Portfolio optimization in stochastic markets

We consider a multiperiod mean-variance model where the model parameters change according to a stochastic market. The mean vector and covariance matrix of the random returns of risky assets all depend on the state of the market during any period where the market process is assumed to follow a Markov chain. Dynamic programming is used to solve an auxiliary problem which, in turn, gives the efficient frontier of the mean-variance formulation. An explicit expression is obtained for the efficient frontier and an illustrative example is given to demonstrate the application of the procedure.     

Mean-Variance Hedging Under Multiple Defaults Risk

We solve a mean-variance hedging problem in an incomplete market where multiple defaults can occur. For this purpose, we use a default-density modeling approach. The global market information is formulated as a progressive enlargement of a default-free Brownian filtration, and the dependence of the default times is modelled using a conditional density hypothesis. We prove the quadratic form of each value process between consecutive default times and recursively solve systems of coupled quadratic backward stochastic differential equations (BSDEs). We demonstrate the existence of these solutions using BSDE techniques. Then, using a verification theorem, we prove that the solutions of each subcontrol problem are related to the solution of our global mean-variance hedging problem. As a byproduct, we obtain an explicit formula for the optimal trading strategy. Finally, we illustrate our results for certain specific cases and for a multiple defaults case in particular.     

Remarks on maximal meanstandard devition ratio in undiscounted mdps

In the steady state of an undiscounted Markov decision process, we consider the problem to find an optimal stationary probability distribution that maximizes the mean standard deviation ratio among all the stationary probability distributions. The problem injects considerations in MDPs from the relative point of view     

Quadratic Hedging Methods for Defaultable Claims

We apply the local risk-minimization approach to defaultable claims and we compare it with intensity-based evaluation formulas and the mean-variance hedging. We solve analytically the problem of finding respectively the hedging strategy and the associated portfolio for the three methods in the case of a default put option with random recovery at maturity.     

Constrained LQ Problem with a Random Jump and Application to Portfolio Selection

This paper deals with a constrained stochastic linear-quadratic(LQ for short)optimal control problem where the control is constrained in a closed cone. The state process is governed by a controlled SDE with random coefficients. Moreover, there is a random jump of the state process. In mathematical finance, the random jump often represents the default of a counter party. Thanks to the It-Tanaka formula, optimal control and optimal value can be obtained by solutions of a system of backward stochastic differential equations(BSDEs for short). The solvability of the BSDEs is obtained by solving a recursive system of BSDEs driven by the Brownian motions. The author also applies the result to the mean variance portfolio selection problem in which the stock price can be affected by the default of a counterparty.     

<Emphasis Type="Italic">L</Emphasis><Superscript>2</Superscript>-approximating Pricing under Restricted Information

We consider the mean-variance hedging problem under partial information in the case where the flow of observable events does not contain the full information on the underlying asset price process. We introduce a certain type martingale equation and characterize the optimal strategy in terms of the solution of this equation. We give relations between this equation and backward stochastic differential equations for the value process of the problem. This work was supported by Georgian National Science Foundation grant STO07/3-172.     

Multiperiod Mean-Variance Optimization with Intertemporal Restrictions

We investigate mean-variance optimization problems that arise in portfolio selection. Restrictions on intermediate expected values or variances of the portfolio are considered. Some explicit procedures for obtaining the solution are presented. The main advantage of this technique is that it is possible to control the intermediate behavior of a portfolio’s return or variance. Some examples illustrating these situations are presented. The first author received financial support from CNPq (Brazilian National Research Council) Grants 472920/03-0 and 304866/03-2, FAPESP (Research Council of the State of S?o Paulo) Grant 03/06736-7, PRONEX Grant 015/98, and IM-AGIMB.     

Use of stochastic and mathematical programming in?portfolio theory and practice

Standard finance portfolio theory draws graphs and writes equations usually with no constraints and frequently in the univariate case. However, in reality, there are multivariate random variables and multivariate asset weights to determine with constraints. Also there are the effects of transaction costs on asset prices in the theory and calculation of optimal portfolios in the static and dynamic cases. There we use various stochastic programming, linear complementary, quadratic programming and nonlinear programming problems. This paper begins with the simplest problems and builds the theory to the more complex cases and then applies it to real financial asset allocation problems, hedge funds and professional racetrack betting. This paper is based on a keynote lecture at the APMOD conference in Madrid in June 2006. It was also presented at the London Business School. Many thanks are due to APMOD organizers Antonio Alonso-Ayuso, Laureano Escudero, and Andres Ramos for inviting me and for excellent hospitality in Madrid. Thanks are also due to my teachers at Berkeley who got me on the right track on stochastic and mathematical programming, especially Olvi Mangasarian, Roger Wets and Willard Zangwill, and my colleagues and co-authors on portfolio theory in finance and horseracing, especially Chanaka Edirishinge, Donald Hausch, Jarl Kallberg, Victor Lo, Leonard MacLean, Raymond Vickson and Yonggan Zhao.     

均值-方差准则及其应用

在位置-尺度分布族中研究了均值-方差准则与期望效用理论的一致性,特别指出当均值相等时或源的支撑可达到负无穷时,均值-方差准则与期望效用理论是完全一致的,这表明可以用均值-方差准则研究满足条件的经济问题、管理问题.还介绍了均值-方差准则在金融中的一些应用.     

Mean-variance Hedging for Pricing European-type Contingent Claims with Transaction Costs

In this paper,a European-type contingent claim pricing problem with transaction costs is considered by a mean-variance hedging argument.The investor has to pay transaction costs which areproportional to the amount of stock transacted.The writer‘‘s hedging object is to minimize the hedgingrisk,defined as the variance of hedging error at expiration,with a proper expected excess return level.At first, we consider the mean-variance hedging problem:for initial hedging wealth f,maximizing the excess expected return under the minimum hedging risk level V0.On the other hand,we consider a mean-variance portfolio problem,which is to maximize the expected return with initial wealth 0 under the same risk level V0.The minimum initial hedging wealth f,which can offset the difference of the maximum expected return of these two problems,is the writer‘s price.