Stochastic Optimal Control in Mathematical Finance
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STOCHASTIC CONTROL Contents 1. Introduction 1 2. Mathematical Probability 1 3. Stochastic Calculus 3 4. Diffusions 7 5. the Stoc
STOCHASTIC CONTROL NAFTALI HARRIS Abstract. In this paper we discuss the Stochastic Control Problem, which deals with the best way to control a stochastic system and has applications in fields as diverse as robotics, finance, and industry. After quickly reviewing mathematical probability and stochastic integration, we prove the Hamilton- Jacobi-Bellman equations for stochastic control, which transform the Stochas- tic Control Problem into a partial differential equation. Contents 1. Introduction 1 2. Mathematical Probability 1 3. Stochastic Calculus 3 4. Diffusions 7 5. The Stochastic Control Problem 11 6. The Hamilton-Jacobi-Bellman Equation 12 7. Acknowledgements 18 References 18 1. Introduction The Stochastic Control Problem arises when we wish to control a continuous- time random system optimally. For example, we wish to fly an airplane through turbulence, or manage a stock portfolio, or precisely maintain the temperature and pressure of a system in the presence of random air currents and temperatures. To build up to a formal definition of this problem, we will first review mathematical probability and stochastic calculus. These will give us the tools to talk about It^o processes and diffusions, which will allow us to state the Stochastic Control Problem formally. Finally, we will spend the latter half of this paper proving necessary conditions and sufficient conditions for its solution. 2. Mathematical Probability In this section we briskly define the mathematical model of probability required in the rest of this paper. We define probability spaces, random variables, and conditional expectation. For more detail, see Williams [4]. Definition 2.1 (Probability Space). A probability space is a triple (Ω; F;P ), where Ω is an arbitrary set, F is a sigma algebra over Ω, and P is a measure on sets in F with P (Ω) = 1. -
Upper and Lower Snell Envelopes and Robust Partial Hedging
American Options in incomplete Markets: Upper and lower Snell Envelopes and robust partial Hedging DISSERTATION zur Erlangung des akademischen Grades doctor rerum naturalium (Dr. rer. nat.) im Fach Mathematik eingereicht an der Mathematisch-Naturwissenschaftlichen Fakultät II Humboldt-Universität zu Berlin von Herr Dipl. Math. Erick Treviño Aguilar geboren am 24.03.1975 in México Präsident der Humboldt-Universität zu Berlin: Prof. Dr. Christoph Markschies Dekan der Mathematisch-Naturwissenschaftlichen Fakultät II: Prof. Dr. Wolfgang Coy Gutachter: 1. Prof. Dr. Hans Föllmer 2. Prof. Dr. Peter Imkeller 3. Prof. Dr. Frank Riedel eingereicht am: 14. Dezember 2007 Tag der mündlichen Prüfung: 9. Juni 2008 Abstract This thesis studies American options in an incomplete financial market and in continuous time. It is composed of two parts. In the first part we study a stochastic optimization problem in which a robust convex loss functional is minimized in a space of stochastic integrals. This problem arises when the seller of an American option aims to control the shortfall risk by using a partial hedge. We quantify the shortfall risk through a robust loss functional motivated by an extension of classical expected util- ity theory due to Gilboa and Schmeidler. In a general semimartingale model we prove the existence of an optimal strategy. Under additional compactness assumptions we show how the robust problem can be reduced to a non-robust optimization problem with respect to a worst-case probability measure. In the second part, we study the notions of the upper and the lower Snell envelope associated to an American option. We construct the envelopes for stable families of equivalent probability measures, the family of local martin- gale measures being an important special case. -
MARKOV DECISION PROCESSES Abstract: the Theory of Markov
