连续时间下的最佳投资组合和弹性

考虑了随机过程框架下的最优投资组合问题 ,发现弹性是投资组合的决策变量 .求解最优投资组合问题可以分为两个阶段 :在第一阶段 ,求解最优弹性使得 (期望 )效用最大 ;在第二阶段 ,寻找投资组合 ,使得投资组合的弹性等于最优弹性 .结果具有一般性 ,有广泛应用 ,例如 ,可用于含有期权的投资组合中去     

The maximum principles for partially observed risk-sensitive optimal controls of Markov regime-switching jump-diffusion system

ABSTRACT

This paper studies partially observed risk-sensitive optimal control problems with correlated noises between the system and the observation. It is assumed that the state process is governed by a continuous-time Markov regime-switching jump-diffusion process and the cost functional is of an exponential-of-integral type. By virtue of a classical spike variational approach, we obtain two general maximum principles for the aforementioned problems. Moreover, under certain convexity assumptions on both the control domain and the Hamiltonian, we give a sufficient condition for the optimality. For illustration, a linear-quadratic risk-sensitive control problem is proposed and solved using the main results. As a natural deduction, a fully observed risk-sensitive maximum principle is also obtained and applied to study a risk-sensitive portfolio optimization problem. Closed-form expressions for both the optimal portfolio and the corresponding optimal cost functional are obtained.     

Stochastic differential portfolio games for an insurer in a jump-diffusion risk process

We discuss an optimal portfolio selection problem of an insurer who faces model uncertainty in a jump-diffusion risk model using a game theoretic approach. In particular, the optimal portfolio selection problem is formulated as a two-person, zero-sum, stochastic differential game between the insurer and the market. There are two leader-follower games embedded in the game problem: (i) The insurer is the leader of the game and aims to select an optimal portfolio strategy by maximizing the expected utility of the terminal surplus in the “worst-case” scenario; (ii) The market acts as the leader of the game and aims to choose an optimal probability scenario to minimize the maximal expected utility of the terminal surplus. Using techniques of stochastic linear-quadratic control, we obtain closed-form solutions to the game problems in both the jump-diffusion risk process and its diffusion approximation for the case of an exponential utility.     

比例保本约束下的最优投资组合选择

本文研究了幂效用函数下带有比例保本约束的最优投资组合选择问题.利用拉格朗日乘子和投资组合复制方法,得到最优财富过程和最优投资组合,推广了带有限制的投资组合的相关结果.     

Optimal Control for Utility Portfolio Selection with Liability

In this paper we use stochastic optimal control theory to investigate a dynamic portfolio selection problem with liability process, in which the liability process is assumed to be a geometric Brownian motion and completely correlated with stock prices. We apply dynamic programming principle to obtain Hamilton-Jacobi-Bellman (HJB) equations for the value function and systematically study the optimal investment strategies for power utility, exponential utility and logarithm utility. Firstly, the explicit expressions of the optimal portfolios for power utility and exponential utility are obtained by applying variable change technique to solve corresponding HJB equations. Secondly, we apply Legendre transform and dual approach to derive the optimal portfolio for logarithm utility. Finally, numerical examples are given to illustrate the results obtained and analyze the effects of the market parameters on the optimal portfolios.     

Continuous-time mean-variance portfolios: a comparison

In this article, we present and compare three mean-variance optimal portfolio approaches in a continuous-time market setting. These methods are the L ²-projection as presented in Schweizer [M. Schweizer, Approximation of random variables by stochastic integrals, Ann. Prob. 22 (1995), pp. 1536–1575], the Lagrangian function approach of Korn and Trautmann [R. Korn and S. Trautmann, Continuous-time portfolio optimization under terminal wealth constraints, ZOR-Math. Methods Oper. Res. 42 (1995), pp. 69–92] and the direct deterministic approach of Lindberg [C. Lindberg, Portfolio optimization when expected stock returns are determined by exposure to risk, Bernoulli 15 (2009), pp. 464–474]. As the underlying model, we choose the recent innovative market parameterization introduced by Lindberg (2009) that has the particular aim to overcome the estimation problems of the stock price drift parameters. We derive some new results for the Lagrangian function approach, in particular explicit representations for the optimal portfolio process. Further, we compare the different optimization frameworks in detail and highlight their attractive and not so attractive features by numerical examples.     

Optimal portfolio selection for general provisioning and terminal wealth problems

In Dhaene et al. (2005), multiperiod portfolio selection problems are discussed, using an analytical approach to find optimal constant mix investment strategies in a provisioning or a savings context. In this paper we extend some of these results, investigating some specific, real-life situations. The problems that we consider in the first section of this paper are general in the sense that they allow for liabilities that can be both positive or negative, as opposed to Dhaene et al. (2005), where all liabilities have to be of the same sign. Secondly, we generalize portfolio selection problems to the case where a minimal return requirement is imposed. We derive an intuitive formula that can be used in provisioning and terminal wealth problems as a constraint on the admissible investment portfolios, in order to guarantee a minimal annualized return. We apply our results to optimal portfolio selection.     

