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In this chapter I characterize the relationship between macroeconomic variables and the terms structure of interest rates using the recent macro-finance approach adapted to the case of an emerging economy and applying it to Brazil. I find that macro variables help to explain the dynamics of the yield curve in emerging markets, specially in periods of high volatility. Moreover, the notion of great external vulnerability of emerging economies is confirmed by the strong role of the nominal exchange rate change, which explains up to 37% of the variation in yields in Brazil. However, the model does a poor job in forecasting yields during the financial crisis of 2008. This fact seems to be related to the strong fall in international commodity and industrial goods prices (in dollar terms), which limited the passthrough from the strong depreciation of the exchange rate to inflation.

This paper compares alternative estimation procedures for multi-level factor models which imply blocks of zero restrictions on the associated matrix of factor loadings. We suggest a sequential least squares algorithm for minimizing the total sum of squared residuals and a two-step approach based on canonical correlations that are much simpler and faster than Bayesian approaches previously employed in the literature. An additional advantage is that our approaches can be used to estimate more complex multi-level factor structures where the number of levels is greater than two. Monte Carlo simulations suggest that the estimators perform well in typical sample sizes encountered in the factor analysis of macroeconomic data sets. We apply the methodologies to study international comovements of business and financial cycles.

Poor performance of India’s commercial banks, in the public and private sectors as well as those owned by foreign interests, has been a major concern of the policymakers. Their gross non-performing assets (NPAs), as a proportion of gross advances, were 10.2% as of March 2017, which is reported to have grown to 11.6% in March 2018. The public sector banks (PSBs) have a share of 70% of business, and the ratio of NPAs to gross advances is 15.6%. The Reserve Bank of India’s forecast is that the ratio for PSBs would rise to 17.3% by March 2019, of private banks to 5.3%, and of foreign banks to 4.8%. This chapter focuses on causal factors which comprise macroeconomic as well as bank-specific factors, influencing NPA. We undertake a panel approach by using 16 annual observations (from fiscal year 2000–2001 to 2015–2016) for three groups of banks by ownership: public, private, and foreign. The study findings reveal that the macroeconomic and bank-specific factors are important determinants.

We address the problem of selecting the common factors that are relevant for forecasting macroeconomic variables. In economic forecasting using diffusion indexes, the factors are ordered, according to their importance, in terms of relative variability, and are the same for each variable to predict, that is, the process of selecting the factors is not supervised by the predictand. We propose a simple and operational supervised method, based on selecting the factors on the basis of their significance in the regression of the predictand on the predictors. Given a potentially large number of predictors, we consider linear transformations obtained by principal components analysis. The orthogonality of the components implies that the standard t-statistics for the inclusion of a particular component are independent, and thus applying a selection procedure that takes into account the multiplicity of the hypotheses tests is both correct and computationally feasible. We focus on three main multiple testing procedures: Holm's sequential method, controlling the familywise error rate, the Benjamini–Hochberg method, controlling the false discovery rate, and a procedure for incorporating prior information on the ordering of the components, based on weighting the p-values according to the eigenvalues associated to the components. We compare the empirical performances of these methods with the classical diffusion index (DI) approach proposed by Stock and Watson, conducting a pseudo-real-time forecasting exercise, assessing the predictions of eight macroeconomic variables using factors extracted from an U.S. dataset consisting of 121 quarterly time series. The overall conclusion is that nature is tricky, but essentially benign: the information that is relevant for prediction is effectively condensed by the first few factors. However, variable selection, leading to exclude some of the low-order principal components, can lead to a sizable improvement in forecasting in specific cases. Only in one instance, real personal income, we were able to detect a significant contribution from high-order components.

Research on high-growth firms (HGFs) or gazelles is expanding due to their significant contribution to job growth and economic development. However, the knowledge about the conditions and factors that set these firms on their rapid growth trajectory remains fragmented. Therefore, this chapter provides an abreast inventory of the surging gazelle studies by systematically reviewing the international gazelle growth literature and consolidating firm-level, industry-level, and macroeconomic-level growth factors and their interactions as elaborated in the studies. Based on the review of 62 international empirical studies, this chapter finds that the gazelle growth is complex and multidimensional in its scope and nature. The firm’s growth intention and entrepreneurial nature emerge as necessary but not sufficient conditions to guarantee rapid growth as it results from the impact of and interaction between various firm-level and external factors. The different growth-influencing factors are summarized using a theoretical gazelle growth model, which supports the rare and temporal nature of the gazelle growth.

