Robust reward–risk ratio optimization with application in allocation of generation asset

In this article, we study reward–risk ratio models under partially known message of random variables, which is called robust (worst-case) performance ratio problem. Based on the positive homogenous and concave/convex measures of reward and risk, respectively, the new robust ratio model is reduced equivalently to convex optimization problems with a min–max optimization framework. Under some specially partial distribution situation, the convex optimization problem is converted into simple framework involving the expectation reward measure and conditional value-at-risk measure. Compared with the existing reward–risk portfolio research, the proposed ratio model has two characteristics. First, the addressed problem combines with two different aspects. One is to consider an incomplete information case in real-life uncertainty. The other is to focus on the performance ratio optimization problem, which can realize the best balance between the reward and risk. Second, the complicated optimization model is transferred into a simple convex optimization problem by the optimal dual theorem. This indeed improves the usability of models. The generation asset allocation in power systems is presented to validate the new models.     

Can asset allocation limits determine portfolio risk–return profiles in DC pension schemes?

In defined contribution (DC) pension schemes, the regulator usually imposes asset allocation constraints (minimum and maximum limits by asset class) in order to create funds with different risk–return profiles. In this article, we challenge this approach and show that such funds can exhibit erratic risk–return profiles that deviate significantly from the intended design. We propose to replace all minimum and maximum asset allocation constraints by a single risk metric (or measure) that controls risk directly. Thus, funds with different risk–return profiles can be immediately created by adjusting the risk tolerance parameter accordingly. Using data from the Chilean DC pension system, we show that our approach generates funds whose risk–return profiles are consistently ordered according to the intended design, and outperforms funds created by means of asset allocation limits.     

Tail risk measures and risk allocation for the class of multivariate normal mean–variance mixture distributions

The Conditional Tail Expectation (CTE), also known as the Expected Shortfall and Tail-VaR, has received much attention as a preferred risk measure in finance and insurance applications. A related risk management exercise is to allocate the amount of the CTE computed for the aggregate or portfolio risk into individual risk units, a procedure known as the CTE allocation. In this paper we derive analytic formulas of the CTE and its allocation for the class of multivariate normal mean–variance mixture (NMVM) distributions, which is known to be extremely flexible and contains many well-known special cases as its members. We also develop the closed-form expression of the conditional tail variance (CTV) for the NMVM class, an alternative risk measure proposed in the literature to supplement the CTE by capturing the tail variability of the underlying distribution. To illustrate our findings, we focus on the multivariate Generalized Hyperbolic Distribution (GHD) family which is a popular subclass of the NMVM in connection with Lévy processes and contains some common distributions for financial modelling. In addition, we also consider the multivariate slash distribution which is not a member of GHD family but still belongs to the NMVM class. Our result is an extension of the recent contribution of Ignatieva and Landsman (2015).     

Mean–variance asset–liability management with asset correlation risk and insurance liabilities

Consider an insurer who invests in the financial market where correlations among risky asset returns are randomly changing over time. The insurer who faces the risk of paying stochastic insurance claims needs to manage her asset and liability by taking into account of the correlation risk. This paper investigates the impact of correlation risk to the optimal asset–liability management (ALM) of an insurer. We employ the Wishart process to model the stochastic covariance matrix of risky asset returns. The insurer aims to minimize the variance of the terminal wealth given an expected terminal wealth subject to the risk of paying out random liabilities of compound Poisson process. This ALM problem then becomes a linear–quadratic stochastic optimal control problem with stochastic volatilities, stochastic correlations and jumps. The recognition of an affine form in the solution process enables us to derive the explicit closed-form solution to the optimal ALM portfolio policy, obtain the efficient frontier, and identify the condition that the solution is well behaved.     

Multi‐asset portfolio optimization with transaction cost

The inclusion of transaction costs in the optimal portfolio selection and consumption rule problem is accomplished via the use of perturbation analyses. The portfolio under consideration consists of more than one risky asset, which makes numerical methods impractical. The objective is to establish both the transaction and the no‐transaction regions that characterize the optimal investment strategy. The optimal transaction boundaries for two and three risky assets portfolios are solved explicitly. A procedure for solving the N risky assets portfolio is described. The formulation used also reduces the restriction on the functional form of the utility preference.     

Multi-objective and prioritized berth allocation in container ports

This paper considers a berth allocation problem (BAP) which requires the determination of exact berthing times and positions of incoming ships in a container port. The problem is solved by optimizing the berth schedule so as to minimize concurrently the three objectives of makespan, waiting time, and degree of deviation from a predetermined priority schedule. These objectives represent the interests of both port and ship operators. Unlike most existing approaches in the literature which are single-objective-based, a multi-objective evolutionary algorithm (MOEA) that incorporates the concept of Pareto optimality is proposed for solving the multi-objective BAP. The MOEA is equipped with three primary features which are specifically designed to target the optimization of the three objectives. The features include a local search heuristic, a hybrid solution decoding scheme, and an optimal berth insertion procedure. The effects that each of these features has on the quality of berth schedules are studied.     