Portfolio optimization with linear and fixed transaction costs

We consider the problem of portfolio selection, with transaction costs and constraints on exposure to risk. Linear transaction costs, bounds on the variance of the return, and bounds on different shortfall probabilities are efficiently handled by convex optimization methods. For such problems, the globally optimal portfolio can be computed very rapidly. Portfolio optimization problems with transaction costs that include a fixed fee, or discount breakpoints, cannot be directly solved by convex optimization. We describe a relaxation method which yields an easily computable upper bound via convex optimization. We also describe a heuristic method for finding a suboptimal portfolio, which is based on solving a small number of convex optimization problems (and hence can be done efficiently). Thus, we produce a suboptimal solution, and also an upper bound on the optimal solution. Numerical experiments suggest that for practical problems the gap between the two is small, even for large problems involving hundreds of assets. The same approach can be used for related problems, such as that of tracking an index with a portfolio consisting of a small number of assets.     

A utility maximization approach to hedging in incomplete markets

In this paper we introduce the notion of portfolio optimization by maximizing expected local utility. This concept is related to maximization of expected utility of consumption but, contrary to this common approach, the discounted financial gains are consumed immediately. In a general continuous-time market optimal portfolios are obtained by pointwise solution of equations involving the semimartingale characteristics of the underlying securities price process. The new concept is applied to hedging problems in frictionless, incomplete markets.     

Comparison and robustification of Bayes and Black-Litterman models

For determining an optimal portfolio allocation, parameters representing the underlying market—characterized by expected asset returns and the covariance matrix—are needed. Traditionally, these point estimates for the parameters are obtained from historical data samples, but as experts often have strong opinions about (some of) these values, approaches to combine sample information and experts’ views are sought for. The focus of this paper is on the two most popular of these frameworks—the Black-Litterman model and the Bayes approach. We will prove that—from the point of traditional portfolio optimization—the Black-Litterman is just a special case of the Bayes approach. In contrast to this, we will show that the extensions of both models to the robust portfolio framework yield two rather different robustified optimization problems.     

Optimal consumption and portfolio policies with the consumption habit constraints and the terminal wealth downside constraints

In this paper, we consider the optimal consumption and portfolio policies with the consumption habit constraints and the terminal wealth downside constraints, that is, here the consumption rate is greater than or equal to some nonnegative process, and the terminal wealth is no less than some positive constant. Using the martingale approach, we get the optimal consumption and portfolio policies.     

随机模糊的等比例-最小方差投资组合优化研究

Markowitz的均值-方差模型在投资组合优化中得到了广泛的运用和拓展,其中多数拓展模型仅局限于对随机投资组合或模糊投资组合的研究,而忽略了实际问题同时包含了随机信息和模糊信息两个方面。本文首先定义随机模糊变量的方差用以度量投资组合的风险,提出具有阀值约束的最小方差随机模糊投资组合模型,基于随机模糊理论,将该模型转化为具有线性等式和不等式约束的凸二次规划问题。为了提高上述模型的有效性,本文以投资者期望效用最大化为压缩目标对投资组合权重进行压缩,构建等比例-最小方差混合的随机模糊投资组合模型,并求解该模型的最优解。最后,运用滚动实际数据的方法,比较上述两个模型的夏普比率以验证其有效性。     

Dynamic mean-variance and optimal reinsurance problems under the no-bankruptcy constraint for an insurer

In this paper, we consider the optimal investment and optimal reinsurance problems for an insurer under the criterion of mean-variance with bankruptcy prohibition, i.e., the wealth process of the insurer is not allowed to be below zero at any time. The risk process is a diffusion model and the insurer can invest in a risk-free asset and multiple risky assets. In view of the standard martingale approach in tackling continuous-time portfolio choice models, we consider two subproblems. After solving the two subproblems respectively, we can obtain the solution to the mean-variance optimal problem. We also consider the optimal problem when bankruptcy is allowed. In this situation, we obtain the efficient strategy and efficient frontier using the stochastic linear-quadratic control theory. Then we compare the results in the two cases and give a numerical example to illustrate our results.     

Probability maximization models for portfolio selection under ambiguity

This paper considers several probability maximization models for multi-scenario portfolio selection problems in the case that future returns in possible scenarios are multi-dimensional random variables. In order to consider occurrence probabilities and decision makers’ predictions with respect to all scenarios, a portfolio selection problem setting a weight with flexibility to each scenario is proposed. Furthermore, by introducing aspiration levels to occurrence probabilities or future target profit and maximizing the minimum aspiration level, a robust portfolio selection problem is considered. Since these problems are formulated as stochastic programming problems due to the inclusion of random variables, they are transformed into deterministic equivalent problems introducing chance constraints based on the stochastic programming approach. Then, using a relation between the variance and absolute deviation of random variables, our proposed models are transformed into linear programming problems and efficient solution methods are developed to obtain the global optimal solution. Furthermore, a numerical example of a portfolio selection problem is provided to compare our proposed models with the basic model.     

