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The purpose of this study is to investigate the interactive relationships between oil price shocks and the Nigeria stock market.

The paper applied the multivariate vector auto‐regression that employed the generalized impulse response function and the forecast variance decomposition error.

Empirical evidence reveals that stock market returns exhibit insignificant positive response to oil price shocks but reverts to negative effects after a period of time depending on the nature of the oil price shocks. The results are similar even with the inclusion of other variables. Also, the asymmetric effect of oil price shocks on the Nigerian stock returns indices is not supported by statistical evidences.

This is the first study to examine the dynamic linkages between stock market behaviour and oil price shocks in Nigeria.

This paper aims to attempt to capture the intertemporal/time-varying risk–return relationship in the Brazil, Russia, India and China (BRIC) equity markets after the global financial crisis (2007-2009), i.e. during a relative calm period. There has been a significant increase in advanced economies’ equity allocations to the emerging markets ever since the financial crisis. So, the present study is an attempt to account for the said relationship, thereby justifying investments made by the international investors.

The study uses non-linear models comprising asymmetric component generalised autoregressive conditional heteroskedastic model in mean (CGARCH-M) (1,1) model, generalised impulse response functions under vector autoregressive framework and Markov regime switching in mean and standard deviation model. The span of data ranges from 1 July 2009 to 31 December 2014.

The ACGARCH-M (1,1) model reports a positive and significant risk-return relationship in the Russian and Chinese equity markets only. There is leverage and volatility feedback effect in the Russian market because falling returns further increase conditional variance making the investors to expect a risk premium in the expected returns. The impulse responses indicate that for all of the BRIC markets, the ex-ante returns respond positively to a shock in the long-term risk component, whereas the response is negative to a shock in the short-term risk component. Finally, the Markov regime switching model confirms the existence of two regimes in all of the BRIC markets, namely, Bull and Bear regimes. Both the regimes exhibit negative relationship between risk and return.

It is an imperative task to comprehend the relationship shared between risk and returns for an investor. The investors in the emerging economies should understand the risk-return dynamics well ahead of time so that the returns justify the investments made under riskier environment.

The present study contributes to the literature in three senses. First, the data relate to a period especially after the global financial crisis (2007-2009). Second, the study has used a relatively newer version of GARCH based model [ACGARCH-M (1,1) model], generalised impulse response functions and Markov regime switching model to account for the relationship between risk and return. Finally, the study provides an insightful understanding of the risk–return relationship in the most promising emerging markets group “BRIC nations”, making the study first of its kind in all the perspectives.

The purpose of this paper is to examine how global liquidity affects international housing prices. The data sample covers 35 economies from 2000Q1 to 2017Q4.

The existing papers seldom investigated whether the impacts of global liquidity on housing prices display differences between advanced and developing economies. Cesa-Bianchi et al. (2015) is an exceptional study in that they focused on the impulse response of house price volatility to global liquidity shocks but did not examine the long-run equilibrium relationship. To fill the gap in the existing research, this paper used panel cointegration of Pedroni (2000, 2004) to estimate the long-run linkage between global liquidity and housing prices in both advanced and developing economies, and generalized impulse response function (GIRF) and generalized variance decomposition (GVDC) were also applied to capture the relative strengths and contribution of global liquidity shock on house price volatility.

First, the global liquidity elasticity of housing prices is 0.0679 in developing economies, and 0.0454 in advanced economies, implying that the positive effect of global liquidity on housing prices is higher in developing economies. Next, the results of generalized impulse response indicate that the innovation of global liquidity can significantly and positively impact housing prices only in developing economies and the duration is two quarters. Third, in light of the long-run portions of the global liquidity shock on house price volatility in individual economies, the two highest portions are 28.51% in the USA and 20.04% in the UK, while there are low portions, less than 10%, in most of the European economies. Moreover, comparing the long-run contributions of global liquidity and other variables shock on house price volatility, the contribution of the global liquidity shock ranks the highest or second highest in 21 out of 35 economies, confirming that it played a more important role than other economic variables in explaining house price volatility for most economies.

