David Roubaud

Bio: David Roubaud is an academic researcher from University of Montpellier. The author has contributed to research in topics: Volatility (finance) & Granger causality. The author has an hindex of 46, co-authored 142 publications receiving 8068 citations.

On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier?

Abstract: This paper uses a dynamic conditional correlation model to examine whether Bitcoin can act as a hedge and safe haven for major world stock indices, bonds, oil, gold, the general commodity index and the US dollar index. Daily and weekly data span from July 2011 to December 2015. Overall, the empirical results indicate that Bitcoin is a poor hedge and is suitable for diversification purposes only. However, Bitcoin can only serve as a strong safe haven against weekly extreme down movements in Asian stocks. We also show that Bitcoin hedging and safe haven properties vary between horizons.

How economic growth, renewable electricity and natural resources contribute to CO2 emissions?

Abstract: This study explores the relationship between economic growth and CO2 emissions in the so-called European Union 5 (EU-5) countries (Germany, France, Italy, Spain, and the United Kingdom) for the 1985–2016 period. In doing so, we employ a carbon emission function to investigate the environmental Kuznets curve phenomenon, which describes a relationship between economic growth and environmental degradation. The empirical results confirm the existence of an N-shaped relationship between economic growth and CO2 emissions in the EU-5 countries. We incorporate additional variables such as renewable electricity consumption, trade openness, natural resource abundance, and energy innovation to augment the carbon emission function. Renewable electricity consumption, natural resources, and energy innovation improve environmental quality, while trade openness and the interaction between economic growth and renewable electricity consumption exert a positive impact on CO2 emissions. This study is novel in that it presents an interaction between economic growth and renewable electricity consumption. We also confirm the need for renewable energy regulations related to increasing renewable sources and promoting energy innovation to reduce the negative effects of energy and fossil energy resources on environmental degradation.

Environmental Degradation in France: The Effects of FDI, Financial Development, and Energy Innovations

Abstract: This paper explores the determinants of carbon emissions in France by accounting for the significant role played by foreign direct investment (FDI), financial development, economic growth, energy consumption and energy research innovations in influencing CO2 emissions function. In this endeavour, we employ the novel SOR (Shahbaz et al. 2017) unit root test on French time series data over the period 1955-2016 to examine the order of integration in the presence of sharp and smooth structural breaks in the variables. We also apply the bootstrapping bounds testing approach, recently developed by McNown et al. (2018), to investigate the presence of cointegration and the empirical findings underscore the presence of cointegration among the time series. Moreover, we find that FDI has a positive impact, while energy research innovations have a negative impact, on French carbon emissions. Financial development lowers carbon emissions, thereby improving the French environmental quality. FDI degrades the environment, and thus supports the pollution-haven hypothesis in France. Similarly, financial development suggests that financial stability is a required condition for improving environmental quality, so are energy research innovations. Contrarily, energy consumption is positively linked with carbon emissions. However, the relationship between economic growth and CO2 emissions is an inverted-U, which is a validation of the environmental Kuznets curve (EKC).

Industry 4.0 and the circular economy: a proposed research agenda and original roadmap for sustainable operations

Abstract: This work makes a case for the integration of the increasingly popular and largely separate topics of Industry 4.0 and the circular economy (CE). The paper extends the state-of-the-art literature by proposing a pioneering roadmap to enhance the application of CE principles in organisations by means of Industry 4.0 approaches. Advanced and digital manufacturing technologies are able to unlock the circularity of resources within supply chains; however, the connection between CE and Industry 4.0 has not so far been explored. This article therefore contributes to the literature by unveiling how different Industry 4.0 technologies could underpin CE strategies, and to organisations by addressing those technologies as a basis for sustainable operations management decision-making. The main results of this work are: (a) a discussion on the mutually beneficial relationship between Industry 4.0 and the CE; (b) an in-depth understanding of the potential contributions of smart production technologies to the ReSOLVE model of CE business models; (c) a research agenda for future studies on the integration between Industry 4.0 and CE principles based on the most relevant management theories.

Can volume predict Bitcoin returns and volatility? A quantiles-based approach

Abstract: Prior studies on the price formation in the Bitcoin market consider the role of Bitcoin transactions at the conditional mean of the returns distribution. This study employs in contrast a non-parametric causality-in-quantiles test to analyse the causal relation between trading volume and Bitcoin returns and volatility, over the whole of their respective conditional distributions. The nonparametric characteristics of our test control for misspecification due to nonlinearity and structural breaks, two features of our data that cover 19th December 2011 to 25th April 2016. The causality-in-quantiles test reveals that volume can predict returns – except in Bitcoin bear and bull market regimes. This result highlights the importance of modelling nonlinearity and accounting for the tail behaviour when analysing causal relationships between Bitcoin returns and trading volume. We show, however, that volume cannot help predict the volatility of Bitcoin returns at any point of the conditional distribution.

Convergence of Probability Measures

Abstract: Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

Estimation and Inference in Econometrics

Abstract: Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields (Chinese Translation)

Abstract: What makes organizations so similar? We contend that the engine of rationalization and bureaucratization has moved from the competitive marketplace to the state and the professions. Once a set of organizations emerges as a field, a paradox arises: rational actors make their organizations increasingly similar as they try to change them. We describe three isomorphic processes-coercive, mimetic, and normative—leading to this outcome. We then specify hypotheses about the impact of resource centralization and dependency, goal ambiguity and technical uncertainty, and professionalization and structuration on isomorphic change. Finally, we suggest implications for theories of organizations and social change.

Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case

Abstract: Epidemic outbreaks are a special case of supply chain (SC) risks which is distinctively characterized by a long-term disruption existence, disruption propagations (i.e., the ripple effect), and high uncertainty. We present the results of a simulation study that opens some new research tensions on the impact of COVID-19 (SARS-CoV-2) on the global SCs. First, we articulate the specific features that frame epidemic outbreaks as a unique type of SC disruption risks. Second, we demonstrate how simulation-based methodology can be used to examine and predict the impacts of epidemic outbreaks on the SC performance using the example of coronavirus COVID-19 and anyLogistix simulation and optimization software. We offer an analysis for observing and predicting both short-term and long-term impacts of epidemic outbreaks on the SCs along with managerial insights. A set of sensitivity experiments for different scenarios allows illustrating the model's behavior and its value for decision-makers. The major observation from the simulation experiments is that the timing of the closing and opening of the facilities at different echelons might become a major factor that determines the epidemic outbreak impact on the SC performance rather than an upstream disruption duration or the speed of epidemic propagation. Other important factors are lead-time, speed of epidemic propagation, and the upstream and downstream disruption durations in the SC. The outcomes of this research can be used by decision-makers to predict the operative and long-term impacts of epidemic outbreaks on the SCs and develop pandemic SC plans. Our approach can also help to identify the successful and wrong elements of risk mitigation/preparedness and recovery policies in case of epidemic outbreaks. The paper is concluded by summarizing the most important insights and outlining future research agenda.

Market Liquidity: Asset Pricing with Liquidity Risk

Abstract: This paper solves explicitly a simple equilibrium model with liquidity risk. In our liquidityadjusted capital asset pricing model, a security s required return depends on its expected liquidity as well as on the covariances of its own return and liquidity with the market return and liquidity. In addition, a persistent negative shock to a security s liquidity results in low contemporaneous returns and high predicted future returns. The model provides a unified framework for understanding the various channels through which liquidity risk may affect asset prices. Our empirical results shed light on the total and relative economic significance of these channels and provide evidence of flight to liquidity. r 2005 Elsevier B.V. All rights reserved.