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The impact of Big Data on the organization of the European market
Capitalism. Democracy. Rule of Law

Abstract

The asymmetric short – and long-run relationships between BRICS stock markets are examined using monthly stock price data from January 2001 through December 2014. The asymmetric co-integration analysis confirms the presence of a long-run association between the BRICS stock markets; where, the speed of adjustment to the negative shocks is higher and statistically significant for the Brazil-India and China-India pairs, which indicates quick adjustment of stock prices to bad news compared to good news. Conversely, the speed of adjustment for Indian and South African stock markets is higher for positive shocks, while the relationship between the stock markets pair of Russia and South Africa is linear. The results of asymmetric error correction model (AECM) reveal evidence of bidirectional causality between China-India, India-South Africa and South Africa-Russia, while unidirectional causality runs from the Indian to Brazilian stock market. Thus, we can safely conclude that the Indian stock market has long-run and short-run relationships with most of the other stock markets. This suggests that investors should pay attention to the Indian stock market when investing in BRICS stock markets.

Abstract

This paper addresses the issue of measuring tolerance, viewed as a multifaceted phenomenon involving several different social domains. We develop a multidimensional index for Likert-scale data, characterized by the following features: (i) it reflects the individual’s intensity of tolerant attitudes towards each social domain; (ii) the index can be broken down by dimension in order to determine the contribution of each dimension to overall tolerance; (iii) the index combines the different dimensions of tolerance using a weighted scheme that reflects the importance of each dimension in determining the overall level of tolerance. To show how this new measure of tolerance works in practice, we carry out a case study using an Italian recent survey asking the opinion of university students about different subjects, such as interreligious dialog, women/religion relationship, religion/death relationship, homosexuality, and multicultural society.

Abstract

The NoVaS methodology for prediction of stationary financial returns is reviewed, and the applicability of the NoVaS transformation for volatility estimation is illustrated using realized volatility as a proxy. The realm of applicability of the NoVaS methodology is then extended to non-stationary data (involving local stationarity and/or structural breaks) for one-step ahead point prediction of squared returns. In addition, a NoVaS-based algorithm is proposed for the construction of bootstrap prediction intervals for one-step ahead squared returns for both stationary and non-stationary data. It is shown that the “Time-varying” NoVaS is robust against possible nonstationarities in the data; this is true in terms of locally (but not globally) financial returns but also in change point problems where the NoVaS methodology adapts fast to the new regime that occurs after an unknown/undetected change point. Extensive empirical work shows that the NoVaS methodology generally outperforms the GARCH benchmark for (i) point prediction of squared returns, (ii) interval prediction of squared returns, and (iii) volatility estimation. With regard to target (i), earlier work had shown little advantage of using a nonzero α in the NoVaS transformation. However, in terms or targets (ii) and (iii), it appears that using the Generalized version of NoVaS—either Simple or Exponential—can be quite beneficial and well-worth the associated computational cost.

Abstract

This paper proposes a Bayesian estimation algorithm to estimate Generalized Partition of Unity Copulas (GPUC), a class of nonparametric copulas recently introduced by [18]. The first approach is a random walk Metropolis-Hastings (RW-MH) algorithm, the second one is a random blocking random walk Metropolis-Hastings algorithm (RBRW-MH). Both approaches are Markov chain Monte Carlo methods and can cope with ˛at priors. We carry out simulation studies to determine and compare the efficiency of the algorithms. We present an empirical illustration where GPUCs are used to nonparametrically describe the dependence of exchange rate changes of the crypto-currencies Bitcoin and Ethereum.

Abstract

We show that each infinite exchangeable sequence τ1, τ2, . . . of random variables of the generalised Marshall–Olkin kind can be uniquely linked to an additive subordinator via its deFinetti representation. This is useful for simulation, model estimation, and model building.

Abstract

The possible nonlinearity of the income elasticity of child labor has been at the center of the debate regarding both its causes and the policy instruments to address it. We contribute to this debate providing theoretical and empirical novel results. From a theoretical point of view, for any given transfer size, there is a critical level of household income below which an increase in income has no impact on child labor and education. We estimate the causal impact of an increase in income on child labor and education exploiting the random allocation of the Child Grant Programme, an unconditional cash transfer (CT), in Lesotho. We show that the poorest households do not increase investment in children’s human capital, while relatively less poor households reduce child labor and increase education. In policy terms, the results indicate that CTs might not be always effective to support the investment in children’s human capital of the poorest households. Beside the integration with other measures, making the amount of transfer depends of the level of deprivation of the household, might improve CT effectiveness.