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The purpose of this study is to examine the effect of risk sharing and non-risk sharing instruments on both the profitability of Islamic banks and the economic growth of the country. This study also aims to improve the profit and loss sharing-based asset growth of Islamic banks.

The data for this study are obtained from the annual reports of all Islamic banks from Bangladesh using Bank scope database and annual report for the period of 1983–2014. The research uses Autoregressive Distributive Lag approach.

The findings reveal that risk sharing instruments are positively related to profitability and the economic growth of the country. This study also finds that non-risk sharing instruments play a predominant role in the profitability of the Islamic bank but are negatively related to the economic growth of the country.

Banks and other financial institutions need to pay greater attention to systemic risk created by risk transfer and apply risk sharing methods of financing more vigorously than has hitherto been the case.

This study will also contribute to the literature as relatively few Islamic financial literatures deal with the relationship between equity financing and profitability which may make a strong contribution to the area of Islamic finance.

This article proposes a new approach to detecting the presence of common cyclical features when several time series are sampled at different frequencies. We generalize the common-frequency approach introduced by Engle and Kozicki (1993) and Vahid and Engle (1993). We start with the mixed-frequency VAR representation investigated in Ghysels (2012) for stationary time series. For non-stationary time series in levels, we show that one has to account for the presence of two sets of long-run relationships. The first set is implied by identities stemming from the fact that the differences of the high-frequency I (1) regressors are stationary. The second set comes from possible additional long-run relationships between one of the high-frequency series and the low-frequency variables. Our transformed vector error-correction model (VECM) representations extend the results of Ghysels (2012) and are important for determining the correct set of variables to be used in a subsequent common cycle investigation. This fact has implications for the distribution of test statistics and for forecasting. Empirical analyses with quarterly real gross national product (GNP) and monthly industrial production indices for, respectively, the United States and Germany illustrate our new approach. We also conduct a Monte Carlo study which compares our proposed mixed-frequency models with models stemming from classical temporal aggregation methods.

This chapter considers the estimation of a parametric single-index predictive regression model with integrated predictors. This model can handle a wide variety of non-linear relationships between the regressand and the single-index component containing either the cointegrated predictors or the non-cointegrated predictors. The authors introduce a new estimation procedure for the model and investigate its finite sample properties via Monte Carlo simulations. This model is then used to examine stock return predictability via various combinations of integrated lagged economic and financial variables.

In this chapter, the relationship between terrorism and military expenditure and between terrorism and foreign capital inflow has been studied empirically with Indian data. We considered an index for terrorism based on the number of terrorism incidents, the number of deaths and the number of injuries. Data are collected from the period of 1977–1978 to 2016–2017 on the incidence of terrorism, obtained from the data released by Government of India in July 2016. Augmented Dicky–Fuller (ADF) test is used for unit root and stationarity checks. Johansen co-integration test is performed for testing the presence of co-integrating relationship between Index of terrorism and military expenditure and also between FDI flow and index of terrorism. As a result, a co-integrating relationship is also found between terrorism and military expenditure but not between terrorism and foreign capital inflow. Vector error correction model (VECM) is used to study both the short-run and the long-run relationships between the variables.

The authors provide a way to represent spatial and temporal equilibria in terms of error correction models in a panel setting. This requires potentially two different processes for spatial or network dynamics, both of which can be expressed in terms of spatial weights matrices. The first captures strong cross-sectional dependence, so that a spatial difference, suitably defined, is weakly cross-section dependent (granular) but can be non-stationary. The second is a conventional weights matrix that captures short-run spatio-temporal dynamics as stationary and granular processes. In large samples, cross-section averages serve the first purpose and the authors propose the mean group, common correlated effects estimator together with multiple testing of cross-correlations to provide the short-run spatial weights. The authors apply this model to the 324 local authorities of England, and show that our approach is useful for modeling weak and strong cross-section dependence, together with partial adjustments to two long-run equilibrium relationships and short-run spatio-temporal dynamics. This exercise provides new insights on the (spatial) long-run relationship between house prices and income in the UK.

This paper compares the out-of-sample forecasting performance of three long-memory volatility models (i.e., fractionally integrated (FI), break and regime switching) against three short-memory models (i.e., GARCH, GJR and volatility component). Using S&P 500 returns, we find that structural break models produced the best out-of-sample forecasts, if future volatility breaks are known. Without knowing the future breaks, GJR models produced the best short-horizon forecasts and FI models dominated for volatility forecasts of 10 days and beyond. The results suggest that S&P 500 volatility is non-stationary at least in some time periods. Controlling for extreme events (e.g., the 1987 crash) significantly improved forecasting performance.