Rangan Gupta

Bio: Rangan Gupta is an academic researcher from University of Pretoria. The author has contributed to research in topics: Volatility (finance) & Monetary policy. The author has an hindex of 57, co-authored 1098 publications receiving 16142 citations. Previous affiliations of Rangan Gupta include University of Connecticut & Eastern Mediterranean University.

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.

Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions

Abstract: We examine whether Bitcoin can hedge global uncertainty, measured by the first principal component of the VIXs of 14 developed and developing equity markets. After decomposing Bitcoin returns into various frequencies, i.e., investment horizons, and given evidence of heavy-tails, we employ quantile regression. We reveal that Bitcoin does act as a hedge against uncertainty: it reacts positively to uncertainty at both higher quantiles and shorter frequency movements of Bitcoin returns. Further, we use quantile-on-quantile regression and identify that hedging is observed at shorter investment horizons, and at both lower and upper ends of Bitcoin returns and global uncertainty.

Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions

Abstract: In this study, we analyse whether Bitcoin can hedge uncertainty using daily data for the period of 17th March, 2011, to 7th October, 2016. Global uncertainty is measured by the first principal component of the VIXs of 14 developed and developing equity markets. We first use wavelets to decompose Bitcoin returns into various frequencies, i.e., investment horizons. Then, we apply standard OLS regressions and observe that uncertainty negatively affects raw Bitcoin return and its longer-term movements. However, given the heavy tails of the variables, we rely on quantile methods and reveal much more nuanced and interesting results. Quantile regressions indicate that Bitcoin does act as a hedge against uncertainty, that is, it reacts positively to uncertainty at both higher quantiles and shorter frequency movements of Bitcoin returns. Finally, when we use quantile-on-quantile regressions, we observe that hedging is observed at shorter investment horizons, and at both lower and upper ends of Bitcoin returns and global uncertainty.

The nexus of electricity consumption, economic growth and CO2 emissions in the BRICS countries

Abstract: This study reexamines the causal link between electricity consumption, economic growth and CO 2 emissions in the BRICS countries (i.e., Brazil, Russia, India, China, and South Africa) for the period 1990–2010, using panel causality analysis, accounting for dependency and heterogeneity across countries. Regarding the electricity–GDP nexus, the empirical results support evidence on the feedback hypothesis for Russia and the conservation hypothesis for South Africa. However, a neutrality hypothesis holds for Brazil, India and China, indicating neither electricity consumption nor economic growth is sensitive to each other in these three countries. Regarding the GDP–CO 2 emissions nexus, a feedback hypothesis for Russia, a one-way Granger causality running from GDP to CO 2 emissions in South Africa and reverse relationship from CO 2 emissions to GDP in Brazil is found. There is no evidence of Granger causality between GDP and CO 2 emissions in India and China. Furthermore, electricity consumption is found to Granger cause CO 2 emissions in India, while there is no Granger causality between electricity consumption and CO 2 emissions in Brazil, Russia, China and South Africa. Therefore, the differing results for the BRICS countries imply that policies cannot be uniformly implemented as they will have different effects in each of the BRICS countries under study.

Has oil price predicted stock returns for over a century

Abstract: This paper contributes to the debate on the role of oil prices in predicting stock returns. The novelty of the paper is that it considers monthly time-series historical data that span over 150 years (1859:10–2013:12) and applies a predictive regression model that accommodates three salient features of the data, namely, a persistent and endogenous oil price, and model heteroscedasticity. Three key findings are unraveled: first, oil price predicts US stock returns. Second, in-sample evidence is corroborated by out-sample evidence of predictability. Third, both positive and negative oil price changes are important predictors of US stock returns, with negative changes relatively more important. Our results are robust to the use of different estimators and choice of in-sample periods.

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.

Economic growth and income inequality

Abstract: We investigate whether income inequality affects subsequent growth in a cross-country sample for 1965-90, using the models of Barro (1997), Bleaney and Nishiyama (2002) and Sachs and Warner (1997), with negative results. We then investigate the evolution of income inequality over the same period and its correlation with growth. The dominating feature is inequality convergence across countries. This convergence has been significantly faster amongst developed countries. Growth does not appear to influence the evolution of inequality over time. Outline

The Self-Organizing Map

Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.