Multi-period portfolio optimization for asset–liability management with bankrupt control

In this paper, we investigate a multi-period portfolio optimization problem for asset–liability management of an investor who intends to control the probability of bankruptcy before reaching the end of an investment horizon. We formulate the problem as a generalized mean–variance model that incorporates bankrupt control over intermediate periods. Based on the Lagrangian multiplier method, the embedding technique, the dynamic programming approach and the Lagrangian duality theory, we propose a method to solve the model. A numerical example is given to demonstrate our method and show the impact of bankrupt control and market parameters on the optimal portfolio strategy.     

Dynamic asset allocation under VaR constraint with?stochastic interest rates

This paper addresses the problem of dynamic asset allocation under a bounded shortfall risk in a market composed of three assets: cash, stocks and a zero coupon bond. The dynamics of the instantaneous short rates is driven by a Hull and White model. In this setting, we determine and compare optimal investment strategies maximizing the CRRA utility of terminal wealth with and without value at risk constraint.     

Storage allocation under processor sharing I: exact solutions and asymptotics

We consider a processor-sharing storage allocation model, which has m primary holding spaces and infinitely many secondary ones, and a single processor servicing the stored items (customers). An arriving customer takes a primary space, if one is available. We define the traffic intensity ρ to be λ/μ where λ is the customers’ arrival rate and μ is the service rate of the processor. We study the joint probability distribution of the numbers of occupied primary and secondary spaces. For 0<ρ<1, we obtain the exact solutions for m=1 and m=2. For arbitrary m we study the problem in the asymptotic limit ρ↑1 with m fixed. We also give the tail of the distribution for a fixed ρ<1 and any m.     

FirmNet: the scope of firms and the allocation of task in a knowledge-based economy

The increasing knowledge intensity of jobs, typical of a knowledge economy, highlights the role of firms as integrators of know how and skills. As economic activity becomes mainly intellectual and requires the integration of specific and idiosyncratic skills, firms need to allocate skills to tasks and traditional hierarchical control may result increasingly ineffective. In this work, we explore under what circumstances networks of agents, which bear specific skills, may self-organize in order to complete tasks. We use a computer simulation approach and investigate how local interaction of agents, endowed with skills and individual decision-making rules, may produce aggregate network structure able to perform tasks. To design algorithms that mimic individual decision-making, we borrow from computer science literature and, in particular, from studies addressing protocols that produce cooperation in P2P networks. We found that self-organization depends on imitation of successful peers, competition among agents holding specific skills, and the structural features of, formal or informal, organizational networks embedding both professionals, holding skills, and project managers, holding access to jobs.

Optimal reinsurance under VaR and TVaR risk measures in the presence of reinsurer’s risk limit

In most studies on optimal reinsurance, little attention has been paid to controlling the reinsurer’s risk. However, real-world insurance markets always place a limit on coverage, otherwise the insurer will be subjected to under a heavy financial burden when the insured suffers a large unexpected covered loss. In this paper, we revisit the optimal reinsurance problem under the optimality criteria of VaR and TVaR risk measures when the constraints for the reinsurer’s risk exposure are presented. Two types of constraints are considered that have been proposed by Cummins and Mahul (2004) and Zhou et al. (2010), respectively. It is shown that two-layer reinsurance is always the optimal reinsurance policy under both VaR and TVaR risk measures and under both types of constraints. This implies that the two-layer reinsurance policy is more robust. Furthermore, the optimal quantity of ceded risk depends on the confidence level, the safety loading and the tolerance level, as well as on the relation between them.     

Applying simulation optimization to the asset allocation of a property–casualty insurer

Proper asset allocations are vital for property–casualty insurers to be competitive and solvent. Theories of finance offer little practical guidance in constructing such asset allocations however. This research integrates simulation models with a newly developed evolutionary algorithm for the multi-period asset allocation problem of a property–casualty insurer. We first construct a simulation model to simulate operations of a property–casualty insurer. Then we develop multi-phase evolution strategies (MPES) to be used with the simulation model to search for promising asset allocations for the insurer. A thorough experiment is conducted to evaluate the performance of our simulation optimization approach. Computational results show that MPES is an effective search algorithm. It dominates the grid search method by a significant margin. The re-allocation strategy resulting from MPES outperforms re-balancing strategies significantly. This research further demonstrates that the simulation optimization approach can be used to study economic issues related to multi-period asset allocation problems in practical settings.     

Moment calculations for piecewise-defined functions: an application to stochastic optimization with coherent risk measures

This work introduces a new analytical approach to the formulation of optimization problems with piecewise-defined (PD) objective functions. First, we introduce a new definition of multivariate PD functions and derive formal results for their continuity and differentiability. Then, we obtain closed-form expressions for the calculation of their moments. We apply these findings to three classes of optimization problems involving coherent risk measures. The method enables one to obtain insights on problem structure and on sensitivity to imprecision at the problem formulation stage, eliminating reliance on ad-hoc post-optimality numerical calculations.     

