金融数学代写-FM50
时间:2022-06-23
MSc in Financial Mathematics, FM50Stochastic Control for Optimal TradingRoxana Dumitrescu and Leandro Sa´nchez-BetancourtDepartment of MathematicsKing’s College London1 Part 1: Literature reviewA stochastic optimal control problem deals with uncertainties when making decisions to maximize orminimize an objective function. With a given objective function, decision makers need to determine astrategy, which is the stochastic control, to optimize the objective function in a random environment.The decision-making problem is the so-called stochastic control problem. One powerful tool to studythe stochastic control problems is the dynamic programming principle and the associated Hamilton-Jacobi-Bellman (HJB) equation. Optimal controls are obtained by solving the HJB equation. For anoverview of this approach, students are referred to [Pha09], [FS06, Tou12]. Stochastic control theoryhas applications to a wide range of areas, from engineering to financial mathematics and economics.For Part 1, students are expected to present an overview of possible applications of stochasticcontrol problems arising in financial mathematics. You should describe some examples of problemsto which these techniques can be applied and you should try to write down some HJB equations forthe problems you consider and the solutions if known. You should be proactive in researching theliterature, which involves published journal papers and books. Working papers should be used mostlyfor orientation, given that their content has not been peer reviewed. It is particularly important thatyou synthesize the information gathered from these sources and presents it as a flowing story that isconsistent both in terms of notation and mathematical and financial content.2 Part 2: An optimal trading problemIn this part, we consider an extension of the standard optimal trading problem. We refer the studentto Chapter 6 in [CJP15] for an introduction to the subject.Consider a completed filtered probability space (Ω,F ,F = (Ft)t∈T,P), with Ft the natural filtrationgenerated by the 2−dimensional Brownian motion W = (Wα, WS), with Wα, and WS independent,and T = [0, T ], where T > 0 is the trading horizon. We let the mid-price process of the traded assetfollowdSt = κs αt dt+ σs dWSt , (1)where κs, σs ∈ R+ and αt is the informed trader’s signal, which we assume followsdαt = −κα αt dt+ σα dWαt , (2)for κα, σα ∈ R+. The process (νt)t∈T is understood as the speed at which the trader trades in themarket. In particular, we have the understanding that when νt > 0 it means that the trader ispurchasing the security and when νt < 0 the trader is selling the security.We denote by A the set of admissible strategies for the informed trader defined asA :={ν = (νt){t∈T} | ν is F− progressively measurable, and E[∫ T0(νs)2ds]< ∞}(3)and we let ν ∈ A be a given trading strategy. Given ν, the inventory process of the informed trader,denoted by (Qνt )t∈T, satisfiesdQνt = νt dt, Qν0 = 0 . (4)1Similarly, we define the controlled cash process (Xνt )t∈T of the informed trader, which followsdXνt = −νt (St + κ νt) dt , Xν0 = 0 , (5)where κ is the temporary price impact parameter that captures the quality of the liquidity that thebroker offers to their clients.The performance criterion of the informed trader is given byHν(t, α, q, S, x) = Et,x,S,q[XνT +QνT ST − a (QνT )2 − ϕ∫ Tt(Qνs )2 ds], (6)with the value function given byH(t, α, q, S, x) = supν∈AHν(t, α, q, S, x) , (7)and the notation Et,x,S,q meansEt,x,S,q[ · ] = E[ · |Xνt = x, St = S, Qνt = q] . (8)Task 1. Find the explicit solution to the control problem described above, i.e., find the value functionH and the optimal control ν∗ in closed-form. What happens to the optimal trading strategy if theBrownian motions Wα and WS have a correlation ρ ̸= 0?To accomplish this task, you can follow the standard approach, which consists in (formally) provingthe dynamic programming principle satisfied by the value function H and deriving the associated HJBequation (see e.g. [CJP15, Pha09]). Then, compute the optimal control in feedback form and substituteit back in the HJB equation and derive the PDE