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The real estate markets in Asia have attracted significant investor attention as they have grown rapidly in recent years. Both local and foreign investors continue to display a strong appetite for Asian real estate investment projects. Given the different characteristics of listed real estate stocks, the purpose of this paper is to focus on the causal relations between the financial variables of these stocks. This financial analysis can help investors to understand the characteristics of listed real estate companies, provide implications for optimal asset allocation decisions, and also increase the predictability of portfolio returns.

In this research, the paper investigates the contemporaneous and causal relations between stock returns, trading volume and volatility in a domestic market context and between different national markets for listed real estate companies in seven Asian economies.

The paper finds that there are positive contemporaneous relations between trading volume and both returns and absolute returns. When the paper examines the causal relations between the financial variables, the evidence implies that current trading volume helps to explain the returns indirectly by leading return volatility; however, trading volume does not help to explain future returns directly. Extending the causality test to international markets, the listed real estate portfolios of the four Southeast Asian countries are found to be more closely correlated than those of the other three countries studied here. Among the four Southeast Asian countries, Singapore, the only developed country, is found to play an influential role, its current financial variables having predictive power for the other countries.

This research provides global investors with a better understanding of the Asian listed real estate market, showing that trading volume contains important information regarding returns, that the characteristics of listed real estate companies are closer to those of the financial market than those of the real estate markets, and that the markets of the major economies have extensive influence over the smaller markets. Moreover, given the scarcity of research on the performance of Asian listed real estate companies themselves, this study improves the completeness of the academic literature.

Housing market is predominantly driven by supply and demand, and the measurement of housing supply plays a crucial role in understanding market dynamics. One such measure is the number of building permits (BPs) issued. Despite the importance of BPs as an economic indicator, direct links have yet to be drawn between BP and housing value index (HVI). The purpose of this paper is to establish links between HVI and BP.

Trials were conducted using data at the national, state and metropolitan statistical area (MSA) levels. For each trial, the Granger causality test was used first to identify causal relationships between HVI and BP. Subsequently, the vector autoregression model was implemented in an attempt to observe impulse–response relationships and to create a forecast for HVI.

Bidirectional causal relationships were observed between HVI and BP at the national, state and MSA levels. The number of issued BPs proves to be an indicator for HVI. Impulse response functions indicate that HVI responds negatively to an increase in BP in the short term of 4–7 months but positively to an increase in BP with a lag of 10–12 months.

To the best of the authors’ knowledge, this paper is the first in the body of knowledge that establishes the number of issued BPs as an indicator for housing value. The results drawn using impulse–response function are also novel and had not been observed in previous studies.

Using the notions of unit root, cointegration theory and Granger‐Akaike’s synthesis of modelling strategy, this paper examines the nature of stationarities, cointegration properties and Granger causal relationship between domestic savings and aid based on a sample of 27 developing countries. The KPSS unit root test results indicate that variables of interest in a trivariate vector autoregressive system such as aid inflows, domestic savings and income exhibit a dissimilar trend in the majority of countries, with the exceptions of Bolivia and Korea. The cointegration test results based on the Johansen and Juselius testing procedure found evidence of cointegration among the variables, domestic savings, aid and income in Bolivia and Korea. However, the presence and direction of causality between aid inflows and domestic savings are mixed across countries. Whilst the findings are indicative of a causal independence in a majority of the cases, little support is attached to either Griffin’s dependency hypothesis or Papaneck’s reverse causality hypothesis.

The purpose of this paper is to investigate dynamics of causal interactions and financial risk contagion among BRICS stock markets under rare events.

Two methods are adopted: The new causal inference technique, namely, the Liang causality analysis based on information flow theory and the dynamic causal index (DCI) are used to measure the financial risk contagion.

The causal relationships among the BRICS stock markets estimated by the Liang causality analysis are significantly stronger in the mid-periods of rare events than in the pre- and post-periods. Moreover, different rare events have heterogeneous effects on the causal relationships. Notably, under rare events, there is almost no significant Liang's causality between the Chinese and other four stock markets, except for a few moments, indicating that the former can provide a relatively safe haven within the BRICS. According to the DCIs, the causal linkages have significantly increased during rare events, implying that their connectivity becomes stronger under extreme conditions.

The obtained results not only provide important implications for investors to reasonably allocate regional financial assets, but also yield some suggestions for policymakers and financial regulators in effective supervision, especially in extreme environments.

This paper uses the Liang causality analysis to construct the causal networks among BRICS stock indices and characterize their causal linkages. Furthermore, the DCI derived from the causal networks is applied to measure the financial risk contagion of the BRICS countries under three rare events.

