giulia (2010). Time consistent linear and convex price systems in L_p. Based on joint work with J. Bion-Nadal.

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