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.
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.
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.
We derive risk-neutral probability densities for future euro/Swiss franc exchange rates as implied by option prices. We find that the credibility of the Swiss franc floor decreased somewhat as the spot exchange rate approached the lower bound of 1.20 CHF per euro. We also compare the forecasting performance of a random walk benchmark model with an error-correction model (ECM) augmented with option-implied break probabilities of breaching the currency floor. We find some evidence that the augmented ECM has an informational advantage over the random walk when using one-month break probabilities. But we find that one-month option-implied densities cannot predict the entire range of exchange rate realizations.
The spatial lag model (SLM) has been widely studied in the literature for spatialised data modeling in various disciplines such as geography, economics, demography, regional sciences, etc. This is an extension of the classical linear model that takes into account the proximity of spatial units in modeling. In this paper, we propose a Bayesian estimation of the functional spatial lag (FSLM) model. The Bayesian MCMC technique is used as a method of estimation for the parameters of the model. A simulation study is conducted in order to compare the results of the Bayesian functional spatial lag model with the functional spatial lag model and the functional linear model. As an illustration, the proposed Bayesian functional spatial lag model is used to establish a relationship between the unemployment rate and the curves of illiteracy rate observed in the 45 departments of Senegal.
This paper empirically investigates the dynamics between budget deficit and government debt in the U.S. using two different measures of the budget deficit: the current budget deficit and cyclically-adjusted budget deficit. A threshold Vector autoregression (VAR) model is estimated to explore the dynamics in different regimes using quarterly data from 1947:Q1 to 2017:Q3. The specification test rejects a linear VAR model against the threshold VAR. When we use the current budget deficit, regime 1 resemble governments prioritize minimizing budget deficit and debt, whereas, regime 2 resemble otherwise. When we use the cyclically adjusted budget deficit, regime 1 resemble economic expansions, whereas, regime 2 resemble recessions. The impulse responses show evidence of asymmetry and counter-cyclicality. The impulse responses also indicate that an increase in the debt dictate the government’s response towards minimizing the budget deficit and tend to prioritize budget deficit less when the economy expands.
Mathematical models of economic dynamics and growth are usually expressed in terms of differential equations/inclusions (in the case of continuous time) or difference equations/inclusions (if discrete time is assumed).3 This class of models includes von Neumann-Leontief-Gale type dynamic input-output models to which the paper refers. The paper focuses on the turnpike stability of optimal growth processes in a Gale non-stationary economy with discrete time in the neighbourhood of von Neumann dynamic equilibrium states (so-called growth equilibrium). The paper refers to Panek (2019, 2020) and shows an intermediate result between the strong and very strong turnpike theorem in the non-stationary Gale economy with changing technology assuming that the prices of temporary equilibrium in such an economy (so-called von Neumann prices) do not change rapidly. The aim of the paper is to bring mathematical proof that the introduction of these assumptions making the model more realistic does not change its asymptotic (turnpike-like) properties.
The purpose of the present study was to investigate the relationships between different dimensions of corporate social responsibility (CSR), as well as the mediating role of innovation between CSR dimensions and financial performance. Data was collected with questionnaires from 321 managers of Slovene companies to test a conceptual model with structural equation modeling (SEM). The field-research results were that CSR is the most relevant dimension for employees. It positively influences CSR to the natural environment, to customers, and to the local community. The mediating role of innovation between CSR and financial performance was confirmed. The results also showed that CSR to the natural environment and CSR to customers positively affect innovation, while CSR to the local community had a negative impact. In addition, the positive impact of innovation initiated by CSR on financial performance was confirmed. The principal limitation of this study was its focus on Slovenian firms and the fact that data was obtained from only one manager in each firm. Slovene companies should consider the global initiatives supportive of CSR as the way to create opportunities for innovation and differentiation from other companies and increase their financial performance. The conceptual model developed and tested on the data obtained by Slovene managers gives new perspective on the impacts of social responsibility, innovation and financial performance. It highlights the areas in which the theory of social responsibility needs more research.
The aim of this paper is to examine a complex pattern of mutual interdependence between Unified Growth Theory (subroutine) and the evolution of the entire field of economic growth theories (main routine) from a philosophical and methodological perspective. The analysis utilises the recently introduced concept of research routine (and respectively, subroutine) aimed at an explanation of the evolution of scientific research. The study identifies the influence of the subroutine (and its specific concept of demographic transition) on the core concepts of the main routine: human capital, population growth and learning. The results are based on network analyses of extensive bibliometric evidence from Scopus and the Web of Knowledge.