House price-to-rent (P-t-R) ratios are among the most widely used measures of housing market conditions. Given the theoretical and apparent bidirectional, causal relationships and imbalances between the housing market, broader economy and financial market determinants, it is important to understand the relationship between macro- and micro-economic characteristics in relation to the P-t-R ratio to enhance the understanding of housing market dynamics. This paper studies the joint dynamics and persistence of house prices and rents and examines the temporal interactions of the P-t-R ratio and economic and financial determinants.

The authors examine the lead–lag relationships between the P-t-R ratios and a spectrum of macroeconomic variables using cointegration and causality methods, initially at the aggregate position and also across housing types within the Northern Ireland housing market to establish whether there are subtle differences in how various housing segments react to changes in economic activity and market fundamentals.

The findings reveal price switching dynamics and some very distinct long- and short-run relationships between macroeconomic and financial indicators and the P-t-R ratios across the various housing segments. The results exhibit monetary supply, foreign exchange markets and the stock market to be important drivers of the P-t-R ratio, with P-t-R movements seemingly tied, or are in tandem, with the overall economy, particularly with the construction sector.

The study shows that the P-t-R ratio can be used as an early measure for establishing the effects of macroprudential policy changes and how these may manifest across housing tiers and quality, which can further act as a signal for preventing or at least mitigating future irrational price cyclicity. These insights serve to inform housing and economic policy and macroprudential policy design, principally within lending policy and the effect of regulatory interventions and further enhance the understanding of the P-t-R ratio on housing market structure and dynamics.

This study is the first in the housing literature that examines the causal relationships between the P-t-R ratio and macroeconomic activity at the sub-market level. It investigates whether and how money supply, inflation, foreign exchange markets, general economic productivity and other important macroeconomic factors interact with the pricing of different property types over time.

The purpose of this study is to investigate the Granger causal link between the stock market index and housing prices in terms of apartment and villa prices.

Monthly data from September 2005 to October 2013 on apartment prices, villa prices, the stock market index, mortgage rates and the consumer price index were used. Statistical methods were applied to explore the long-run co-integration and Granger causal link between the stock market index and apartment and villa prices in Sweden.

The results indicate that the stock market index and housing prices are co-integrated and that a long-run equilibrium relationship exists between them. According to the Granger causality tests, bidirectional relationships exist between the stock market index and apartment and villa prices, respectively, supporting the wealth and credit-price effects. Moreover, variations in apartment and villa prices are primarily caused by endogenous shocks.

To the authors’ best knowledge, this study represents a first analysis of the causal nexus between the stock market and the housing market in terms of apartment and villa prices in the Swedish context using a vector error-correction model to analyze monthly data.

The purpose of this study is to attempt to analyze Granger causality in the frequency domain framework between producers' prices measured by wholesale price index (WPI) and consumers' prices measured by consumer price index (CPI) in the context of India.

Analysis was carried out in the framework of time series and for analysis Johansen and Juselius's maximum likelihood approach for cointegration was applied after confirming that variables are integrated of order one, i.e. I(1) through the Lee and Strazicich unit root test. Finally, Granger causality was tested in the frequency domain by utilizing a recently developed approach of Lemmens et al. over the period January 1957‐February 2009.

The paper finds that CPI Granger cause WPI at a lower, intermediate as well as higher levels of frequency, reflecting very long‐run, intermediate as well as short‐run cycles. By contrast WPI Granger cause CPI at 5 percent level of significance was found at intermediate frequencies, reflecting significant intermediate cycles.

The study reveals that CPI is a leading indicator of producers' prices and inflation (i.e. WPI). This gives an indication that Indian policy analysts ought to control for factors affecting CPI in order to have control on WPI since WPI is used for making various macroeconomic indicators in real terms.

The main contribution of the paper is to show the evidence of bidirectional causality between WPI and CPI. Furthermore, use of a recent approach developed by Lemmens et al. for Granger causality in the frequency domain in this study is also relatively new. To the best of the author's knowledge there is no such study in this area either for developed or developing economy to date.

The purpose of this paper is to bridge the gap between the existing theoretical and empirical studies by examining the asymmetric return–volume relationship. Indeed, the authors aim to shed light on the return–volume linkages for French-listed small and medium-sized enterprises (SMEs) compared to blue chips across different market regimes.

This study includes both large capitalizations included in the CAC 40 index and listed SMEs included in the Euronext Growth All Share index. The Markov-switching (MS) approach is applied to understand the asymmetric relationship between trading volume and stock returns. The study investigates also the causal impact between stock returns and trading volume using regime-dependent Granger causality tests.