July 06, 2010, 15:00 Draft, don’t distribute MARKOV DECISION PROCESSES NICOLE BAUERLE¨ ∗ AND ULRICH RIEDER‡ Abstract: The theory of Markov Decision Processes is the theory of controlled Markov chains. Its origins can be traced back to R. Bellman and L. Shapley in the 1950’s. During the decades of the last century this theory has grown dramatically. It has found applications in various areas like e.g. computer science, engineering, operations research, biology and economics. In this article we give a short intro- duction to parts of this theory. We treat Markov Decision Processes with finite and infinite time horizon where we will restrict the presentation to the so-called (gen- eralized) negative case. Solution algorithms like Howard’s policy improvement and linear programming are also explained. Various examples show the application of the theory. We treat stochastic linear-quadratic control problems, bandit problems and dividend pay-out problems. AMS 2010 Classification: 90C40, 60J05, 93E20 Keywords and Phrases: Markov Decision Process, Markov Chain, Bell- man Equation, Policy Improvement, Linear Programming 1. Introduction Do you want to play a card game? Yes? Then I will tell you how it works. We have a well-shuffled standard 32-card deck which is also known as a piquet deck. 16 cards are red and 16 cards are black. Initially the card deck lies on the table face down. Then I start to remove the cards and you are able to see its faces. Once you have to say ”stop”. If the next card is black you win 10 Euro, if it is red you loose 10 Euro. -
On Some Recent Aspects of Stochastic Control and Their Applications 507
Probability Surveys Vol. 2 (2005) 506–549 ISSN: 1549-5787 DOI: 10.1214/154957805100000195 On some recent aspects of stochastic control and their applications∗ Huyˆen Pham† Laboratoire de Probabilit´es et Mod`eles Al´eatoires, CNRS, UMR 7599 Universit´eParis 7, e-mail: [email protected], and CREST Abstract: This paper is a survey on some recent aspects and developments in stochastic control. We discuss the two main historical approaches, Bell- man’s optimality principle and Pontryagin’s maximum principle, and their modern exposition with viscosity solutions and backward stochastic differ- ential equations. Some original proofs are presented in a unifying context including degenerate singular control problems. We emphasize key results on characterization of optimal control for diffusion processes, with a view towards applications. Some examples in finance are detailed with their ex- plicit solutions. We also discuss numerical issues and open questions. AMS 2000 subject classifications: 93E20, 49J20, 49L20, 60H30. Keywords and phrases: Controlled diffusions, dynamic programming, maximum principle, viscosity solutions, backward stochastic differential equations, finance. Received December 2005. arXiv:math/0509711v3 [math.PR] 12 Jan 2006 ∗This is an original survey paper †I would like to thank Philip Protter for his encouragement to write this paper. 506 H. Pham/On some recent aspects of stochastic control and their applications 507 Contents 1 Introduction 508 2 Theproblemanddiscussiononmethodology 508 2.1 Problemformulation . 508 2.2 Bellman’s optimality principle . 509 2.2.1 The Hamilton-Jacobi-Bellman (in)equation . 511 2.2.2 Theclassicalverificationapproach . 512 2.3 Pontryagin’s maximum principle . 514 2.4 Othercontrolproblems . 515 2.4.1 Randomhorizon . -
Stochastic Control
1 Stochastic Control Roger Brockett April 3, 2009 2 PREFACE Teaching stochastic processes to students whose primary interests are in applications has long been a problem. On one hand, the subject can quickly become highly technical and if mathematical concerns are allowed to dominate there may be no time available for exploring the many interesting areas of applications. On the other hand, the treatment of stochastic calculus in a cavalier fashion leaves the student with a feeling of great uncertainty when it comes to exploring new material. Moreover, the problem has become more acute as the power of the differential equation point of view has become more widely appreciated. In these notes, an attempt is made to resolve this dilemma with the needs of