A stochastic programming approach for multi-period portfolio optimization

This paper extends previous work on the use of stochastic linear programming to solve life-cycle investment problems. We combine the feature of asset return predictability with practically relevant constraints arising in a life-cycle investment context. The objective is to maximize the expected utility of consumption over the lifetime and of bequest at the time of death of the investor. Asset returns and state variables follow a first-order vector auto-regression and the associated uncertainty is described by discrete scenario trees. To deal with the long time intervals involved in life-cycle problems we consider a few short-term decisions (to exploit any short-term return predictability), and incorporate a closed-form solution for the long, subsequent steady-state period to account for end effects.     

A possibilistic mean-semivariance-entropy model for multi-period portfolio selection with transaction costs

This paper deals with a multi-period portfolio selection problem with fuzzy returns. A possibilistic mean-semivariance-entropy model for multi-period portfolio selection is presented by taking into account four criteria viz., return, risk, transaction cost and diversification degree of portfolio. In the proposed model, the return level is quantified by the possibilistic mean value of return, the risk level is characterized by the lower possibilistic semivariance of return, and the diversification degree of portfolio is measured by the originally presented possibilistic entropy. Furthermore, a hybrid intelligent algorithm is designed to obtain the optimal portfolio strategy. Finally, the comparison analysis between the possibilistic entropy model and the proportion entropy model is provided by two numerical examples to illustrate the efficiency of the proposed approaches and the designed algorithm.     

An analytic solution for multi-period uncertain portfolio selection problem

Fuzzy Optimization and Decision Making - The return rates of risky assets in financial markets are usually assumed as random variables or fuzzy variables. For the ever-changing real asset market,...     

A class of multi-period semi-variance portfolio selection with a four-factor futures price model

Considering the stochastic exchange rate, a four-factor futures model with the underling asset, convenience yield, instantaneous risk free interest rate and exchange rate, is established. These processes follow jump-diffusion processes (Wiener process and Poisson process). The corresponding partial differential equation (PDE) of the futures price is derived. The general solution with parameters of the PDE is drawn. The weight least squares approach is applied to obtain the parameters of above PDE. Variance is substituted by semi-variance in Markovitz’s portfolio selection model. Therefore, a class of multi-period semi-variance model is formulated originally. A hybrid genetic algorithm (GA) with particle swarm optimizer (PSO) is proposed to solve the multi-period semi-variance model. Finally, an example, which are fuel futures in Shanghai exchange market, is selected to demonstrate the effectiveness of above models and methods.     

Worst-case robust decisions for multi-period mean–variance portfolio optimization

In this paper, we extend the multi-period mean–variance optimization framework to worst-case design with multiple rival return and risk scenarios. Our approach involves a min–max algorithm and a multi-period mean–variance optimization framework for the stochastic aspects of the scenario tree. Multi-period portfolio optimization entails the construction of a scenario tree representing a discretised estimate of uncertainties and associated probabilities in future stages. The expected value of the portfolio return is maximized simultaneously with the minimization of its variance. There are two sources of further uncertainty that might require a strengthening of the robustness of the decision. The first is that some rival uncertainty scenarios may be too critical to consider in terms of probabilities. The second is that the return variance estimate is usually inaccurate and there are different rival estimates, or scenarios. In either case, the best decision has the additional property that, in terms of risk and return, performance is guaranteed in view of all the rival scenarios. The ex-ante performance of min–max models is tested using historical data and backtesting results are presented.     

A multi-period stochastic portfolio optimization model applied for an airline company in the EU ETS

This paper aims to set up and solve a multi-period stochastic portfolio optimization model from an airline company’s point of view, considering all the specific European Union Emissions Trading Scheme (EU ETS) regulatory, managerial and trading constraints (i.e. physical constraints). Our contribution to existing academic literature is multiple. As the first ever case, we apply this technique to the aviation sector, a newly included sector within the EU ETS. More than mainly incorporating physical and technical (‘engineering’) features and focusing on short-term planning issues, we particularly address financial features and focus on mid-term planning issues. Therefore, instead of using spot prices, we run Monte Carlo simulations of correlated geometric Brownian motions (GBM) for traded futures prices of various emission allowance types for different CO₂ delivery time periods. We thereby specifically refer to the existing exchange-traded emission allowance types EU Emission Allowance (EUA) and Certified Emission Reduction (CER). By implementing actually valid and real-world-oriented regulatory constraints for EU ETS, namely managerial and trading constraints, our model implies a real-life application. We also highlight the possibility of banking and borrowing of emission allowances between CO₂ compliance periods, which is a crucial regulatory feature of EU ETS.     