The main objective of this study is to identify the main determinants of the Islamic banks’ performance in Gulf Cooperation Council (GCC) regions.

The research uses both static model (fixed effects and random effects) and Generalized method of Moments (GMM). The data for this study are obtained from the annual reports of 29 Islamic banks from GCC countries using Bankscope database for the period from 2005 to 2013.

The empirical findings reveal that Islamic banks’ specific factors such as the equity financing and bank size are positive and statistically significant to the profitability of Islamic banks. The operating efficiency ratio is negatively and statistically significant to return on asset. It is also found that macroeconomic variables such as money supply and inflation are negatively and statistically significant to the performance of Islamic banks whereas oil price has been found positive and statistically significant to the performance of Islamic banks in the GCC region.

The present study seeks to fill a demanding gap in the literature by providing new empirical evidence on the factors that influence the profitability of the Islamic banking sector in GCC regions.

These findings have significant contribution to the literature by comprehensively clarifying and critically analyzing the current state of profitability among the Islamic banks in GCC regions.

In the context of Dynamic Factor Models, we compare point and interval estimates of the underlying unobserved factors extracted using small- and big-data procedures. Our paper differs from previous works in the related literature in several ways. First, we focus on factor extraction rather than on prediction of a given variable in the system. Second, the comparisons are carried out by implementing the procedures considered to the same data. Third, we are interested not only on point estimates but also on confidence intervals for the factors. Based on a simulated system and the macroeconomic data set popularized by Stock and Watson (2012), we show that, for a given procedure, factor estimates based on different cross-sectional dimensions are highly correlated. On the other hand, given the cross-sectional dimension, the maximum likelihood Kalman filter and smoother factor estimates are highly correlated with those obtained using hybrid procedures. The PC estimates are somehow less correlated. Finally, the PC intervals based on asymptotic approximations are unrealistically tiny.

The chapter contains a methodology for formalized evaluation of the public fiscal policy from the view point of its impact on the financial stability of a national economy using the example of the Russian Federation and taking into account the fiscal multipliers’ effects. The significance of this problem is predetermined by recent trends in Russia’s development, when the national economy legs twice behind the world indicators. Taking into account the importance of the Russian budget system as a mechanism for the redistribution of gross domestic product (GDP), the financial stability safeguarding has been connected with the public finance sustainability and with the federal budget revenues and expenditures equilibrium. There are used the methodology of analysis of economic systems’ dynamic factors of financial stability as well as fiscal multipliers’ effects, aiming at managing national economy’s long-term development with the ultimate purpose to maintain the GDP growth rates. Taking into account the fiscal multipliers’ values, the model comparisons of the macroeconomics and budget parameters’ dynamics prove the necessity of the budget consolidation policy in 2018–2020 provided that the budget expenditures efficiency increases. The latter has been proved by modeling dependences represented by the fiscal multipliers’ effects in terms of national financial stability.

This current research tries to answer the widespread debate about the role of derivatives in propagating the last financial crisis. So, this work aims to examine the effect of derivatives on bank stability in emerging countries by using the bank stability index (BSI) as developed by Ghosh (2011) from three major dimensions of banking operations: stability, soundness, and profitability. We use the generalized method of moments (GMM) estimator technique developed by Blundell and Bond (1998) to estimate regressions during the normal, the turbulent, and the whole period, following the guidance given by Chiaramonte, Poli, and Oriani (2013).

The major conclusion of this study reveals that except to futures the other derivative instruments cannot be considered as troubling factors. The main implication of the research shows that derivatives – in general – are not responsible for the propagation of the recent financial crisis. Hence, the common debate accusing derivatives as being responsible for the aggravation of the recent financial crisis should be rejected.