Michele Giordano

• Giordano, Michele & Yurchenko-Tytarenko, Anton (2021). Optimal control in linear stochastic advertising models with memory.
• Giordano, Michele & Nunno, Giulia Di (2020). Lifting of Volterra processes: optimal control and HJB equations.
• Giordano, Michele (2020). Lifts of Volterra processes: optimal control and HJB equations. Show summary

  Based on a joint work with Giulia Di Nunno

• Giordano, Michele (2020). Lifts of Volterra processes: optimal control and HJB equations. Show summary

  We want to maximize a performance functional, with dynamics of the underlying forward process of Volterra type. Due to this assumption, the system is not Markovian, and one cannot apply directly the dynamic programming principle (DPP). In order to recover the DPP, we introduce a lift that allows us to get some Markov properties by rewriting the forward process' dynamics in a Banach space setting. Once we reformulated the problem in the new setting, we deduce a DPP for the lifted problem and obtain the corresponding infinite dimensional HJB equations. We go back to the initial real valued problem and characterize the optimal control of the original problem in terms of the solution of the lifted one. Examples and applications will be presented. Joint work with Giulia di Nunno

• Giordano, Michele (2020). Lifts of Volterra processes: optimal control and HJB equations. Show summary

  Based on a joint work with Giulia di Nunno

• Giordano, Michele (2020). Maximum Principles for Stochastic Time Changed Volterra Games. Show summary

  We establish a framework for the study of forward-backward stochastic Volterra integral games (FBSVIG) driven by time-changed Lévy noises. We use a maximum principle approach in order to find the Nash equilibriums of a two player game based on a forward equation and a backward equation for each player. Since we work with time change, we deal with two information flows and we consider one filtration to have partial information respect to the other. Examples will be provided. Joint work with Giulia di Nunno Based on a joint work with Giulia di Nunno

• Giordano, Michele (2020). Lifts of Volterra processes: optimal control and HJB equations.
• Giordano, Michele (2019). A Maximum principle for Volterra Time Changed Processes. Show summary

  We establish a framework for the study of backward stochastic Volterra integral equations (BSVIE) driven by time-changed Lévy noises. In fact we shall consider a random measure $\mu$ that can be decomposed as the sum of a conditional Gaussian measure and a conditional centered Poisson measure. In this paper we deal with two information flows: $F_t$ namely the smallest right continuous filtration to which $\mu$ is adapted, $G_t$, generated by $\mu$ and the entire history of the time change processes, and we shall consider the information $F$ as partial with respect to $G$. Given a controlled dynamic, we will consider the optimization problem of finding the supremum of a functional $J(u)$ for a suitable control set $A$ which we consider to be either $F$ or $G$ predictable. We prove both a sufficient and a necessary maximum principle for such performance functional, showing that in the $F$-predictable case we can find a solution by projecting the results obtained for the $G$-predictable case onto the $F$-predictable one. We shall make use of stochastic derivatives. We stress that we cannot use the classical Malliavin calculus as our integrators are not the Brownian motion nor the centered Poisson random measure. Indeed we could use a conditional form of such calculus as introduced by Yablonski, however we resolve by using the non-anticipating derivative introduced and for martingale random fields as integrators. The use of the non-anticipating derivative has also the advantage that we do not require more restrictive conditions on domains, since it is already well defined for all $L^2(P)$ random variables. When studying such problems, we come across a BSVIE driven by a noise $\mu$ as above: we prove existence and uniqueness results for such BSVIE and we compute an explicit solution in the linear case. Examples and applications will be presented.

• Giordano, Michele (2019). A Maximum principle for Volterra Time Changed Processes. Show summary

  We establish a framework for the study of backward stochastic Volterra integral equations (BSVIE) driven by time-changed Lévy noises. In fact we shall consider a random measure $\mu$ that can be decomposed as the sum of a conditional Gaussian measure and a conditional centered Poisson measure. In this paper we deal with two information flows: $F_t$ namely the smallest right continuous filtration to which $\mu$ is adapted, $G_t$, generated by $\mu$ and the entire history of the time change processes, and we shall consider the information $F$ as partial with respect to $G$. Given a controlled dynamic, we will consider the optimization problem of finding the supremum of a functional $J(u)$ for a suitable control set $A$ which we consider to be either $F$ or $G$ predictable. We prove both a sufficient and a necessary maximum principle for such performance functional, showing that in the $F$-predictable case we can find a solution by projecting the results obtained for the $G$-predictable case onto the $F$-predictable one. We shall make use of stochastic derivatives. We stress that we cannot use the classical Malliavin calculus as our integrators are not the Brownian motion nor the centered Poisson random measure. Indeed we could use a conditional form of such calculus as introduced by Yablonski, however we resolve by using the non-anticipating derivative introduced and for martingale random fields as integrators. The use of the non-anticipating derivative has also the advantage that we do not require more restrictive conditions on domains, since it is already well defined for all $L^2(P)$ random variables. When studying such problems, we come across a BSVIE driven by a noise $\mu$ as above: we prove existence and uniqueness results for such BSVIE and we compute an explicit solution in the linear case. Examples and applications will be presented.

