Dynamic spillovers of oil price shocks and economic policy uncertainty

This study examines the dynamic relationship between changes in oil prices and the economic policy uncertainty index for a sample of both net oil-exporting and net oil-importing countries over the period 1997:01–2013:06. To achieve that, an extension of the Diebold and Yilmaz (2009, 2012) dynamic spillover index based on structural decomposition is employed. The results reveal that economic policy uncertainty (oil price shocks) responds negatively to aggregate demand oil price shocks (economic policy uncertainty shocks). Furthermore, during the Great Recession of 2007–2009, total spillovers increase considerably, reaching unprecedented heights. Moreover, in net terms, economic policy uncertainty becomes the dominant transmitter of shocks between 1997 and 2009, while in the post-2009 period there is a significant role for supply-side and oil specific demand shocks, as net transmitters of spillover effects. These results are important for policy makers, as well as, investors interested in the oil market

Dynamic co-movements of stock market returns, implied volatility and policy uncertainty

We examine time-varying correlations among stock market returns, implied volatility and policy uncertainty. Our findings suggest that correlations are indeed time-varying and sensitive to oil demand shocks and US recessions. Highlights: We examine dynamic correlations of stock market returns, implied volatility and policy uncertainty. Dynamic correlations reveal heterogeneous patterns during US recessions. Aggregate demand oil price shocks and US recessions affect dynamic correlations. A rise in the volatility of policy uncertainty dampens stock market returns and increases uncertainty. Increases in stock market volatility reduce stock market returns and increase uncertainty

Oil shocks and stock markets: Dynamic connectedness under the prism of recent geopolitical and economic unrest.

In this study we examine the dynamic structural relationship between oil price shocks and stock market returns or volatility for a sample of both net oil–exporting and net oil–importing countries between 1995:09 and 2013:07. We accomplish that, by extending the Diebold and Yilmaz (2014) dynamic connectedness measure using structural forecast error variance de- composition. The results for both stock market returns and volatility suggest that connect- edness varies across different time periods, and that this time–varying character is aligned with certain developments that take place in the global economy. In particular, aggregate demand shocks appear to act as the main transmitters of shocks to stock markets during periods characterised by economic–driven events, while supply–side and oil–specific demand shocks during periods of geopolitical unrest. Furthermore, differences regarding the direc- tions and the strength of connectedness can be reported both between and within the net oil–importing and net oil–exporting countries. These results are of particular importance to investors and portfolio managers, given the recent financialisation of the oil market

Oil dependence, quality of political institutions and economic growth: A panel VAR approach

This paper examines the resource curse hypothesis both within and between countries of different democratic footprint, based on a dynamic model that properly accounts for endogeneity issues. To achieve that, we apply a panel Vector Auto-Regressive (PVAR) approach along with panel impulse response functions to data on oil dependence variables, economic growth and several political institutional variables in 76 countries classified by different income groupings and level of development, over the period 1980–2012. Our results suggest that controlling for the quality of political institutions, and in particular the constraints to the executives, is important in rendering the resource curse hypothesis significant. Doing so, the resource curse hypothesis is documented mainly for developing economies and medium-high income countries. Specifically, when economies from the aforementioned groups are characterised by weak quality of political institutions, then oil dependence is not growth-enhancing

Forecasting Accuracy Evaluation of Tourist Arrivals.

This paper evaluates the use of several parametric and nonparametric forecasting techniques for predicting tourism demand in selected European countries. We find that no single model can provide the best forecasts for any of the countries in the short-, medium- and long-run. The results, which are tested for statistical significance, enable forecasters to choose the most suitable model (from those evaluated here) based on the country and horizon for forecasting tourism demand. Should a single model be of interest, then, across all selected countries and horizons the Recurrent Singular Spectrum Analysis model is found to be the most efficient based on lowest overall forecasting error. Neural Networks and ARFIMA are found to be the worst performing models