Compared with the related literature, the contributions of this paper are as follows. First, except for Cesa-Bianchi et al. (2015), the existing papers seldom investigated whether the impacts of global liquidity on housing prices display differences between advanced and developing economies. Hence, the study adopted a wider data sample, including 7 developing economies and 28 advanced economies, to examine the differences in the impact of global liquidity on housing prices between advanced and developing economies. Second, most of the relative literature calculated global liquidity by applying a monetary aggregate, such as M2 or M3, while Cesa-Bianchi et al. (2015) argued that global liquidity being measured by the international supply of credit is intuitively connected to housing prices. This paper follows the argument of Cesa-Bianchi et al. (2015) to use the international supply of credit as the measure of global liquidity, and both the long-run effects and the short-run relative strengths of global liquidity on housing prices are analyzed. Hence, this paper uses not only the GIRF to discuss the short-run relative strengths, as with Cesa-Bianchi et al. (2015) but also the panel cointegration of Pedroni (2000, 2004) to identify the long-run linkage between global liquidity and housing prices. Moreover, GVDC was used to estimate the contribution of a global liquidity shock on house price volatility in individual economies, which can confirm that global liquidity innovations are a very important factor in explaining house price volatility in most countries.

This chapter analyzes the exchange rate pass-through (ERPT) into different prices for 12 euro area (EA) countries. We provide new up-to-date estimates of ERPT by paying attention to either the time-series properties of data and variables endogeneity. Using VECM framework, we examine the pass-through at different stages along the distribution chain, that is, import prices, producer prices, and consumer prices. When carrying out impulse response functions analysis, we find a higher pass-through to import prices with a complete pass-through (after one year) detected for roughly half of EA countries. These estimates are relatively large compared to single-equation literature. We denote that the magnitude of the pass-through of exchange rate shocks declines along the distribution chain of pricing, with the modest effect recorded for consumer prices. When assessing for the determinant of cross-country differences in the ERPT, we find that inflation level, inflation volatility, and exchange rate persistence are the main macroeconomic factors influencing the pass-through almost along the pricing chain. Thereafter, we have tested for the decline of the response of consumer prices across EA countries. According to multivariate time-series Chow test, the stability of ERPT coefficients was rejected, and the impulse responses of consumer prices over 1990–2010 provide an evidence of general decline in rates of pass-through in most of the EA countries. Finally, using the historical decompositions, our results reveal that external factors, that is, exchange rate and import prices shocks, have had important inflationary impacts on inflation since 1999 compared to the pre-EMU period.

This paper investigates the dynamic relationship between the trade-weighted dollar exchange rates and the oil prices in the world market. Monthly data during 1980–2017 are used for this purpose.

The symmetric and asymmetric generalized impulse response functions are estimated for these important economic indicators.

The empirical findings show that if the dollar rate increases (i.e. the dollar depreciates), the oil price will increase. The reverse relationship is also supported empirically meaning that an increase in the oil price will results in a significant depreciation of the dollar rate. Based on the asymmetric impulses responses, it can also be claimed that the negative interaction is only significant for the positive changes and not for the negative ones. Thus, the underlying variables are negatively interrelated only for the positive shocks since a negative shock from any variable does not seem to have any significant impact on the other variable. These results have implications for cross hedging of price risk.

To the best knowledge, this is the first attempt to investigate the relationship between the dollar weighted exchange rate and the oil pieces via the asymmetric impulse response functions. Both of these variables and their interactions are very important for investors as well as policy makers worldwide.

The purpose of this paper is to examine the short‐ and long‐run causal relationship between energy consumption and gross domestic product (GDP) of six emerging economies of Asia. The importance of identifying the direction of causality emanates from its relevance in national policy‐making issues regarding energy conservation.