An Asymptotic Expansion Scheme for Optimal Investment Problems

We shall propose a new computational scheme for the evaluation of the optimal portfolio for investment. Our method is based on an extension of the asymptotic expansion approach which has been recently developed for pricing problems of the contingent claims’ analysis by Kunitomo and Takahashi (1992, 1995, 2001, 2003), Yoshida (1992), Takahashi (1995, 1999), Takahashi and Yoshida (2001). In particular, we will explicitly derive a formula of the optimal portfolio associated with maximizing utility from terminal wealth in a financial market with Markovian coefficients, and give a numerical example for a power utility function. This revised version was published online in June 2006 with corrections to the Cover Date.     

Stability of Merton's portfolio optimization problem for Lévy models

Merton's classical portfolio optimization problem for an investor, who can trade in a risk-free bond and a stock, can be extended to the case where the driving noise of the logreturns is a pure jump process instead of a Brownian motion. Benth et al. [4,5] solved the problem and found the optimal control implicitly given by an integral equation in the hyperbolic absolute risk aversion (HARA) utility case. There are several ways to approximate a Levy process with infinite activity by neglecting the small jumps or approximating them with a Brownian motion, as discussed in Asmussen and Rosinski [1]. In this setting, we study stability of the corresponding optimal investment problems. The optimal controls are solutions of integral equations, for which we study convergence. We are able to characterize the rate of convergence in terms of the variance of the small jumps. Additionally, we prove convergence of the corresponding wealth processes and indirect utilities (value functions).     

Optimal control of impulsive switched systems with minimum subsystem durations

This paper presents a new computational approach for solving optimal control problems governed by impulsive switched systems. Such systems consist of multiple subsystems operating in succession, with possible instantaneous state jumps occurring when the system switches from one subsystem to another. The control variables are the subsystem durations and a set of system parameters influencing the state jumps. In contrast with most other papers on the control of impulsive switched systems, we do not require every potential subsystem to be active during the time horizon (it may be optimal to delete certain subsystems, especially when the optimal number of switches is unknown). However, any active subsystem must be active for a minimum non-negligible duration of time. This restriction leads to a disjoint feasible region for the subsystem durations. The problem of choosing the subsystem durations and the system parameters to minimize a given cost function is a non-standard optimal control problem that cannot be solved using conventional techniques. By combining a time-scaling transformation and an exact penalty method, we develop a computational algorithm for solving this problem. We then demonstrate the effectiveness of this algorithm by considering a numerical example on the optimization of shrimp harvesting operations.     

Project portfolio designing using data envelopment analysis and De Novo optimisation

In a rapidly evolving economic world, projects become tools to support organization goals. Project portfolio is set of all projects that are implemented in the organisation at a time. Possible projects are characterized by sets of inputs and outputs, where inputs are resources for project realisation and outputs measure multiple goals of the organisation. The data envelopment analysis (DEA) is an appropriate approach to select efficient projects. The organisation has its total resources in limited quantities. Designing a portfolio of efficient projects not exceeding the limited resources does not always lead to the most efficient portfolio. De Novo optimisation is an approach for designing optimal systems by reshaping the feasible set. The paper proposes a new approach for project portfolio designing based on a systemic combination of DEA model and De Novo optimisation approach. A total available budget is a restriction on project portfolio. The proposed concept provides designing of optimal project portfolio with the minimal budget. Performance measures of the designed project portfolio are the efficiency of the portfolio and the effectiveness of outputs. Possible extensions of the concept are formulated and discussed.     

Nested Conditional Value-at-Risk portfolio selection: A model with temporal dependence driven by market-index volatility

In a multistage stochastic programming framework, we develop a new method for finding an approximated portfolio allocation solution to the nested Conditional Value-at-Risk model when asset log returns are stagewise dependent. We describe asset log returns through a single-factor model where the driving factor is the market-index log return modeled by a Generalized Autoregressive Conditional Heteroskedasticity process to take into account the serial dependence usually observed. To solve the nested Conditional Value-at-Risk model, we implement a backward induction scheme coupled with cubic spline interpolation that reduces the computational complexity of the optimal portfolio allocation and allows to treat problems otherwise unmanageable.     

Mean-variance models for portfolio selection with fuzzy random returns

This paper develops two novel types of mean-variance models for portfolio selection problems, in which the security returns are assumed to be characterized by fuzzy random variables with known possibility and probability distributions. In the proposed models, we take the expected return of a portfolio as the investment return and the variance of the expected return of a portfolio as the investment risk. We assume that the security returns are triangular fuzzy random variables. To solve the proposed portfolio problems, this paper first presents the variance formulas for triangular fuzzy random variables. Then this paper applies the variance formulas to the proposed models so that the original portfolio problems can be reduced to nonlinear programming ones. Due to the reduced programming problems include standard normal distribution in the objective functions, we cannot employ the conventional solution methods to solve them. To overcome this difficulty, this paper employs genetic algorithm (GA) to solve them, and verify the obtained optimal solutions via Kuhn-Tucker (K-T) conditions. Finally, two numerical examples are presented to demonstrate the effectiveness of the proposed models and methods.