A heuristic framework for the bi-objective enhanced index tracking problem

The index tracking problem is the problem of determining a portfolio of assets whose performance replicates, as closely as possible, that of a financial market index chosen as benchmark. In the enhanced index tracking problem the portfolio is expected to outperform the benchmark with minimal additional risk. In this paper, we study the bi-objective enhanced index tracking problem where two competing objectives, i.e., the expected excess return of the portfolio over the benchmark and the tracking error, are taken into consideration. A bi-objective Mixed Integer Linear Programming formulation for the problem is proposed. Computational results on a set of benchmark instances are given, along with a detailed out-of-sample analysis of the performance of the optimal portfolios selected by the proposed model. Then, a heuristic procedure is designed to build an approximation of the set of Pareto optimal solutions. We test the proposed procedure on a reference set of Pareto optimal solutions. Computational results show that the procedure is significantly faster than the exact computation and provides an extremely accurate approximation

Approximating Markov Chains for Bootstrapping and Simulation

In this work we develop a bootstrap method based on the theory of Markov chains. The method moves from the two competing objectives that a researcher pursues when performing a bootstrap procedure: (i) to preserve the structural similarity -in statistical sense- between the original and the bootstrapped sample; (ii) to assure a diversification of the latter with respect to the former. The original sample is assumed to be driven by a Markov chain. The approach we follow is to implement an optimization problem to estimate the memory of a Markov chain (i.e. its order) and to identify its relevant states. The basic ingredients of the model are the transition probabilities, whose distance is measured through a suitably defined functional. We apply the method to the series of electricity prices in Spain. A comparison with the Variable Length Markov Chain bootstrap, which is a well established bootstrap method, shows the superiority of our proposal in reproducing the dependence among data

A Tabu Search Heuristic Procedure in Markov Chain Bootstrapping

Markov chain theory is proving to be a powerful approach to bootstrap nite states processes, especially where time dependence is non linear. In this work we extend such approach to bootstrap discrete time continuous-valued processes. To this purpose we solve a minimization problem to partition the state space of a continuous-valued process into a nite number of intervals or unions of intervals (i.e. its states) and identify the time lags which provide \memory" to the process. A distance is used as objective function to stimulate the clustering of the states having similar transition probabilities. The problem of the exploding number of alternative partitions in the solution space (which grows with the number of states and the order of the Markov chain) is addressed through a Tabu Search algorithm. The method is applied to bootstrap the series of the German and Spanish electricity prices. The analysis of the results conrms the good consistency properties of the method we propose

Enhanced index tracking with CVaR-based ratio measures

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Clinical impact of an inter-hospital transfer strategy in patients with ST-elevation myocardial infarction undergoing primary angioplasty: the Emilia-Romagna ST-segment elevation acute myocardial infarction network

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Kawasaki disease : an epidemiological study in central Italy

BACKGROUND: Kawasaki disease (KD) is a systemic vasculitis with an acute and self-limited course. The incidence of KD differs widely among ethnic groups and is higher in the Asian population. In Italy, no recent data are available. Our purpose is to define the epidemiology of Kawasaki disease in the years 2008-2013 in children aged\u2009<\u200914 years in the Italian regions of Tuscany and Emilia Romagna through administrative data. METHODS: We studied the epidemiology of KD in the years 2008-2013 in children 0-14 years old resident in Tuscany and in Emilia Romagna regions using hospital ICD-9 discharge codes with a thorough data cleaning for duplicates. RESULTS: The distribution of the KD patients across ages was similar for the two regions with a peak in the second year of life. When considering data of the two regions together, the rate of incidence was 17.6 for 100,000 children under 5 years. For both Regions the incidence rose slightly during the study period and had a seasonal distribution, with higher incidence in spring and winter. CONCLUSION: This is the first Italian study performed through the use of administrative data. Figures are in line but slightly higher than those published in other European countries

Strategic and operational decision-making in expanding supply chains for LNG as a fuel

The European Union aims for a 40% reduction in greenhouse gas emissions by 2030, compared to 1990 levels, and recognizes the opportunities of Liquefied Natural Gas (LNG) as an alternative fuel for transportation to reach this goal. The lack of a mature supply chain for LNG as a fuel results in a need to invest in new (satellite) terminals, bunker barges and tanker trucks. This network design problem can be defined as a Two-Echelon Capacitated Location Routing Problem with Split Deliveries (2E-CLRPSP). An important feature of this problem is that direct deliveries are allowed from terminals, which makes the problem much harder to solve than the existing location routing literature suggests. In this paper, we improve the performance of a hybrid exact algorithm and apply our algorithm to a real world network design problem related to the expansion of the European supply chain for LNG as a fuel. We show that satellite terminals and bunker barges become an interesting option when demand for LNG grows and occurs further away from the import terminal. In those situations, the large investments associated with LNG satellites and bunker barges are offset by reductions in operational costs of the LNG tanker trucks

The incorporation of fixed cost and multilevel capacities into the discrete and continuous single source capacitated facility location problem

In this study we investigate the single source location problem with the presence of several possible capacities and the opening (fixed) cost of a facility that is depended on the capacity used and the area where the facility is located. Mathematical models of the problem for both the discrete and the continuous cases using the Rectilinear and Euclidean distances are produced. Our aim is to find the optimal number of open facilities, their corresponding locations, and their respective capacities alongside the assignment of the customers to the open facilities in order to minimise the total fixed and transportation costs. For relatively large problems, two solution methods are proposed namely an iterative matheuristic approach and VNS-based matheuristic technique. Dataset from the literature is adapted to assess our proposed methods. To assess the performance of the proposed solution methods, the exact method is first applied to small size instances where optimal solutions can be identified or lower and upper bounds can be recorded. Results obtained by the proposed solution methods are also reported for the larger instances

Portfolio Optimization: Scenario Generation, Models and Algorithms

Finally, we study the index tracking and the enhanced index tracking problems. We present two mixed-integer linear programming formulations. We introduce a heuristic framework, called Enhanced Kernel Search, to solve the index tracking problem. We show its effectiveness comparing the performances of several heuristics with those of a general-purpose solver using benchmark instances.Thirdly, we study portfolio optimization in a rebalancing framework, considering transaction costs and evaluating how much they affect a re-investment strategy. Specifically, we modify the single-period portfolio optimization model with transaction costs, based on the CVaR as performance measure, to introduce portfolio rebalancing. We suggest a procedure to use the proposed optimization model in a rebalancing framework. Extensive computational results are presented.Secondly, we analyze portfolio optimization when data uncertainty is taken into consideration. In deterministic mathematical optimization, it is assumed that all the input data are equal to some nominal values. Nevertheless, the solution can be sub-optimal or even infeasible when some of the data take values different from the nominal ones. Several techniques that are immune to data uncertainty, called robust, are known. We investigate the effectiveness of two robust techniques when applied to a portfolio selection problem. The reference model assumes the CVaR as performance measure. We carried out extensive computational experiments under different market behaviors.Firstly, we consider the problem of generating scenarios. We survey different techniques to generate scenarios for the rates of return. We also compare these techniques by providing in-sample and out-of-sample analysis of the portfolios. As reference model we use the Conditional Value-at-Risk (CVaR) model with transaction costs. Extensive computational results are presented.In single-period portfolio optimization several facets of the problem may influence the goodness of the portfolios. In this thesis, we aim at investigating the impact of some of these facets on the performances of the portfolios