Parametric Immunization in Bond Portfolio Management

In this paper, we evaluate the relative immunization performance of the multifactor parametric interest rate risk model based on the Nelson-Siegel-Svensson specification of the yield curve with that of standard benchmark investment strategies, using European Central Bank yield curve data in the period between January 3, 2005 and December 31, 2011. In addition, we examine the role of portfolio design in the success of immunization strategies, particularly the role of the maturity bond. Considering multiperiod tests, the goal is to assess, in a highly volatile interest rate period, whether the use of the multifactor parametric immunization model contributes to improve immunization performance when compared to traditional single-factor duration strategies and whether durationmatching portfolios constrained to include a bond maturing near the end of the holding period prove to be an appropriate immunization strategy. Empirical results show that: (i) immunization models (single- and multi-factor) remove most of the interest rate risk underlying a naïve or maturity strategy; (ii) duration-matching portfolios constrained to include the maturity bond and formed using a single-factor model outperform the traditional duration-matching portfolio set up using a ladder portfolio and provide appropriate protection against interest rate risk; (iii) the multifactor parametric model outperforms all the other non-duration and duration-matching strategies, behaving almost like a perfect immunization asset; (iv) these results are consistent to changes in the rebalancing frequency of bond portfolios

Community-based Immunization Strategies for Epidemic Control

Understanding the epidemic dynamics, and finding out efficient techniques to control it, is a challenging issue. A lot of research has been done on targeted immunization strategies, exploiting various global network topological properties. However, in practice, information about the global structure of the contact network may not be available. Therefore, immunization strategies that can deal with a limited knowledge of the network structure are required. In this paper, we propose targeted immunization strategies that require information only at the community level. Results of our investigations on the SIR epidemiological model, using a realistic synthetic benchmark with controlled community structure, show that the community structure plays an important role in the epidemic dynamics. An extensive comparative evaluation demonstrates that the proposed strategies are as efficient as the most influential global centrality based immunization strategies, despite the fact that they use a limited amount of information. Furthermore, they outperform alternative local strategies, which are agnostic about the network structure, and make decisions based on random walks.Comment: 6 pages, 7 figure

Efficient Immunization Strategies for Computer Networks and Populations

We present an effective immunization strategy for computer networks and populations with broad and, in particular, scale-free degree distributions. The proposed strategy, acquaintance immunization, calls for the immunization of random acquaintances of random nodes (individuals). The strategy requires no knowledge of the node degrees or any other global knowledge, as do targeted immunization strategies. We study analytically the critical threshold for complete immunization. We also study the strategy with respect to the susceptible-infected-removed epidemiological model. We show that the immunization threshold is dramatically reduced with the suggested strategy, for all studied cases.Comment: Revtex, 5 pages, 4 ps fig

Immunization for complex network based on the effective degree of vertex

The basic idea of many effective immunization strategies is first to rank the importance of vertices according to the degrees of vertices and then remove the vertices from highest importance to lowest until the network becomes disconnected. Here we define the effective degrees of vertex, i.e., the number of its connections linking to un-immunized nodes in current network during the immunization procedure, to rank the importance of vertex, and modify these strategies by using the effective degrees of vertices. Simulations on both the scale-free network models with various degree correlations and two real networks have revealed that the immunization strategies based on the effective degrees are often more effective than those based on the degrees in the initial network.Comment: 16 pages, 5 figure

Immunization strategies for epidemic processes in time-varying contact networks

Spreading processes represent a very efficient tool to investigate the structural properties of networks and the relative importance of their constituents, and have been widely used to this aim in static networks. Here we consider simple disease spreading processes on empirical time-varying networks of contacts between individuals, and compare the effect of several immunization strategies on these processes. An immunization strategy is defined as the choice of a set of nodes (individuals) who cannot catch nor transmit the disease. This choice is performed according to a certain ranking of the nodes of the contact network. We consider various ranking strategies, focusing in particular on the role of the training window during which the nodes' properties are measured in the time-varying network: longer training windows correspond to a larger amount of information collected and could be expected to result in better performances of the immunization strategies. We find instead an unexpected saturation in the efficiency of strategies based on nodes' characteristics when the length of the training window is increased, showing that a limited amount of information on the contact patterns is sufficient to design efficient immunization strategies. This finding is balanced by the large variations of the contact patterns, which strongly alter the importance of nodes from one period to the next and therefore significantly limit the efficiency of any strategy based on an importance ranking of nodes. We also observe that the efficiency of strategies that include an element of randomness and are based on temporally local information do not perform as well but are largely independent on the amount of information available