Probability, Uncertainty and Quantitative Risk

January 2016 , Volume 1

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A branching particle system approximation for a class of FBSDEs
Dejian Chang, Huili Liu and Jie Xiong
2016, 1: 9 doi: 10.1186/s41546-016-0007-y +[Abstract](732) +[PDF](665.58KB)
In this paper, a new numerical scheme for a class of coupled forwardbackward stochastic differential equations (FBSDEs) is proposed by using branching particle systems in a random environment. First, by the four step scheme, we introduce a partial differential Eq. (PDE) used to represent the solution of the FBSDE system. Then, infinite and finite particle systems are constructed to obtain the approximate solution of the PDE. The location and weight of each particle are governed by stochastic differential equations derived from the FBSDE system. Finally, a branching particle system is established to define the approximate solution of the FBSDE system. The branching mechanism of each particle depends on the path of the particle itself during its short lifetime =n-2α, where n is the number of initial particles and α < $\frac{1}{2}$ is a fixed parameter. The convergence of the scheme and its rate of convergence are obtained.
Backward-forward linear-quadratic mean-field games with major and minor agents
Jianhui Huang, Shujun Wang and Zhen Wu
2016, 1: 8 doi: 10.1186/s41546-016-0009-9 +[Abstract](864) +[PDF](593.73KB)
This paper studies the backward-forward linear-quadratic-Gaussian (LQG) games with major and minor agents (players). The state of major agent follows a linear backward stochastic differential equation (BSDE) and the states of minor agents are governed by linear forward stochastic differential equations (SDEs). The major agent is dominating as its state enters those of minor agents. On the other hand, all minor agents are individually negligible but their state-average affects the cost functional of major agent. The mean-field game in such backward-major and forward-minor setup is formulated to analyze the decentralized strategies. We first derive the consistency condition via an auxiliary mean-field SDEs and a 3×2 mixed backward-forward stochastic differential equation (BFSDE) system. Next, we discuss the wellposedness of such BFSDE system by virtue of the monotonicity method. Consequently, we obtain the decentralized strategies for major and minor agents which are proved to satisfy the -Nash equilibrium property.
Linear quadratic optimal control of conditional McKean-Vlasov equation with random coefficients and applications
Huyên Pham
2016, 1: 7 doi: 10.1186/s41546-016-0008-x +[Abstract](1295) +[PDF](636.32KB)
We consider the optimal control problem for a linear conditional McKeanVlasov equation with quadratic cost functional. The coefficients of the system and the weighting matrices in the cost functional are allowed to be adapted processes with respect to the common noise filtration. Semi closed-loop strategies are introduced, and following the dynamic programming approach in (Pham and Wei, Dynamic programming for optimal control of stochastic McKean-Vlasov dynamics, 2016), we solve the problem and characterize time-consistent optimal control by means of a system of decoupled backward stochastic Riccati differential equations. We present several financial applications with explicit solutions, and revisit, in particular, optimal tracking problems with price impact, and the conditional mean-variance portfolio selection in an incomplete market model.
Pseudo-Markovian viscosity solutions of fully nonlinear degenerate PPDEs
Ibrahim Ekren and Jianfeng Zhang
2016, 1: 6 doi: 10.1186/s41546-016-0010-3 +[Abstract](940) +[PDF](725.05KB)
In this paper, we propose a new type of viscosity solutions for fully nonlinear path-dependent PDEs. By restricting the solution to a pseudo-Markovian structure defined below, we remove the uniform non-degeneracy condition needed in our earlier works (Ekren, I, Touzi, N, Zhang, J, Ann Probab, 44:1212-1253, 2016a; Ekren, I, Touzi, N, Zhang, J, Ann Probab, 44:2507-2553, 2016b) to establish the uniqueness result. We establish the comparison principle under natural and mild conditions. Moreover, we apply our results to two important classes of PPDEs:the stochastic HJB equations and the path-dependent Isaacs equations, induced from the stochastic optimization with random coefficients and the path-dependent zero-sum game problem, respectively.
Shige Peng, Rainer Buckdahn and Juan Li
2016, 1: 5 doi: 10.1186/s41546-016-0006-z +[Abstract](837) +[PDF](3504.39KB)
On approximation of BSDE and multi-step MLE-processes
Yu A. Kutoyants
2016, 1: 4 doi: 10.1186/s41546-016-0005-0 +[Abstract](691) +[PDF](577.61KB)
We consider the problem of approximation of the solution of the backward stochastic differential equations in Markovian case. We suppose that the forward equation depends on some unknown finite-dimensional parameter. This approximation is based on the solution of the partial differential equations and multi-step estimator-processes of the unknown parameter. As the model of observations of the forward equation we take a diffusion process with small volatility. First we establish a lower bound on the errors of all approximations and then we propose an approximation which is asymptotically efficient in the sense of this bound. The obtained results are illustrated on the example of the Black and Scholes model.
Pathwise no-arbitrage in a class of Delta hedging strategies
Alexander Schied and Iryna Voloshchenko
2016, 1: 3 doi: 10.1186/s41546-016-0003-2 +[Abstract](641) +[PDF](669.59KB)
We consider a strictly pathwise setting for Delta hedging exotic options, based on Föllmer's pathwise Itô calculus. Price trajectories are d-dimensional continuous functions whose pathwise quadratic variations and covariations are determined by a given local volatility matrix. The existence of Delta hedging strategies in this pathwise setting is established via existence results for recursive schemes of parabolic Cauchy problems and via the existence of functional Cauchy problems on path space. Our main results establish the nonexistence of pathwise arbitrage opportunities in classes of strategies containing these Delta hedging strategies and under relatively mild conditions on the local volatility matrix.
Mean-field stochastic linear quadratic optimal control problems: closed-loop solvability
Xun Li, Jingrui Sun and Jiongmin Yong
2016, 1: 2 doi: 10.1186/s41546-016-0002-3 +[Abstract](1157) +[PDF](641.71KB)
An optimal control problem is studied for a linear mean-field stochastic differential equation with a quadratic cost functional. The coefficients and the weighting matrices in the cost functional are all assumed to be deterministic. Closedloop strategies are introduced, which require to be independent of initial states; and such a nature makes it very useful and convenient in applications. In this paper, the existence of an optimal closed-loop strategy for the system (also called the closedloop solvability of the problem) is characterized by the existence of a regular solution to the coupled two (generalized) Riccati equations, together with some constraints on the adapted solution to a linear backward stochastic differential equation and a linear terminal value problem of an ordinary differential equation.
Portfolio theory for squared returns correlated across time
Ernst Eberlein and Dilip B. Madan
2016, 1: 1 doi: 10.1186/s41546-016-0001-4 +[Abstract](631) +[PDF](1474.12KB)
Allowing for correlated squared returns across two consecutive periods, portfolio theory for two periods is developed. This correlation makes it necessary to work with non-Gaussian models. The two-period conic portfolio problem is formulated and implemented. This development leads to a mean ask price frontier, where the latter employs concave distortions. The modeling permits access to skewness via randomized drifts. Optimal portfolios maximize a conservative market value seen as a bid price for the portfolio. On the mean ask price frontier we observe a tradeoff between the deterministic and random drifts and the volatility costs of increasing the deterministic drift. From a historical perspective, we also implement a mean-variance analysis. The resulting mean-variance frontier is three-dimensional expressing the minimal variance as a function of the targeted levels for the deterministic and random drift.



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