• Giordano, Michele (2019). A Maximum principle for Volterra time changed processes. Show summary

  We establish a framework for the study of backward stochastic Volterra integral equations (BSVIE) driven by time-changed Lévy noises. In fact we shall consider a random measure $\mu$ that can be decomposed as the sum of a conditional Gaussian measure and a conditional centered Poisson measure. In this paper we deal with two information flows: $F_t$ namely the smallest right continuous filtration to which $\mu$ is adapted, $G_t$, generated by $\mu$ and the entire history of the time change processes, and we shall consider the information $F$ as partial with respect to $G$. Given a controlled dynamic, we will consider the optimization problem of finding the supremum of a functional $J(u)$ for a suitable control set $A$ which we consider to be either $F$ or $G$ predictable. We prove both a sufficient and a necessary maximum principle for such performance functional, showing that in the $F$-predictable case we can find a solution by projecting the results obtained for the $G$-predictable case onto the $F$-predictable one. We shall make use of stochastic derivatives. We stress that we cannot use the classical Malliavin calculus as our integrators are not the Brownian motion nor the centered Poisson random measure. Indeed we could use a conditional form of such calculus as introduced by Yablonski, however we resolve by using the non-anticipating derivative introduced and for martingale random fields as integrators. The use of the non-anticipating derivative has also the advantage that we do not require more restrictive conditions on domains, since it is already well defined for all $L^2(P)$ random variables. When studying such problems, we come across a BSVIE driven by a noise $\mu$ as above: we prove existence and uniqueness results for such BSVIE and we compute an explicit solution in the linear case. Examples and applications will be presented.

• Giordano, Michele (2019). A Maximum Principle for Volterra Time Changed Processes. Show summary

  We establish a framework for the study of backward stochastic Volterra integral equations (BSVIE) driven by time-changed Lévy noises. In fact we shall consider a random measure $\mu$ that can be decomposed as the sum of a conditional Gaussian measure and a conditional centered Poisson measure. In this paper we deal with two information flows: $F_t$ namely the smallest right continuous filtration to which $\mu$ is adapted, $G_t$, generated by $\mu$ and the entire history of the time change processes, and we shall consider the information $F$ as partial with respect to $G$. Given a controlled dynamic, we will consider the optimization problem of finding the supremum of a functional $J(u)$ for a suitable control set $A$ which we consider to be either $F$ or $G$ predictable. We prove both a sufficient and a necessary maximum principle for such performance functional, showing that in the $F$-predictable case we can find a solution by projecting the results obtained for the $G$-predictable case onto the $F$-predictable one. We shall make use of stochastic derivatives. We stress that we cannot use the classical Malliavin calculus as our integrators are not the Brownian motion nor the centered Poisson random measure. Indeed we could use a conditional form of such calculus as introduced by Yablonski, however we resolve by using the non-anticipating derivative introduced and for martingale random fields as integrators. The use of the non-anticipating derivative has also the advantage that we do not require more restrictive conditions on domains, since it is already well defined for all $L^2(P)$ random variables. When studying such problems, we come across a BSVIE driven by a noise $\mu$ as above: we prove existence and uniqueness results for such BSVIE and we compute an explicit solution in the linear case. Examples and applications will be presented.

• Giordano, Michele (2019). A Maximum Principle for Volterra Time Changed Processes . Show summary

  We establish a framework for the study of backward stochastic Volterra integral equations (BSVIE) driven by time-changed Lévy noises. In fact we shall consider a random measure $\mu$ that can be decomposed as the sum of a conditional Gaussian measure and a conditional centered Poisson measure. In this paper we deal with two information flows: $F_t$ namely the smallest right continuous filtration to which $\mu$ is adapted, $G_t$, generated by $\mu$ and the entire history of the time change processes, and we shall consider the information $F$ as partial with respect to $G$. Given a controlled dynamic, we will consider the optimization problem of finding the supremum of a functional $J(u)$ for a suitable control set $A$ which we consider to be either $F$ or $G$ predictable. We prove both a sufficient and a necessary maximum principle for such performance functional, showing that in the $F$-predictable case we can find a solution by projecting the results obtained for the $G$-predictable case onto the $F$-predictable one. We shall make use of stochastic derivatives. We stress that we cannot use the classical Malliavin calculus as our integrators are not the Brownian motion nor the centered Poisson random measure. Indeed we could use a conditional form of such calculus as introduced by Yablonski, however we resolve by using the non-anticipating derivative introduced and for martingale random fields as integrators. The use of the non-anticipating derivative has also the advantage that we do not require more restrictive conditions on domains, since it is already well defined for all $L^2(P)$ random variables. When studying such problems, we come across a BSVIE driven by a noise $\mu$ as above: we prove existence and uniqueness results for such BSVIE and we compute an explicit solution in the linear case. Examples and applications will be presented.

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