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The recent development of the European debt sovereign crisis showed that sovereign debt is not “risk free.” The traditional index bond management used during the last two decades such as the market-capitalization weighting scheme has been severely called into question. In order to overcome these drawbacks, alternative weighting schemes have recently prompted attention, both from academic researchers and from market practitioners. One of the key developments was the introduction of passive funds using economic fundamental indicators.

In this chapter, the authors introduced models with economic drivers with an aim of investigating whether the fundamental approaches outperformed the other models on risk-adjusted returns and on other terms.

The authors did this by constructing five portfolios composed of the Eurozone sovereigns bonds. The models are the Market-Capitalization RP, GDP model RP, Ratings RP model, Fundamental-Ranking RP, and Fundamental-Weighted RP models. These models were created exclusively for this chapter. Both Fundamental models are using a range of 10 country fundamentals. A variation from other studies is that this dissertation applied the risk parity concept which is an allocation technique that aims to equalize risk across different assets. This concept has been applied by assuming the credit default swap as proxy for sovereign credit risk. The models were run using the Generalized Reduced Gradient (GRG) method as the optimization model, together with the Lagrange Multipliers as techniques and the Karush–Kuhn–Tucker conditions. This led to the comparison of all the models mentioned above in terms of performance, risk-adjusted returns, concentration, and weighted average ratings.

By analyzing the whole period between 2006 and 2014, it was found that both the fundamental models gave very appealing results in terms of risk-adjusted returns. The best results were returned by the Fundamental-Ranking RP model followed by the Fundamental-Weighting RP model. However, better results for the mixed performance and risk-adjusted returns were achieved on a yearly basis and when sub-dividing the whole period in three equal periods. Moreover, the authors concluded that over the long term, the fundamental bond indexing triumphed over the other approaches by offering superior return and risk characteristics. Thus, one can use the fundamental indexation as an alternative to other traditional models.

It is known that model averaging estimators are useful when there is uncertainty governing which covariates should enter the model. We argue that in applied research there is also uncertainty as to which method one should deploy, prompting model averaging over user-defined choices. Specifically, we propose, and detail, a nonparametric regression estimator averaged over choice of kernel, bandwidth selection mechanism and local-polynomial order. Simulations and an empirical application are provided to highlight the potential benefits of the method.

The time series of the federal funds rate has recently been extended back to 1928, now including several episodes during which interest rates remained near the lower bound of zero. This series is analyzed, using the method of indirect inference, by applying recent research on bounded time series to estimate a set of bounded parametric diffusion models. This combination uncouples the specification of the bounds from the law of motion. Although Louis Bachelier was the first to use arithmetic Brownian motion to model financial time series, he has often been criticized for this proposal, since the process can take on negative values. Most researchers favor processes such as geometric Brownian motion (GBM), which remains positive. Under this framework, Bachelier's proposal remains valid when specified with bounds and is shown to compare favorably when modeling the federal funds rate.

Purpose – This chapter provides a roadmap for future research and evaluation on violent extremist risk analysis.

Methodology/Approach – The authors synthesize the lessons learned from process evaluations of general violence risk assessment, bias research, survey designs, linguistic analyses, and spatial analyses, and apply them to the problem of violent extremist risk assessment and management.

Findings – The next generation of violent extremist risk assessment research will necessitate a focus upon process, barriers to effective implementation and taking the human element of decision-making into account. Furthermore, the development of putative risk factors for violent extremist attitudes and behaviors necessitates a movement toward more survey-based research designs. Future risk assessment processes may additionally take language and spatial components into account for a more holistic understanding.

Originality/Value – Based on existing literature, there is a paucity of research conducting process evaluations, survey designs, linguistic analyses, and spatial analyses in this area. The authors provide several roadmaps, assessments of respective strengths and weaknesses, and highlight some initial promising results.

This chapter proposes a fuzzy approach to forecasting using a financial data set. The methodology used is multiple objective linear programming (MOLP). Selecting an individual forecast based on a single objective may not make the best use of available information for a variety of reasons. Combined forecasts may provide a better fit with respect to a single objective than any individual forecast. We incorporate soft constraints and preemptive additive weights into a mathematical programming approach to improve our forecasting accuracy. We compare the results of our approach with the preemptive MOLP approach. A financial example is used to illustrate the efficacy of the proposed forecasting methodology.

This chapter develops a methodology to assist critical facility operators in designing physical protection systems to defend against a single adversary (thief, saboteur, terrorist, etc.) attack. The developed methodology utilizes a multicriteria decision-making approach that balances the competing goals of minimal security system cost and maximum system performance. The methodology utilizes a network-based approach to facility security system design and analysis, which locates physical protection (detection, delay, and response) elements throughout a facility. These elements enable the facility owner to prevent attacks through deterrence and to defeat the adversary if he or she chooses to attack. The developed approach results in the ability for the facility operator to assess relative facility and/or infrastructure safety, and make decisions regarding how to optimally allocate resources for physical protection elements to balance cost and performance. A hypothetical example is discussed which demonstrates the usefulness of the developed methodology.

Purpose – Accounting research has long shown the effect of subjectivity in performance evaluation. This study investigates one form of subjectivity in performance evaluation: flexibility in weighting performance measures examining decisions made by supervisors about weighting. Empirical studies show that the performance-measure weights are only partially consistent with the predictions of the agency theory and they are a still outstanding issue.

Methodology/approach – We develop an experiment to analyse supervisor decision-making, manipulating two factors: internal organisational interdependence and the level of managerial performance. We derive hypotheses along with both economic and behavioural approaches. The economic approach is based on agency theory predictions and the controllability principle while the behavioural approach is drawn on the organisational justice theory. We argue that in low interdependence contexts the supervisor's decision confirms the agency theory predictions, while in high interdependence conditions weighting decisions could be driven by behavioural considerations of fairness perceptions of the evaluation process and the level of managerial performance.

Findings – We find that in low interdependence contexts the supervisor's decision confirms the agency theory predictions, while in high interdependence contexts it does not. The results indicate that the supervisor's decision stems from the integration of economic and behavioural perspectives.

Research and social implications – The theoretical framework can be useful for interpreting the supervisor decision-making and the weighting process.

Originality – The economic and behavioural approaches allow us to understand flexibility in weighting performance measures suggesting that, in addition to economic considerations, a behavioural perspective may also be relevant in explaining subjective weighting.

The author presents new estimates of the probability weighting functions found in rank-dependent theories of choice under risk. These estimates are unusual in two senses. First, they are free of functional form assumptions about both utility and weighting functions, and they are entirely based on binary discrete choices and not on matching or valuation tasks, though they depend on assumptions concerning the nature of probabilistic choice under risk. Second, estimated weighting functions contradict widely held priors of an inverse-s shape with fixed point well in the interior of the (0,1) interval: Instead the author usually finds populations dominated by “optimists” who uniformly overweight best outcomes in risky options. The choice pairs used here mostly do not provoke similarity-based simplifications. In a third experiment, the author shows that the presence of choice pairs that provoke similarity-based computational shortcuts does indeed flatten estimated probability weighting functions.