Economic growth and exporting activity : an empirical analysis on Greek industry

The aim of the present paper is to locate and analyse the factors affecting firms’ economic behaviour in food products’ sector, by collecting data from a large number of firms and indicators. A special focus concerning exporting activity took place, trying to disclose the factors that spur exports, as they are considered to be a synonym of economic growth and prosperity. Several methodological issues of exports’ valuation were opposed and some strong conclusions were underlined as regard to the necessary infrastructure that a firm should develop, in order to grow, and to establish a dynamic exporting profile.peer-reviewe

Do birds of a feather flock together? evidence from time-varying herding behaviour of bitcoin and foreign exchange majors during Covid-19

This paper analyses herding behaviour within bitcoin and foreign exchange majors before and during the Covid-19 pandemic. We utilise both static and time-varying parameter regression herding measures to assess herding intensity based on hourly and daily frequencies, covering the period from 1 March 2018 to 28 February 2022. Our hourly static and time-varying model results indicate the absence of herding (hence, the presence of anti-herding behaviour) within bitcoin and the foreign exchange majors before and during Covid-19. In daily herding analyses, however, while we do not find evidence of herding within bitcoin or the foreign exchange majors, we do observe strong time-varying herding within the foreign exchange majors after excluding bitcoin both before and during Covid-19, and during both up- and down-market days. We conclude that herding behaviour between foreign exchange majors tends to be time-varying and horizon-dependent. Our results could be useful for bitcoin and foreign exchange investors, traders, researchers and regulators, helping them to strengthen their understanding of herding behaviour before and during periods of market stress such as the period of Covid-19

Cointegration and ARDL specification between the Dubai crude oil and the US natural gas market

This paper examines the relationship between the price of the Dubai crude oil and the price of the US natural gas using an updated monthly dataset from 1992 to 2018, incorporating the latter events in the energy markets. After employing a variety of unit root and cointegration tests, the long-run relationship is examined via the autoregressive distributed lag (ARDL) cointegration technique, along with the Toda-Yamamoto (1995) causality test. Our results indicate that there is a long-run relationship with a unidirectional causality running from the Dubai crude oil market to the US natural gas market. A variety of post specification tests indicate that the selected ARDL model is well-specified, and the results of the Toda-Yamamoto approach via impulse response functions, forecast error variance decompositions, and historical decompositions with generalized weights, show that the Dubai crude oil price retains a positive relationship and affects the US natural gas price.Comment: 23 pages, 5 figures, 7 table

Do commodity investors herd? Evidence from a time-varying stochastic volatility model

Commodities markets due to their unique characteristics as diversification tools have recently garnered investors’ attention especially through the development of commodity index financial products. This financialization process that started in the early 2000s and escalated after 2004 has precipitated price comovements among various types of commodities creating a proper setting for the examination of herding behavior. Employing a comprehensive dataset of investable commodities indices we examine the existence of herding behavior via static and time varying models. Our findings reveal a non significant anti herding behavior according to static model that is reversed when time varying models are in place. In particular the rolling window analysis reveals interesting patterns of the herding phenomenon. These behavioral patterns are corroborated through a time varying stochastic volatility model. Our results contain significant implications for investors, commodities producers and policy makers.http://www.elsevier.com/locate/resourpol2016-12-31hb201