This paper employs co‐integration and vector error correction modeling along with generalized impulse response functions and varience decomposition tests to check the robustness of the findings.

The empirical results show that there exists unidirectional short‐ and long‐run causality running from energy consumption to GDP for China, uni‐directional short‐run causality from output to energy consumption for India, whilst bi‐directional short‐run causality for Thailand. Neutrality between energy consumption and income is found for Indonesia, Malaysia, and Philippines. Both the generalized variance decompositions and impulse response functions confirm the direction of causality.

These findings have important policy implications for the countries concerned. The results suggest that while India may directly initiate energy conservation measures, China and Thailand may opt for a balanced combination of alternative polices.

Many economists and social scientists are claiming that the increased demand for energy from developing countries like China and India is one of the major reasons for the energy price hikes in recent times. In this backdrop, it is justified to search causal relationship between energy consumption and national output (GDP) of some developing countries from Asia. Since the traditional bivariate approach suffers from omitted variable problems, this paper employs a trivariate demand side approach consisting of energy consumption, income and prices.

The purpose of this paper is to investigate whether the macroeconomic effects of government spending shocks vary with the degree of macroeconomic uncertainty.

The authors use quarterly US data from 1960 to 2017 and employ the Self-Exciting Interacted VAR (SEIVAR) to compute nonlinear generalized impulse response functions (GIRFs) to an orthogonalized government spending shock during tranquil and in uncertain times. The parsimonious design of the SEIVAR enables us to focus on extreme deciles of the uncertainty distribution and to control for the financing side of the government budget, monetary policy, financial frictions and consumer confidence.

Fiscal spending has positive output effects in tranquil times, but is contractionary during times of heightened macroeconomic uncertainty. The results indicate an important role of the endogenous response of macroeconomic uncertainty. Investigating different government spending purposes, only increases in research and development expenditures reduce uncertainty and boost output during uncertain times.

The authors contribute to the literature in using a method which allows to control for a large set of confounding factors and accounts for the uncertainty response.

The purpose of this paper is to offer a review of the trade policy in the UAE. It also investigates the dynamic interaction between trade openness and GDP per capita in this emerging economy.

The asymmetric generalized impulse response functions and the asymmetric causality tests developed by Hatemi-J are used.

The results from asymmetric generalized impulse response functions indicate that a positive permanent shock in the trade openness results in a significant positive response in the cumulative sum of the positive component of the GDP per capita. Such a response is not found for the negative shocks in the trade openness. Furthermore, neither a positive nor a negative shock in the GPD per capita results in any significant response in the trade openness. These empirical findings are also supported by the implemented asymmetric causality tests.

This is the first attempt that investigates the impact of trade openness on economic performance in the UAE. Unlike previous literature on the topic, this paper allows for asymmetric impacts in the empirical model.

The purpose of this paper is to demonstrate importance of usage of sector indices which provides insight for sector specific investment strategies and direction for suitable policy formulation for the Indian industry. It investigates long run, short run and causality relationships between eight identified sector indices and Sensex for the post subprime period.

The study uses Vector Error Correction Model (VECM) for econometric analysis. It employs Generalized Impulse Response and Variance Decomposition analysis for developed multivariate framework in order to provide information about precise interplay of the sector indices.

Long-term relationships between sector indices were determined by the usage of VECM indicating minimal benefits from diversifying investments to different sectors. Limited lead – lag short run relationships between sector indices were observed. Banking index played a predominant and integrating role in moving other indices. During this period of recovery; most sectors were protected and provided marginally better returns due to robust Banking policy. Realty and Metal were other significant drivers influencing remaining sectors contemporaneously. The study for the post subprime crisis period helps to understand the importance and behavior of interrelated sector indices and Sensex in the dynamic economic environment.

The study clearly provides direction for sector specific investment strategies and policy formulation.

The study highlights utility and importance of usage of sector indices. No study using sector indices for the Indian economy have been done earlier employing VAR for the post subprime crisis period.