those interested in building models and designing algorithms for estimation and control in mind. The approach is to start with Poisson counters and to identify the Wiener process with a certain limiting form. We do not attempt to define the Wiener process per se. Instead, everything is done in terms of limits of jump processes. The Poisson counter and differential equations whose right-hand sides include the differential of Poisson counters are developed first. This leads to the construction of a sample path representations of a continuous time jump process using Poisson counters. This point of view leads to an efficient problem solving technique and permits a unified treatment of time varying and nonlinear problems. More importantly, it provides sound intuition for stochastic differential equations and their uses without allowing the technicalities to dominate. In treating estimation theory, the conditional density equa- tion is given a central role. -
Optimal Stopping, Smooth Pasting and the Dual Problem
Introduction Preliminaries Main Theorems Questions Applications Optimal Stopping, Smooth Pasting and the Dual Problem Saul Jacka and Dominic Norgilas, University of Warwick Imperial 21 February 2018 Saul Jacka and Dominic Norgilas, University of Warwick The Compensator of the Snell Envelope Introduction Preliminaries Main Theorems Questions Applications The general optimal stopping problem: Given a ltered probability space (Ω; (Ft ); F; P) and an adapted gains process G, nd def St = ess sup E[Gτ jFt ] optional τ≥t Recall, under very general conditions I S is the minimal supermartingale dominating G def I τt = inffs ≥ t : Ss = Gs g is optimal I for any t, S is a martingale on [t; τt ] I when Gt = g(Xt ) (1) for some (continuous-time) Markov Process X , St can be written as a function, v(Xt ). Saul Jacka and Dominic Norgilas, University of Warwick The Compensator of the Snell Envelope Introduction Preliminaries Main Theorems Questions Applications Remark 1.1 Condition Gt = g(Xt ) is less restrictive than might appear. With θ being the usual shift operator, can expand statespace of X by appending adapted functionals F with the property that Ft+s = f (Fs ; (θs ◦ Xu; 0 ≤ u ≤ t)): (2) The resulting process Y def= (X ; F ) is still Markovian. If X is strong Markov and F is right-cts then Y is strong Markov. e.g if X is a BM, Z t Z s 0 Yt = Xt ; Lt ; sup Xs ; exp − α(Xu)du g(Xs ) ds 0≤s≤t 0 0 is a Feller process on the ltration of X . -
Financial Mathematics
Financial Mathematics Rudiger¨ Kiesel February, 2005 0-0 1 Financial Mathematics Lecture 1 by Rudiger¨ Kiesel Department of Financial Mathematics, University of Ulm Department of Statistics, LSE RSITÄT V E U I L N M U · · S O C I D E N N A D R O U · C · D O O C D E c Rudiger¨ Kiesel N 2 Aims and Objectives • Review basic concepts of probability theory; • Discuss random variables, their distribution and the notion of independence; • Calculate functionals and transforms; • Review basic limit theorems. RSITÄT V E U I L N M U · · S O C I D E N N A D R O U · C · D O O C D E c Rudiger¨ Kiesel N 3 Probability theory To describe a random experiment we use sample space Ω, the set of all possible outcomes. Each point ω of Ω, or sample point, represents a possible random outcome of performing the random experiment. For a set A ⊆ Ω we want to know the probability IP (A). The class F of subsets of Ω whose probabilities IP (A) are defined (call such A events) should be be a σ-algebra , i.e. closed under countable, disjoint unions and complements, and contain the empty set ∅ and the whole space Ω. Examples. Flip coins, Roll two dice. RSITÄT V E U I L N M U · · S O C I D E N N A D R O U · C · D O O C D E c Rudiger¨ Kiesel N 4 Probability theory We want (i) IP (∅) = 0, IP (Ω) = 1, (ii) IP (A) ≥ 0 for all A, (iii) If A1,A2,..., are disjoint, S P IP ( i Ai) = i IP (Ai) countable additivity. -
The Harrison-Pliska Story (And a Little Bit More)