Composite time-consistent multi-period risk measure and its application in optimal portfolio selection

Through the composition of two real-valued functions, we propose a new class of multi-period risk measure which is time consistent. The new multi-period risk measure is monotonous and convex when the two real-valued functions satisfy monotonicity and convexity. Based on this generic framework, we construct a specific class of time-consistent multi-period risk measure by considering the lower partial moment between the realized wealth and the target wealth at individual periods. With the new multi-period risk measure as the objective function, we formulate a multi-period portfolio selection model by considering transaction costs at individual investment periods. Furthermore, this stochastic programming model is transformed into a deterministic programming problem using the scenario tree technology. Finally, we show through empirical tests and comparisons the rationality, practicality and efficiency of our new multi-period risk measure and the corresponding portfolio selection model.     

A multi-objective multi-period stochastic programming model for public debt management

While raising debt on behalf of the government, public debt managers need to consider several possibly conflicting objectives and have to find an appropriate combination for government debt taking into account the uncertainty with regard to the future state of the economy. In this paper, we explicitly consider the underlying uncertainties with a complex multi-period stochastic programming model that captures the trade-offs between the objectives. The model is designed to aid the decision makers in formulating the debt issuance strategy. We apply an interactive procedure that guides the issuer to identify good strategies and demonstrate this approach for the public debt management problem of Turkey.     

Optimal bank portfolio choice under fixed-rate deposit insurance

This paper analyzes the investment decisions of insured banks under fixed-rate deposit insurance. The model takes into account the charter value and allows banks to dynamically revise their asset portfolios. Trade-offs exist between preserving the charter and exploiting deposit insurance. The optimal bank portfolio problem is solved analytically for a constant charter value. In any audit period, banks maximize their risk exposure before some critical time and act cautiously thereafter. The corresponding deposit insurance is shown to be a put option that matures at this critical time rather than at the audit date.     

Default probabilities in a corporate bank portfolio: A logistic model approach

Analysis and management of credit risk has taken on an increased importance in recent years. New regulations force banks and other financial institutions to make a credible effort to chart and manage the risk associated with their client portfolio. Increased competition in the financial market has also improved the motivation of monitoring the risk/reward relationship on various clients. Modern risk measures such as Credit Risk Capital (CRC) and Risk Adjusted Return On Capital (RAROC) are now well established among banks. One problem in such risk frameworks is to find the expected loss (EL) of the bank portfolio. The EL is based on assumptions regarding the estimated default frequency (EDF) for each client or group of clients. Benchmark models for CRC calculations treat EDFs as exogenous and do not devote much attention to how they can be obtained. This article presents a method of estimating such rates for a retail bank portfolio. The analysis is based on a logistic regression model where financial variables as well as other firm characteristics affect the default probability.     

A model for portfolio management with mortgage-backed securities

We present a stochastic programming model for the management of large portfolios of mortgage-backed securities (abbreviated: MBS). It is a two-stage, multiperiod model, whereby portfolio decisions made here-and-now are influenced by uncertain information about the future. In particular, we consider uncertainty in both the prepayment activity of the MBSs in the portfolio, as well as uncertainty about the future reinvestment rates. A simulation procedure is used to generate interest rate paths and prepayment behavior, and the stochastic program can be extremely large. Solution of the resulting large-scale programs is particularly challenging. We show that with massively parallel computing technology, the proposed models are indeed solvable. Empirical results on a Connection Machine CM-2 are reported.The HERMES Laboratory was established by a grant from the Digital Equipment Corporation, and is funded in part by grants SES-91-00216 and CCR-881135 from the National Science Foundation and grant 91-0168 from the Air-Force Office of Scientific Research.     

Robust international portfolio management

We present an international portfolio optimization model where we take into account the two different sources of return of an international asset: the local returns denominated in the local currency, and the returns on the foreign exchange rates. The explicit consideration of the returns on exchange rates introduces non-linearities in the model, both in the objective function (return maximization) and in the triangulation requirement of the foreign exchange rates. The uncertainty associated with both types of returns is incorporated directly in the model by the use of robust optimization techniques. We show that, by using appropriate assumptions regarding the formulation of the uncertainty sets, the proposed model has a semidefinite programming formulation and can be solved efficiently. While robust optimization provides a guaranteed minimum return inside the uncertainty set considered, we also discuss an extension of our formulation with additional guarantees through trading in quanto options for the foreign assets and in equity options for the domestic assets.     

Optimal pension fund management under multi-period risk minimization

In this paper, a multi-period stochastic optimization model for solving a problem of optimal selection of a pension fund by a pension plan member is presented. In our model, members of the pension plan are given a possibility to switch periodically between J types of funds with different risk profiles and so actively manage their risk exposure and expected return. Minimization of a multi-period average value-at-risk deviation measure under expected return constraint leads to a large-scale linear program. A theoretical framework and a solution for the case of the pension system of Slovak Republic are presented.     

