Are VIX futures prices predictable? An empirical investigation

This paper investigates whether volatility futures prices per se can be forecasted by studying the fast-growing VIX futures market. To this end, alternative model specifications are employed. Point and interval out-of-sample forecasts are constructed and evaluated under various statistical metrics. Next, the economic significance of the forecasts obtained is also assessed by performing trading strategies. Only weak evidence of statistically predictable patterns in the evolution of volatility futures prices is found. No trading strategy yields economically significant profits. Hence, the hypothesis that the VIX volatility futures market is informationally efficient cannot be rejected.     

Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction

This paper reports the results of the NN3 competition, which is a replication of the M3 competition with an extension of the competition towards neural network (NN) and computational intelligence (CI) methods, in order to assess what progress has been made in the 10 years since the M3 competition. Two masked subsets of the M3 monthly industry data, containing 111 and 11 empirical time series respectively, were chosen, controlling for multiple data conditions of time series length (short/long), data patterns (seasonal/non-seasonal) and forecasting horizons (short/medium/long). The relative forecasting accuracy was assessed using the metrics from the M3, together with later extensions of scaled measures, and non-parametric statistical tests. The NN3 competition attracted 59 submissions from NN, CI and statistics, making it the largest CI competition on time series data. Its main findings include: (a) only one NN outperformed the damped trend using the sMAPE, but more contenders outperformed the AutomatANN of the M3; (b) ensembles of CI approaches performed very well, better than combinations of statistical methods; (c) a novel, complex statistical method outperformed all statistical and CI benchmarks; and (d) for the most difficult subset of short and seasonal series, a methodology employing echo state neural networks outperformed all others. The NN3 results highlight the ability of NN to handle complex data, including short and seasonal time series, beyond prior expectations, and thus identify multiple avenues for future research.     

Group decision-making: Head-count versus intensity of preference

This paper puts forth a framework for reshaping the group decision-making process. The proposed framework extends from the usual one-issue-at-a-time decision-making to one that involves several related issues simultaneously. Weaknesses of the traditional majority voting mechanism are first identified, and then a different voting method that takes each individual voter's sentiment into account is discussed. Specifically, a decision-maker is asked to express his/her intensity of preference for the issues encountered. Three hierarchical structures—benefits, costs, and risks—are developed to evaluate the alternatives. Due to the nature of pairwise comparisons and synthesis, the proposed method is amenable to consensus building and has higher reliability and consistency. It can be used for candidate selection, e.g. governmental election, when a large population is involved. It is also effective for resource allocation and prioritization when a small group or business is concerned. We believe the proposed approach has potential for resolving deficiencies of the conventional voting mechanism, and can be applied to many real-world problems. Its implementation on the Internet is also discussed.     

Economic development and resource degradation: Conflicts and policies

The paper develops a model that shares common features with computable general equilibrium (CGE) models. It is used to address two questions. First, what are the future prospects of a green gross domestic product (GDP); should we be concerned with resource degradation or not; and, to what extent, and under which conditions? Secondly, which policies are more effective than others? Model closures are simulated using different specifications of exogenous variables. Further, alternative policies are treated: human capital, price incentives, property rights and poverty reduction. In the African context, we show that while the prospects of environment-friendly economic development, i.e. a rising green GDP, are weak in the medium-run, under certain structural conditions there is a range of effective policies that resolves the conflict between economic growth and resource degradation, thus contributing to a rising green GDP.     

Transportation cost changes with statewide school district consolidation

This article studies the relationship between school district size and bus transportation costs, and estimates the change in such costs when a statewide policy of consolidation is pursued. To explore this relationship, we develop a multiple-objective model and solution procedure that combines a geographic aggregation and bus routing heuristic to generate consolidation scenarios. The heuristic was developed to explicitly consider efficiency, effectiveness, and equity objectives, and can be applied in both urban and rural states. The scenarios will generate average statewide bus transportation costs. As applied to the State of Iowa, within the legislature's proposed range of consolidation of 500-1000 students, it was found that transportation operational and capital cost increases range from 0.6 to 10.6 percent and 0.7 to 7.7 percent, respectively.     

A substitution orbit model of competitive innovations

Successful innovation and diffusion of technology can be attributed to the identification of the orbit of emerging new technologies that complement or substitute for existing technologies. This dynamism resembles the co-evolution process in an ecosystem. In an ecosystem, in order to maintain sustainable development, the complex interplay between competition and cooperation, typically observed in predator-prey systems, create a sophisticated balance. Given that an ecosystem can be used as a masterpiece system, this sophisticated balance can provide suggestive ideas for identifying an optimal orbit of competitive innovations with complement or substitution dynamism.Prompted by such a sophisticated balance in an ecosystem, this paper analyzes the optimal orbit of competitive innovations and, on the basis of an application of Lotka-Volterra equations, it reviews substitution orbits of Japan's monochrome to color TV system, fixed telephones to cellular telephones, cellular telephones to mobile Internet access service, and analog to digital TV broadcasting. On the basis of substitution orbits analyses, it attempts to extract suggestions supportive to identifying an optimal policy option in a complex orbit leading to expected orbit.Key findings include policy options that are effective in controlling parameters for Lotka-Volterra equations leading to expected orbit.     

Democratization is the driving force for technological and economic change

The purpose of this paper is to analyze the relationship between democratization and technological innovation. In primis, the paper shows, through economic history, that democratization is an antecedent process (cause) to technological and economic change (effect). In particular, the primary finding is that democratization is a driving force for technological change: most free countries, measured with liberal, participatory, and constitutional democracy indices, have a higher level of technology than less free and more autocratic countries. In fact, “democracy richness” generates a higher rate of technological innovation with fruitful effects for the wellbeing and wealth of nations. These findings and predictions lead to the conclusion that policy makers need to be cognizant of positive associations between democratization and technological innovation paths in order to support the modern economic growth and future technological progress of countries.     

Anticipating converging industries using publicly available data

Industry convergence, described as the blurring of boundaries between industries, plays an increasingly pivotal role in shaping markets and industries. Traditionally, this phenomenon has been discussed in respect to telecommunications, information technologies and electronics, but more recently also the chemical and its related industries find themselves affected by a larger convergence process. With the primary example of phytosterols in the two converging industries of Cosmeceuticals and of Nutraceuticals and Functional Foods, we analyze 7455 scientific and patent references in respect to first indicators for signs of convergence. Furthermore, we present and discuss a multiple indicator concept for monitoring convergence in an R&D-intensive field on the basis of publicly available data.