The Harrison-Pliska Story (and a little bit more) Stanley R Pliska Professor Emeritus Department of Finance University of Illinois at Chicago Fields Institute February 2010 Table of Contents 1. Some continuous time stock price models 2. Alternative justification of Black-Scholes formula 3. The preliminary security market model 4. Economic considerations 5. The general security market model 6. Computing the martingale measure 7. Pricing contingent claims (European options) 8. Return processes 9. Complete markets 10. Pricing American options Key References We Used • J.M. Harrison and D.M. Kreps, Martingales and arbitrage in multiperiod securities markets, J. Economic Theory 20 (1979), 381-408. • J. Jacod, Calcul Stochastique et Problèmes de Martingales, Lecture Notes in Mathematics 714, Springer, New York, 1979. • P.-A. Meyer, Un cours sur les integrales stochastiques, Seminaire de Probabilité X, Lecture Notes in Mathematics 511, Springer, New York, 1979, 91-108. 1. Some Continuous Time Stock Price Models 1a. Geometric Brownian motion dS t = µStdt + σStdW (and multi-stock extensions) Reasonably satisfactory, but… • Returns can be correlated at some frequencies • Volatility is random • Big price jumps like on October 19, 1987 1b. A point process model St = S 0 exp {bN t – µt}, where N = Poisson process with intensity λ > 0 b = positive scalar µ = positive scalar J. Cox and S. Ross, The valuation of options for alternative stochastic processes, J. Financial Economics 3 (1976), 145- 166. 1c. Some generalizations and variations • Diffusions ( µ and σ are time and state dependent) • Ito processes (µ and σ are stochastic) • General jump processes • Jump-diffusion processes • Fractional Brownian motion • Etc. -
A Complete Bibliography of Electronic Communications in Probability
A Complete Bibliography of Electronic Communications in Probability Nelson H. F. Beebe University of Utah Department of Mathematics, 110 LCB 155 S 1400 E RM 233 Salt Lake City, UT 84112-0090 USA Tel: +1 801 581 5254 FAX: +1 801 581 4148 E-mail: [email protected], [email protected], [email protected] (Internet) WWW URL: http://www.math.utah.edu/~beebe/ 20 May 2021 Version 1.13 Title word cross-reference (1 + 1) [CB10]. (d; α, β) [Zho10]. (r + ∆) [CB10]. 0 [Sch12, Wag16]. 1 [Duc19, HL15b, Jac14, Li14, Sch12, SK15, Sim00, Uch18]. 1=2 [KV15]. 1=4 [JPR19]. 2 [BDT11, GH18b, Har12, Li14, RSS18, Sab21, Swa01, VZ11, vdBN17]. 2D [DXZ11]. 2M − X [Bau02, HMO01, MY99]. 3 [AB14, PZ18, SK15]. [0;t] [MLV15]. α [DXZ11, Pat07]. α 2 [0; 1=2) [Sch12]. BES0(d) [Win20]. β [Ven13]. d [H¨ag02, Mal15, Van07, Zho20]. d = 2 [KO06]. d>1 [Sal15]. dl2 [Wan14]. ≥ ≥ 2 + @u @mu d 2 [BR07]. d 3 [ST20]. d (0; 1) [Win20]. f [DGG 13]. @t = κm @xm [OD12]. G [NY09, BCH+00, FGM11]. H [Woj12, WP14]. k [AV12, BKR06, Gao08, GRS03]. k(n) [dBJP13]. kα [Sch12]. L1 1 2 1 p [CV07, MR01]. L ([0; 1]) [FP11]. L [HN09]. l [MHC13]. L [CGR10]. L1 [EM14]. Λ [Fou13, Fou14, Lag07, Zho14]. Lu = uα [Kuz00]. m(n) [dBJP13]. Cn [Tko11]. R2 [Kri07]. Rd [MN09]. Z [Sch12]. Z2 [Gla15]. Zd d 2 3 d C1 [DP14, SBS15, BC12]. Zn [ST20]. R [HL15a]. R [Far98]. Z [BS96]. 1 2 [DCF06]. N × N × 2 [BF11]. p [Eva06, GL14, Man05]. pc <pu [NP12a]. -
Approximation of the Snell Envelope and American Options Prices in Dimension One
ESAIM: Probability and Statistics March 2002, Vol. 6, 1–19 URL: http://www.emath.fr/ps/ DOI: 10.1051/ps:2002001 APPROXIMATION OF THE SNELL ENVELOPE AND AMERICAN OPTIONS PRICES IN DIMENSION ONE Vlad Bally1 and Bruno Saussereau2 Abstract. We establish some error estimates for the approximation of an optimal stopping problem along the paths of the Black–Scholes model. This approximation is based on a tree method. Moreover, we give a global approximation result for the related obstacle problem. Mathematics Subject Classification. 49L20, 60G40, 65M15, 91B28. Received February 15, 2001. Revised November 13, 2001. Introduction The purpose of this article is to estimate the rate of convergence of the approximation scheme of an optimal stopping problem along Brownian paths, when the Brownian Motion is approximated by a random walk. F F P We consider a standard Brownian Motion (Bt)t≥0 defined on a filtered probability space (Ω, , ( t)t≥0, ) and we denote x ∈ R Xt = x + bt+ aBt, a,b,x ,a>0. Given a function h which is assumed to be (at least) Lipschitz-continuous, our aim is to compute x E x |F Yt =sup (h(τ,Xτ ) t) , τ∈Tt,T where Tt,T is the set of all the stopping times taking values in [t,T ]. This is the Snell Envelope of the process x (h(t, Xt ))t∈[0,T ]. Our results apply to standard American Options. Recall, for example, that an American Put on a stock is the right to sell one share at a specified price K (called the exercise price) at any instant until a given future date T (called the expiration date or date of maturity). -
