A class of recursive optimal stopping problems with applications to stock trading

In this paper we introduce and solve a class of optimal stopping problems of recursive type. In particular, the stopping payoff depends directly on the value function of the problem itself. In a multi-dimensional Markovian setting we show that the problem is well posed, in the sense that the value is indeed the unique solution to a fixed point problem in a suitable space of continuous functions, and an optimal stopping time exists. We then apply our class of problems to a model for stock trading in two different market venues and we determine the optimal stopping rule in that case.Comment: 35 pages, 2 figures. In this version, we provide a general analysis of a class of recursive optimal stopping problems with both finite-time and infinite-time horizon. We also discuss other application

Dynkin games with incomplete and asymmetric information

We study the value and the optimal strategies for a two-player zero-sum optimal stopping game with incomplete and asymmetric information. In our Bayesian set-up, the drift of the underlying diffusion process is unknown to one player (incomplete information feature), but known to the other one (asymmetric information feature). We formulate the problem and reduce it to a fully Markovian setup where the uninformed player optimises over stopping times and the informed one uses randomised stopping times in order to hide their informational advantage. Then we provide a general verification result which allows us to find the value of the game and players' optimal strategies by solving suitable quasi-variational inequalities with some non-standard constraints. Finally, we study an example with linear payoffs, in which an explicit solution of the corresponding quasi-variational inequalities can be obtained.Comment: 31 pages, 5 figures, small changes in the terminology from game theor

Stopping spikes, continuation bays and other features of optimal stopping with finite-time horizon

We consider optimal stopping problems with finite-time horizon and state-dependent discounting. The underlying process is a one-dimensional linear diffusion and the gain function is time-homogeneous and difference of two convex functions. Under mild technical assumptions with local nature we prove fine regularity properties of the optimal stopping boundary including its continuity and strict monotonicity. The latter was never proven with probabilistic arguments. We also show that atoms in the signed measure associated with the second order spatial derivative of the gain function induce geometric properties of the continuation/stopping set that cannot be observed with smoother gain functions (we call them \emph{continuation bays} and \emph{stopping spikes}). The value function is continuously differentiable in time without any requirement on the smoothness of the gain function.Comment: 41 pages, 2 figures; added more details and fixed some technical problems; main results remain unchange

A change of variable formula with applications to multi-dimensional optimal stopping problems

We derive a change of variable formula for $C^1$ functions $U:\mathbb{R}_+\times\mathbb{R}^m\to\mathbb{R}$ whose second order spatial derivatives may explode and not be integrable in the neighbourhood of a surface $b:\mathbb{R}_+\times\mathbb{R}^{m-1}\to \mathbb{R}$ that splits the state space into two sets $\mathcal{C}$ and $\mathcal{D}$. The formula is tailored for applications in problems of optimal stopping where it is generally very hard to control the second derivatives of the value function near the optimal stopping boundary. Differently to other existing papers on similar topics we only require that the surface $b$ be monotonic in each variable and we formally obtain the same expression as the classical It\^o's formula.Comment: 19 pages; added examples in Section

Dynamic programming principle for classical and singular stochastic control with discretionary stopping

We prove the dynamic programming principle (DPP) in a class of problems where an agent controls a $d$-dimensional diffusive dynamics via both classical and singular controls and, moreover, is able to terminate the optimisation at a time of her choosing, prior to a given maturity. The time-horizon of the problem is random and it is the smallest between a fixed terminal time and the first exit time of the state dynamics from a Borel set. We consider both the cases in which the total available fuel for the singular control is either bounded or unbounded. We build upon existing proofs of DPP and extend results available in the traditional literature on singular control (e.g., Haussmann and Suo, SIAM J. Control Optim., 33, 1995) by relaxing some key assumptions and including the discretionary stopping feature. We also connect with more general versions of the DPP (e.g., Bouchard and Touzi, SIAM J. Control Optim., 49, 2011) by showing in detail how our class of problems meets the abstract requirements therein

A numerical scheme for stochastic differential equations with distributional drift

In this paper we present a scheme for the numerical solution of stochastic differential equations (SDEs) with distributional drift. The approximating process, obtained by the scheme, converges in law to the (virtual) solution of the SDE in a general multi-dimensional setting. When we restrict our attention to the case of a one-dimensional SDE we also obtain a rate of convergence in a suitable $L^1$-norm. Moreover, we implement our method in the one-dimensional case, when the drift is obtained as the distributional derivative of a sample path of a fractional Brownian motion. To the best of our knowledge this is the first paper to study (and implement) numerical solutions of SDEs whose drift cannot be expressed as a function of the state.Comment: 35 pages, 8 figure