Pre-commitment vs. time-consistent strategies for the generalized multi-period portfolio optimization with stochastic cash flows

In this paper, we propose a multi-period portfolio optimization model with stochastic cash flows. Under the mean–variance preference, we derive the pre-commitment and time-consistent investment strategies by applying the embedding scheme and backward induction approach, respectively. We show that the time-consistent strategy is identical to the optimal open-loop strategy. Also, under the exponential utility preference, we develop the optimal strategy for multi-period investment, which is time-consistent. We show that the above two time-consistent strategies are equivalent in some cases. We compare the pre-commitment and time-consistent strategies under different situations with some numerical simulations. The results indicate that the time-consistent strategy is more stable and secure than pre-commitment strategy under the generalized mean–variance criterion.     

Integrated portfolio management with options

In this paper we examine the problem of managing portfolios consisting of both, stocks and options. For the simultaneous optimization of stock and option positions we base our analysis on the generally accepted mean–variance framework. First, we analyze the effects of options on the mean–variance efficient frontier if they are considered as separate investment alternatives. Due to the resulting asymmetric portfolio return distribution mean–variance analysis will be not sufficient to identify optimal optioned portfolios. Additional investor preferences which are expressed in terms of shortfall constraints allow a more detailed portfolio specification. Under a mean–variance and shortfall preference structure we then derive optioned portfolios with a maximum expected return. To circumvent the technical optimization problems arising from stochastic constraints we use an approximation of the return distribution and develop economically meaningful conditions under which the complex optimization problem can be transformed into a linear problem being comparably easy to solve. Empirical results based on both, empirical market data and Monte Carlo simulations, illustrate the portfolio optimization procedure with options.     

A dynamic stochastic programming model for international portfolio management

We develop a multi-stage stochastic programming model for international portfolio management in a dynamic setting. We model uncertainty in asset prices and exchange rates in terms of scenario trees that reflect the empirical distributions implied by market data. The model takes a holistic view of the problem. It considers portfolio rebalancing decisions over multiple periods in accordance with the contingencies of the scenario tree. The solution jointly determines capital allocations to international markets, the selection of assets within each market, and appropriate currency hedging levels. We investigate the performance of alternative hedging strategies through extensive numerical tests with real market data. We show that appropriate selection of currency forward contracts materially reduces risk in international portfolios. We further find that multi-stage models consistently outperform single-stage models. Our results demonstrate that the stochastic programming framework provides a flexible and effective decision support tool for international portfolio management.     

A multi-period machine assignment problem

In this paper, a multi-period assignment problem is studied that arises as part of a weekly planning problem at mail processing and distribution centers. These facilities contain a wide variety of automation equipment that is used to cancel, sort, and sequence the mail. The input to the problem is an equipment schedule that indicates the number of machines required for each job or operation during the day. This result is then post-processed by solving a multi-period assignment problem to determine the sequence of operations for each machine. Two criteria are used for this purpose. The first is to minimize the number of startups, and the second is to minimize the number of machines used per operation.The problem is modeled as a 0–1 integer program that can be solved in polynomial time when only the first criterion is considered. To find solutions in general, a two-stage heuristic is developed that always obtains the minimum number of startups, but not necessarily the minimum number of machines per operation. In a comparative study, high quality solutions were routinely provided by the heuristic in negligible time when compared to a commercial branch-and-bound code (Xpress). For most hard instances, the branch-and-bound code was not able to even find continuous solutions within acceptable time limits.     

A dynamic bank portfolio planning model with multiple scenarios,multiple goals and changing priorities

The paper discusses a practical application of a two-stage linear goal programming model to the management of the domestic and foreign currency denominated assets and liabilities of a large bank in Finland. The planning horizon includes three one-year planning periods. A number of alternative scenarios are used to describe uncertainty concerning future developments in these periods. The scenarios are related to general economic conditions as well as the state of the domestic and foreign financial markets. The bank is assumed to have multiple conflicting goals with different and changing priorities. The goals deal with expected profits, risk, liquidity, capital adequacy, growth, customer relationships and several other aspects of the bank's operations.     

A spatial optimisation model for multi-period landscape level fuel management to mitigate wildfire impacts

Elevated fuel loads are contributing to an increase in the occurrence of, and area burned by, severe wildfires in many regions across the globe. In an attempt to reverse this trend, fire and land management agencies are investing in extensive fuel management programs. However, the planning of fuel treatment activities poses complicated decision-making problems with spatial and temporal dimensions. Here, we present a mixed integer programming model for spatially explicit multi-period scheduling of fuel treatments. The model provides a flexible framework that allows for landscape heterogeneity and a range of ecological and operational considerations and constraints. The model’s functionality is demonstrated on a series of hypothetical test landscapes and a number of implementation issues are discussed.