Stochastic Bridges of Linear Systems Yongxin Chen and Tryphon Georgiou
1 Stochastic bridges of linear systems Yongxin Chen and Tryphon Georgiou Abstract We study a generalization of the Brownian bridge as a stochastic process that models the position and velocity of inertial particles between the two end-points of a time interval. The particles experience random acceleration and are assumed to have known states at the boundary. Thus, the movement of the particles can be modeled as an Ornstein-Uhlenbeck process conditioned on position and velocity measurements at the two end-points. It is shown that optimal stochastic control provides a stochastic differential equation (SDE) that generates such a bridge as a degenerate diffusion process. Generalizations to higher order linear diffusions are considered. I. INTRODUCTION The theoretical foundations on how molecular dynamics affect large scale properties of ensembles were layed down more than a hundred years ago. A most prominent place among mathematical models has been occupied by the Brownian motion which provides a basis for studying diffusion and noise [1], [2], [3], [4]. The Brownian motion is captured by the mathematical model of a Wiener process, herein denoted by w(t). It represents the random motion of particles suspended in a fluid where their inertia is negligible compared to viscous forces. Taking into account inertial effects under a “delta-correlated” stationary Gaussian force field η(t) (that is, white noise, loosely thought of as dw=dt [1, p. 46]) d2x(t) dx(t) m = −λ + η(t) dt2 dt represents the Langevin dynamics ; x represents position, m mass, t time, and λ viscous friction parameter. The corresponding SDE dx(t) 0 1 x(t) 0 = dt + dw(t); dv(t) 0 −λ/m v(t) 1=m where w is a Wiener process and v the velocity, is a degenerate diffusion in that the stochastic term does not affect all degrees of freedom. -
Numerical Methods for Mean Field Games and Mean Field Type Control
Numerical Methods for Mean Field Games and Mean Field Type Control Mathieu Lauri`ere Abstract. Mean Field Games (MFG) have been introduced to tackle games with a large number of competing players. Considering the limit when the number of players is infinite, Nash equilibria are studied by considering the interaction of a typical player with the population's distribution. The situa- tion in which the players cooperate corresponds to Mean Field Control (MFC) problems, which can also be viewed as optimal control problems driven by a McKean-Vlasov dynamics. These two types of problems have found a wide range of potential applications, for which numerical methods play a key role since most models do not have analytical solutions. In these notes, we review several aspects of numerical methods for MFG and MFC. We start by pre- senting some heuristics in a basic linear-quadratic setting. We then discuss numerical schemes for forward-backward systems of partial differential equa- tions (PDEs), optimization techniques for variational problems driven by a Kolmogorov-Fokker-Planck PDE, an approach based on a monotone operator viewpoint, and stochastic methods relying on machine learning tools. Contents 1. Introduction 2 1.1. Outline 3 1.2. Definition of the problems and notation 3 2. Warm-up: Linear-Quadratic problems 5 2.1. Problem definition and MFG solution 5 2.2. Time discretization 6 2.3. Picard (fixed point) iterations 6 arXiv:2106.06231v1 [math.OC] 11 Jun 2021 2.4. Fictitious play 9 2.5. Newton iterations 10 2.6. Mean Field Type Control and the Price of Anarchy 10 3.