satisfied by the value function.Using the PDE satisfied by the value function, one can propose the ansatz H(t, α, q, S, x) =x+ S q + h(t, α, q) and derive the PDE satisfied by h. Then, propose a linear-quadratic ansatz (in q)for the function h and deduce a system of ODEs, that you should solve next. Use the solution for Hto find a closed-form solution to the optimal trading strategy.Consider the following model parameters: S0 = 100, κs = 1, σs = 2, T = 1, κα = 10, σα = 5,κ = 1× 10−3, a = 1, b = 0, and ϕ = 0. Consider the discrete version of the model using time steps of∆ = T/1000 and implement the optimal trading strategy (ν∗).Task 2. Based on the 10,000 simulations using the above parameters, fill the following table andproduce histograms of the four random variables below:Expected value Standard deviationXν∗TQν∗TXν∗T +Qν∗T STTask 3. Study the optimal strategy and its sensitivity to the model parameters. What can be said aboutthe limiting behaviour of the optimal strategy ν∗ as a→∞?To accomplish this task, you can plot trajectories of (αt)0≤t≤T , (νt)0≤t≤T , (QIt )0≤t≤T , and (XIt )0≤t≤Tfor a few outcomes of chance and comment on what the optimal strategy does and why one observesthe plotted behaviour. To analyse the sensitivity of the optimal strategy with respect to model pa-rameters, one can consider different values of a and ϕ and comment on their influence on the optimalstrategy.Task 4. Consider now a benchmark strategy that trades following αt, i.e., νBt = αt. Why would thisbe a good benchmark? Produce histograms for P νT := XνT + QνT SνT − a (QνT )2 − ϕ∫ Tt(Qνs )2 ds understrategies νB and νI∗, and compare the means for the quantity P νT under both strategies. Explain yourresults.23 Part 3: Original contributionIn this part, you should develop your own ideas either on an extension of the model proposed in Part2, or on an independent control problem. Some possible ideas could be:• More sophisticated price process models, e.g., jump-diffusion models, transient price impact,permanent price impact, impacting the signal.• Use neural networks to find the optimal feedback control following Section 2.1 in [GPW21].Once you have managed to replicate the results obtained in Part 2 of the thesis, then use neuralnetworks to solve more general versions of the current problem, e.g., other utility functions,different impact functions, more sophisticated models for the asset price, etc.• Develop a reinforcement learning framework to solve a discrete-time version of the problem inPart 2 and implement it. In particular, discuss advantages of doing so; see [HXY21, CJSB22].• Study the case where there are two (or more) signals. Study the tradeoff between following ashort-term α-signal versus a longer term signal.• Consider ambiguity aversion within the framework.• Consider a model with two correlated assets, and an α-signal that enters both assets. Set up anoptimal trading problem of your design within this framework.References[CJP15] A´lvaro Cartea, Sebastian Jaimungal, and Jose´ Penalva. Algorithmic and high-frequencytrading. Cambridge University Press, 2015.[CJSB22] A´lvaro Cartea, Sebastian Jaimungal, and Leandro Sa´nchez-Betancourt. Deep reinforcementlearning for algorithmic trading. In Machine Learning in Financial Markets: A guide tocontemporary practices (to appear). Edited by C.-A. Lehalle and A. Capponi. CambridgeUniversity Press, 2022.[FS06] Wendell H Fleming and Halil Mete Soner. Controlled Markov processes and viscosity solu-tions, volume 25. Springer Science & Business Media, 2006.[GPW21] Maximilien Germain, Huyeˆn Pham, and Xavier Warin. Neural networks-based algorithmsfor stochastic control and pdes in finance. arXiv preprint arXiv:2101.08068, 2021.[HXY21] Ben Hambly, Renyuan Xu, and Huining Yang. Recent advances in reinforcement learningin finance. arXiv preprint arXiv:2112.04553, 2021.[Pha09] Huyeˆn Pham. Continuous-time stochastic control and optimization with financial applica-tions, volume 61. Springer Science & Business Media, 2009.[Tou12] Nizar Touzi. Optimal stochastic control, stochastic target problems, and backward SDE,volume 29. Springer Science & Business Media, 2012.3