Static and dynamic server allocation in systems with on/off sources

A system consisting of a number of servers, where demands of different types arrive in bursts (modelled by interrupted Poisson processes), is examined in the steady state. The problem is to decide how many servers to allocate to each job type, so as to minimize a cost function expressed in terms of average queue sizes. First, an exact analysis is provided for an isolated IPP/M/n queue. The results are used to compute the optimal static server allocation policy. The latter is then compared to four heuristic policies which employ dynamic switching of servers from one queue to another (such switches take time and hence incur costs). This work was carried out in the framework of the collaborative project DOPCHE (Dynamic Operative Policies for Commercial Hosting Environments), funded by the UK Engineering and Physical Sciences Research Council under its E-Science programme. The support of the Network of Excellence EuroNGI, funded by the EU, is also acknowledged.     

Robust Stability and Stabilization of a Class of Nonlinear Switched Discrete-Time Systems with Time-Varying Delays

In this paper, we investigate the problems of robust delay-dependent ℒ₂ gain analysis and feedback control synthesis for a class of nominally-linear switched discrete-time systems with time-varying delays, bounded nonlinearities and real convex bounded parametric uncertainties in all system matrices under arbitrary switching sequences. We develop new criteria for such class of switched systems based on the constructive use of an appropriate switched Lyapunov-Krasovskii functional coupled with Finsler’s Lemma and a free-weighting parameter matrix. We establish an LMI characterization of delay-dependent conditions under which the nonlinear switched delay system is robustly asymptotically stable with an ℒ₂-gain smaller than a prescribed constant level. Switched feedback schemes, based on state measurements, output measurements or by using dynamic output feedback, are designed to guarantee that the corresponding switched closed-loop system enjoys the delay-dependent asymptotic stability with an ℒ₂ gain smaller than a prescribed constant level. All the developed results are expressed in terms of convex optimization over LMIs and tested on representative examples.     

Dynamic sample budget allocation in model-based optimization

Model-based search methods are a class of optimization techniques that search the solution space by sampling from an underlying probability distribution “model,” which is updated iteratively after evaluating the performance of the samples at each iteration. This paper aims to improve the sampling efficiency of model-based methods by considering a generalization where a population of distribution models is maintained and subsequently propagated from generation to generation. A key issue in the proposed approach is how to efficiently allocate the sampling budget among the population of models to maximize the algorithm performance. We formulate this problem as a generalized max k-armed bandit problem, and derive an efficient dynamic sample allocation scheme based on Markov decision theory to adaptively allocate computational resources. The proposed allocation scheme is then further used to update the current population to produce an improving population of models. Our preliminary numerical results indicate that the proposed procedure may considerably reduce the number of function evaluations needed to obtain high quality solutions, and thus further enhance the value of model-based methods for optimization problems that require expensive function evaluations for performance evaluation.     

Better than optimal mean–variance portfolio policy in multi-period asset–liability management problem

When the wealth is larger than some threshold in multi-period mean–variance asset–liability management, the pre-committed policy is no longer mean–variance efficient policy for the remaining investment horizon. To revise the policy, by relaxing self-financing constraint and allowing to withdraw some wealth, we derive a new dominating policy, which is better than the pre-committed policy. The revised policy can achieve the same mean–variance pairs attained by the pre-committed policy, and yields a nonnegative free cash flow stream over the investment horizon.     

A recursive algorithm for multivariate risk measures and a set-valued Bellman’s principle

A method for calculating multi-portfolio time consistent multivariate risk measures in discrete time is presented. Market models for d assets with transaction costs or illiquidity and possible trading constraints are considered on a finite probability space. The set of capital requirements at each time and state is calculated recursively backwards in time along the event tree. We motivate why the proposed procedure can be seen as a set-valued Bellman’s principle, that might be of independent interest within the growing field of set optimization. We give conditions under which the backwards calculation of the sets reduces to solving a sequence of linear, respectively convex vector optimization problems. Numerical examples are given and include superhedging under illiquidity, the set-valued entropic risk measure, and the multi-portfolio time consistent version of the relaxed worst case risk measure and of the set-valued average value at risk.     

Are quantile risk measures suitable for risk-transfer decisions?

Although controversial from the theoretical point of view, quantile risk measures are widely used by institutions and regulators.In this paper, we use a unified approach to find the optimal treaties for an agent who seeks to minimize one of these measures, assuming premium calculation principles of various types.We show that the use of measures like Value at Risk or Conditional Tail Expectation as optimization criteria for insurance or reinsurance leads to treaties that are not enforceable and/or are clearly bad for the cedent. We argue that this is one further argument against the use of quantile risk measures, at least for the purpose of risk-transfer decisions.     

Hardness measures for gridworld benchmarks and performance analysis of real-time heuristic search algorithms

Gridworlds are one of the most popular settings used in benchmark problems for real-time heuristic search algorithms. However, no comprehensive studies have been published so far on how the difference in the density of randomly positioned obstacles affects the hardness of the problems. This paper presents two measures for characterizing the hardness of gridworld problems parameterized by obstacle ratio, and relates them to the performance of the algorithms. We empirically show that the peak locations of those measures and actual performance degradation of the basic algorithms (RTA* and LRTA*) almost coincide with each other for a wide variety of problem settings. Thus the measures uncover some interesting aspects of the gridworlds.