Hiding in Plain Sight: A Longitudinal Study of Combosquatting Abuse

Domain squatting is a common adversarial practice where attackers register domain names that are purposefully similar to popular domains. In this work, we study a specific type of domain squatting called "combosquatting," in which attackers register domains that combine a popular trademark with one or more phrases (e.g., betterfacebook[.]com, youtube-live[.]com). We perform the first large-scale, empirical study of combosquatting by analyzing more than 468 billion DNS records---collected from passive and active DNS data sources over almost six years. We find that almost 60% of abusive combosquatting domains live for more than 1,000 days, and even worse, we observe increased activity associated with combosquatting year over year. Moreover, we show that combosquatting is used to perform a spectrum of different types of abuse including phishing, social engineering, affiliate abuse, trademark abuse, and even advanced persistent threats. Our results suggest that combosquatting is a real problem that requires increased scrutiny by the security community

Oil volatility, oil and gas firms and portfolio diversification

This paper investigates the volatility spillovers and co-movements among oil prices and stock prices of major oil and gas corporations over the period between 18th June 2001 and 1st February 2016. To do so, we use the spillover index approach by Diebold and Yilmaz (2009, 2012, 2014, 2015) and the dynamic correlation coefficient model of Engle (2002) so as to identify the transmission mechanisms of volatility shocks and the contagion of volatility among oil prices and stock prices of oil and gas companies, respectively. Given that volatility transmission across oil and major oil and gas corporations is important for portfolio diversification and risk management, we also examine optimal weights and hedge ratios among the aforementioned series. Our results point to the existence of significant volatility spillover effects among oil and oil and gas companies’ stock volatility. However, the spillover is usually unidirectional from oil and gas companies’ stock volatility to oil volatility, with BP, CHEVRON, EXXON, SHELL and TOTAL being the major net transmitters of volatility to oil markets. Conditional correlations are positive and time-varying, with those between each of the aforementioned companies and oil being the highest. Finally, the diversification benefits and hedging effectiveness based on our results are discussed

A haystack full of needles: scalable detection of IoT devices in the wild

Consumer Internet of Things (IoT) devices are extremely popular, providing users with rich and diverse functionalities, from voice assistants to home appliances. These functionalities often come with significant privacy and security risks, with notable recent large scale coordinated global attacks disrupting large service providers. Thus, an important first step to address these risks is to know what IoT devices are where in a network. While some limited solutions exist, a key question is whether device discovery can be done by Internet service providers that only see sampled flow statistics. In particular, it is challenging for an ISP to efficiently and effectively track and trace activity from IoT devices deployed by its millions of subscribers --all with sampled network data. In this paper, we develop and evaluate a scalable methodology to accurately detect and monitor IoT devices at subscriber lines with limited, highly sampled data in-the-wild. Our findings indicate that millions of IoT devices are detectable and identifiable within hours, both at a major ISP as well as an IXP, using passive, sparsely sampled network flow headers. Our methodology is able to detect devices from more than 77% of the studied IoT manufacturers, including popular devices such as smart speakers. While our methodology is effective for providing network analytics, it also highlights significant privacy consequences

Surface profile gradient in amorphous Ta<inf>2</inf>O<inf>5</inf> semi conductive layers regulates nanoscale electric current stability

© 2016 The Author(s)A link between the morphological characteristics and the electric properties of amorphous layers is established by means of atomic, conductive, electrostatic force and thermal scanning microscopy. Using amorphous Ta2O5 (a-Ta2O5) semiconductive layer, it is found that surface profile gradients (morphological gradient), are highly correlated to both the electron energy gradient of trapped electrons in interactive Coulombic sites and the thermal gradient along conductive paths and thus thermal and electric properties are correlated with surface morphology at the nanoscale. Furthermore, morphological and electron energy gradients along opposite conductive paths of electrons intrinsically impose a current stability anisotropy. For either long conductive paths (L > 1 μm) or along symmetric nanodomains, current stability for both positive and negative currents i is demonstrated. On the contrary, for short conductive paths along non-symmetric nanodomains, the set of independent variables (L, i) is spanned by two current stability/intability loci. One locus specifies a stable state for negative currents, while the other locus also describes a stable state for positive currents