We apply different techniques and uncover the quantile conditional dependence between the global financial stress index and Bitcoin returns from July 18, 2010, to December 29, 2017. The results from the copula-based dependence show evidence of right-tail dependence between the global financial stress index and Bitcoin returns. We focus on the conditional quantile dependence and indicate that the global financial stress index strongly Granger-causes Bitcoin returns at the left and right tail of the distribution of the Bitcoin returns, conditional on the global financial stress index. Finally, we use a bivariate cross-quantilogram approach and show only limited directional predictability from the global financial stress index to Bitcoin returns in the medium term, for which Bitcoin can act as a safe-haven against global financial stress.
This paper investigates the relationship between white precious metals and gold, oil and global equity by means of spillovers and volatility transmission. Relying on the recently introduced ETFs, this study is the first to analyse return spillovers derived from an E-GARCH model and to take into account frequency dynamics to understand changes in connectedness across periods of time. Results uncover numerous channels of return transmission across the selected ETF markets over the last 10 years and highlight the role of gold ETFs as the most influential market in the sample. Furthermore, our work provides insights into the characteristics of white precious metal markets using a hidden semi-Markov model. Finally, we argue that even though silver and platinum have gained more importance as investment assets over the last few years, palladium still very much remains an industrial metal.
Recent years have witnessed companies abandon traditional open-loop supply chain structures in favour of closed-loop variants, in a bid to mitigate environmental impacts and exploit economic opportunities. Central to the closed-loop paradigm is remanufacturing: the restoration of used products to useful life. While this operational model has huge potential to extend product life-cycles, the collection and recovery processes diminish the effectiveness of existing control mechanisms for open-loop systems. We systematically review the literature in the field of closed-loop supply chain dynamics, which explores the time-varying interactions of material and information flows in the different elements of remanufacturing supply chains. We supplement this with further reviews of what we call the three 'pillars' of such systems, i.e. forecasting, collection, and inventory and production control. This provides us with an interdisciplinary lens to investigate how a 'boomerang' effect (i.e. sale, consumption, and return processes) impacts on the behaviour of the closed-loop system and to understand how it can be controlled. To facilitate this, we contrast closed-loop supply chain dynamics research to the welldeveloped research in each pillar; explore how different disciplines have accommodated the supply, process, demand, and control uncertainties; and provide insights for future research on the dynamics of remanufacturing systems.
We propose a significance test to determine if data on a regular d-dimensional grid can be assumed to be a realization of Gaussian process. By accounting for the spatial dependence of the observations, we derive statistics analogous to sample skewness and kurtosis. We show that the sum of squares of these two statistics converges to a chi-square distribution with two degrees of freedom. This leads to a readily applicable test. We examine two variants of the test, which are specified by two ways the spatial dependence is estimated. We provide a careful theoretical analysis, which justifies the validity of the test for a broad class of stationary random fields. A simulation study compares several implementations. While some implementations perform slightly better than others, all of them exhibit very good size control and high power, even in relatively small samples. An application to a comprehensive data set of sea surface temperatures further illustrates the usefulness of the test.
The detection of (structural) breaks or the so called change point problem has drawn increasing attention from the theoretical, applied economic and financial fields. Much of the existing research concentrates on the detection of change points and asymptotic properties of their estimators in panels when N, the number of panels, as well as T , the number of observations in each panel are large. In this paper we pursue a different approach, i.e., we consider the asymptotic properties when N → ∞ while keeping T fixed. This situation is typically related to large (firm-level) data containing financial information about an immense number of firms/stocks across a limited number of years/quarters/months. We propose a general approach for testing for break(s) in this setup. In particular, we obtain the asymptotic behavior of test statistics. We also propose a wild bootstrap procedure that could be used to generate the critical values of the test statistics. The theoretical approach is supplemented by numerous simulations and by an empirical illustration. We demonstrate that the testing procedure works well in the framework of the four factors CAPM model. In particular, we estimate the breaks in the monthly returns of US mutual funds during the period January 2006 to February 2010 which covers the subprime crises.
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