Asymmetric contemporaneous and lagged relationships between stock returns and trading volume are found for both large capitalizations and listed SMEs. However, the causality investigation reveals some differences between large capitalizations and SMEs. Indeed, causal relationships depend on market conditions and the size of the market.

This paper explains the asymmetric return–volume relationship for both large capitalizations and listed SMEs by incorporating several psychological biases, such as the disposition effect, investor overconfidence and self-attribution bias. Future research needs to deepen the analysis especially for SMEs as most of the literature focuses on large capitalizations.

This empirical study has fundamental implications for portfolio management. The findings provide a deeper understanding of how trading activity impact current returns and vice versa. The authors’ results constitute an important input to build and control trading strategies.

This paper fills the literature gap on the asymmetric return–volume relationship across different regimes. To the best of the authors’ knowledge, the present study is the first empirical attempt to test the asymmetric return–volume relationship for listed SMEs by using an accurate MS framework.

The purpose of this paper is to analyse the effects of a declining birth rate and an increasing old age‐population ratio on Ireland's economic output.

This paper utilises data on the birth rate, old‐age population ratio, economic output and labour effort of the Irish economy to estimate a vector‐autoregressive model. The results of this model are then analysed to test for the presence of Granger causality among these variables. In doing so it is possible to assess whether there are statistically significant causal relationships existing among these factors. Subsequently, impulse response functions are derived from this model in order to assess the magnitude of the causal relationships.

The results suggest that declining fertility rates and increases in the old‐age dependency ratio have a significant impact on labour effort and economic output. Labour effort is also found to explain variation in the fertility rate and economic output. Economic output is found to effect labour effort and the fertility rate.

The results derived in this paper raise interesting policy implications. It is evident that Ireland's declining birth rate and increasing old‐age population ratio are creating a demographic situation which will have implications for future economic growth. Policies need to be put in place to mitigate the negative effects these factors will have on Irish growth.

This paper adopts modern econometric techniques to assess the causal relationships which exist between the demographic and economic factors considered. These have not previously been applied to the Irish situation. In doing this, this paper provides an important insight into the changing dynamics of the Irish economy.

The purpose of this paper is to investigate the interrelationships amongst the sector‐specific indices of the Qatar Exchange (QE) (i.e. Banking and Financial Institutions (BFI), Industrial (IND), Insurance (INS), and Services (SER)). More specifically, three key issues are explored in this study. First, the long‐run relationships amongst the sectors. Second, the short‐run causal relationships amongst them; and third, the relative degree of endogeneity/exogeneity of each sector.

To address the issues of interest, the author employs the econometric analyses of Johansen's multivariate cointegration, Granger's causality, and generalized forecast error variance decomposition. This battery of techniques gives the opportunity to examine the nature of both long‐ and short‐run intersectoral relationships in the QE. To augment the robustness of the empirical analysis, daily as well as weekly closing stock price indices for the four sectors of the Qatar Exchange are used, spanning the period from January 2, 2008 up to April 7, 2011.

Based on daily and weekly data, the results of Johansen's multivariate cointegration analysis suggest that the four sector indices of the QE share a long‐term equilibrium relationship. The Granger's causality analysis based on daily and weekly datasets provides clear evidence that the BFI sector seems to be a significant causal factor in regard to the price predictability of the remaining sectors in the short run, and that the SER sector surprisingly seems to have the least influential role. Finally, the results of the generalized forecast error variance decomposition analysis using daily data show that the IND and BFI appear to be the most exogenous sectors, whereas the SER and INS are the most endogenous ones. The results based on weekly data confirm the relative exogeneity of the BFI sector and the relative endogeneity of the SER sector.

The findings of this study hold practical implications for individual and institutional investors alike. The potential gains derived from cross‐sector diversification could be rather limited, given the significant degree of interrelationships found amongst the sector indices of the QE. Moreover, the composition of domestic portfolios based on sector‐level investments should be revisited, particularly after major events. The findings also bring some important insights for policymakers. Given the influential role played by the BFI sector in the Qatari economy, policymakers should design appropriate strategies that curb the spread of unanticipated shocks originating from this sector to its counterparts. Besides, due to the considerable degree of endogeneity of the SER sector, it is essential for policymakers to set up precautionary regulations, with the aim of minimizing its vulnerability to common shocks in turbulent times.

Building upon the extant research and focusing on a relatively unexplored market, the paper represents a pioneer attempt to provide empirical evidence on the interdependence structure amongst the sector‐specific indices of the Qatar Exchange.