Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.     

Easing the assessment of emerging technologies in technology observatories. Findings about patterns of dissemination of emerging technologies on the internet

This paper considers the Web as a big data container that can be used by Technology Observatories and administrations to track emerging issues and more specifically emerging technologies. It considers information that is available on the Internet for free from different sources, and proposes a framework that can be useful to characterise them and to detect patterns of dissemination. This framework is made up of 30 metrics obtained from different kinds of sources (general web, patents, scholars?…). Some of them are obtained directly as the number of hits retrieved by queries on a search engine, and other ones calculated by means of ratios. This paper contains the development of a complete case that utilises this framework to characterise emerging technologies included in the well-known Hype Cycle for Emerging Technologies, in this case the 2015 release¹ and to analyze patterns of dissemination of these technologies on the Internet.     

Towards an operations strategy model of servitization: the role of sustainability pressure

Servitization requires an important strategic shift to drive changes in the operations of manufacturing firms. Using a large-scale survey, the purpose of this paper is (1) to build and validate an operations strategy model of servitization confirming previous case study findings on servitization as a strategic action and (2) to explore the role of sustainability pressures in, and the sustainability performance effects of, pursuing service-based operations strategies. To reach these objectives, a dataset including the responses of 735 manufacturing plants from 21 different countries is analyzed using the PLS-SEM method. The results indicate that the sustainability pressures of stakeholders can push manufacturers to adopt a service-based operations strategy, materializing in the provision of both basic (product-oriented) and advanced (customer-oriented) services (BAS and ADS). Our analysis further indicates that while offering BAS is a precondition for ADS provision, only ADS can offer a competitive edge for manufacturers, both in terms of service and sustainability-related operational performance.     

Bivariate and multivariate scaled association models. An application to homogamy of social origin and education in Hungary between 1930 and 1979

In this article we compare bivariate and multivariate models for homogamy of social origin and education to test whether bivariate models of homogamy lead to biased results. We use data on Hungarian couples married between 1930 and 1979 and loglinear models of scaled association. The results indicate some differences between bivariate and multivariate analyses. At each point of time bivariate models overestimate homogamy, both with respect to education and social origin. However, results on trends in time do not differ much between the two analyses. The exception is the period 1940–1959, in which bivariate analysis showed decreasing educational homogamy, and multivariate analysis showed an increasing trend. The latter finding can be explained by declining homogamy of social origin, as well as the weaker reproduction and cross-effects in this period.     

Regional innovation and small high technology firms in peripheral regions

This paper highlights the difficulty of developing HT firms in a peripheral region of Spain. Although the endogenous development of HT firms in these regions is possible, their evolution depends on more central regions. The firm's geographical location and the type of HT production are the most important endogenous factors governing the firm's success.     

Network neutrality and the nature of competition between network operators

The neutral architecture of the Internet is being challenged by various parties, such as network operators providing the connections to end-users, who are interested in gaining control of the information exchanged over the Internet. What are the effects on competition and welfare of such practices? Currently, there exists very little economic theory on network neutrality. This paper provides a preliminary analysis of the type of economic modeling that can address network neutrality, as well as of the type of results that can be expected.     

The endless need for regulation in telecommunication: An explanation

Telecommunication services have existed as a legal monopoly nearly throughout its entire history. In 1998, telecom market liberalisation was achieved across the European Union (EU) through the introduction of competition among telephone services. Asymmetrical obligations were deemed necessary in order to compensate the market power of the former monopolist.As the evolution of asymmetrical regulation in Spain illustrates, obligations and the telecommunications operators subject to them increased with the regulatory framework established in 2002 in the EU. This new regulatory framework may continue to expand through the inclusion of functional separation as another possible asymmetrical obligation. In short, it seems that the regulatory pressure on the telecommunications industry is increasing, despite the lapse in time since the liberalisation of the industry.In this paper, a methodology developed by the Austrian School of Economics is applied in order to explain why the telecommunication market is subject to increasing regulation in Europe, rather than deregulation, after more than 10 years of liberalisation. In particular, Mises's theory of price control is used to explain the evolution of the regulation of local loop unbundling.