We model cross-market Bitcoin prices as long-memory processes and study dynamic interdependence in a fractionally cointegrated VAR framework. We find (i) long-memory in both individual market and five-market systems depicting non-homogeneous informational inefficiency and (ii) a cointegration relationship with slow adjustment of shocks where uncertainty leaves a negative impact.
We model dynamic interdependence in cross-country economic growth processes by allowing it to vary according to democratic distance among economies. Stochastic distributional dynamics and temporal effects of democracy on economic growth are studied, and spatial variation in economic growth is explored. Among important results, democratic poverty trap is found to exist indicating the possibility of persistence of (un)stable democratic equilibria at different levels of democracy. Our cross-sectional regression evinces that democracy has exerted significant growth-enhancing effect and that the democratic distribution has steadily shifted locus from low-level to high-level equilibrium. Our spatial analysis of democracy-economic growth nexus provide evidence of significant dynamic spatial autocorrelation and complementarity among countries' growth processes. Finally, it is demonstrated that the relevance of geographical proximity in facilitating interdependence in economic growth is overshadowed by relational proximity.
Empirical analyses of supply chain networks (SCNs) in extant literature have been rare due to scarcity of data. As a result, theoretical research have relied on arbitrary growth models to generate network topologies supposedly representative of real-world SCNs. Our study is aimed at filling the above gap by systematically analysing a set of manufacturing sector SCNs to establish their topological characteristics. In particular, we compare the differences in topologies of undirected contractual relationships (UCR) and directed material flow (DMF) SCNs. The DMF SCNs are different from the typical UCR SCNs since they are characterised by a strictly tiered and an acyclic structure which does not permit clustering. Additionally, we investigate the SCNs for any self-organized topological features. We find that most SCNs indicate disassortative mixing and power law distribution in terms of interfirm connections. Furthermore, compared to randomised ensembles, self-organized topological features were evident in some SCNs in the form of either overrepresented regimes of moderate betweenness firms or underrepresented regimes of low betweenness firms. Finally, we introduce a simple and intuitive method for estimating the robustness of DMF SCNs, considering the loss of demand due to firm disruptions. Our work could be used as a benchmark for any future analyses of SCNs.
This paper studies characteristics of optimal investment decisions of risk-averse firms who engage in exports under two types of risks: endogenous and background risks. While endogenous risk arises from the fluctuations in spot exchange rate and affects directly the profit of an exporting firm, background risk arises from uncertain changes in firm-and industryspecific domestic and foreign policies. We propose a mean-variance decision-theoretic model to trace out impact of perturbations in the distributions of these uncertainties on the optimal investment strategy. A testable empirical model is derived and applied to a panel of 840 exporting Indian manufacturing firms for the period 1995-2015. Our results suggest that Indian manufacturing exporters depict decreasing absolute risk aversion and that firms' risk preferences are prone to variance vulnerability.
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