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The purpose of this paper is to provide an analysis of the relationship between expected risk premia on property stocks and some major macroeconomic risk factors as reflected in the general business and financial conditions

Employs a three‐step estimation strategy (principal component analysis, GARCH (1,1) and GMM) to model the macroeconomic risk variables (GDP growth, INDP growth, unexpected inflation, money supply, interest rate and exchange rate) and relate them to the first and second moments on property stock excess returns of four major markets, namely, Singapore, Hong Kong, Japan and the UK. Macroeconomic risk is measured by the conditional volatility of macroeconomic variables.

The expected risk premia and the conditional volatilities of the risk premia on property stocks are time‐varying and dynamically linked to the conditional volatilities of the macroeconomic risk factors. However there are some disparities in the significance, as well as direction of impact in the macroeconomic risk factors across the property stock markets. Consequently there are opportunities for risk diversification in international property stock markets.

Results help international investors and portfolio managers deepen their understanding of the risk‐return relationship, pricing of macroeconomic risk as well as diversification implications in major Asia‐Pacific and UK property stock markets. Additionally, policy makers may play a role in influencing the expected risk premia and volatility on property stock markets through the use of macroeconomic policy.

This research aims to investigate whether and to what extent the co-movements of cross-country business cycles, cross-country stock market cycles and cross-country real estate market cycles are linked across G7 from February 1990 to June 2014.

The empirical approaches include correlation analysis on Hodrick–Prescott (HP) cycles, HP cycle return spillovers effects using Diebold and Yilmaz’s (2012) spillover index methodology, as well as Croux et al.’s (2001) dynamic correlation and cohesion methodology.

There are fairly strong cycle-return spillover effects between the cross-country business cycles, cross-country stock market cycles and cross-country real estate market cycles. The interactions among the cross-country business cycles, cross-country stock market cycles and cross-country real estate market cycles in G7 are less positively pronounced or exhibit counter-cyclical behavior at the traditional business cycle (medium-term) frequency band when “pure” stock market cycles are considered.

The research is subject to the usual limitations concerning empirical research.

This study finds that real estate is an important factor in influencing the degree and behavior of the relationship between cross-country business cycles and cross-country stock market cycles in G7. It provides important empirical insights for portfolio investors to understand and forecast the differential benefits and pitfalls of portfolio diversification in the long-, medium- and short-cycle horizons, as well as for research studying the linkages between the real economy and financial sectors.

In adding to the existing body of knowledge concerning economic globalization and financial market interdependence, this study evaluates the linkages between business cycles, stock market cycles and public real estate market cycles cross G7 and adds to the academic real estate literature. Because public real estate market is a subset of stock market, our approach is to use an original stock market index, as well as a “pure” stock market index (with the influence of real estate market removed) to offer additional empirical insights from two key complementary perspectives.

It has been said that oil price shocks affect stock market returns. However, empirical studies remain inconclusive regarding the nexus between oil price shocks and stock market returns. Consequently, the purpose of this study is to investigate the asymmetric impact of oil price shocks on stock returns in Nigeria.

A two-stage Markov regime-switching approach is used to examine the asymmetric effects of three different structural oil shocks on stock returns. The oil shocks, which include oil supply shock, aggregate demand shock and oil-specific demand shock, are derived using structural vector autoregressive. Monthly data that spans the period between January 1990 and December 2018 are deployed for estimation.

The linear estimation results show that only oil demand shock negatively and significantly affects the stock market returns. The Markov-switching regime results reveal that oil supply shock has a significant positive impact on the stock returns in a low-volatility state, whereas oil-specific demand shock negatively impacts the stock returns in a high-volatility state.

There is a need for policymakers and investors to take cognizance of not only the positive outcomes of a relatively stable state of oil price but also the negative consequences of a high-volatility state when formulating policy and making investment decisions, respectively.

This study differs from other similar studies in Nigeria that have examined the asymmetric relationship between oil price shocks and stock market return by using a two-stage Markov regime-switching approach. To the best of the authors’ knowledge, this is the first attempt at using this methodology.

The upsurge of globalization has made investors cautious toward investing decisions, and, resultantly, sophisticated techniques of forecasting and analyzing the stock markets have emerged. Particularly, this trend has gained momentum in emerging economies. One such trend is to overcome the investing risks associated with formation of rational bubbles. Bubbles are formed when asset prices inflate to a very high level temporarily, and they ultimately burst. Investors may take advantage of this short-lived phenomenon and gain high returns, but may also suffer as the entire investing value declines when the bubble bursts. The purpose of this paper is to identify rational bubbles in the emerging capital markets of South Asian region.

The monthly data have been obtained from June 1997 to February 2018 for Pakistan, Bombay, Dhaka and Colombo stock markets, and supremum-Augmented Dicky Fuller test developed by Phillips and Yu (2011) has been utilized to identify the rational bubbles.

The results revealed the presence of rational bubbles in South Asian equity markets. The current study is of significant nature for the facilitation of investors in future-making investing decisions concerning with the formation of rational bubbles.

Several studies have been conducted on stock markets of developed regions. Specific bubble episodes, which occurred previously, have helped the researchers and investors in gaining plenty of insights. A lot of studies have been conducted on the SAARC region as well. But they have used the conventional unit root test for bubble identification and not used as extensive data as, in this study, have been taken. This research is aimed to study equity prices of the four stock markets to establish the fact that if rational bubbles exist in the index, they are reflected in the returns or not.

This paper aims to examine the impact of oil price uncertainty on the stock market returns of ten oil importing and exporting countries in the Middle East and North Africa (MENA) region. The sample contains both oil importing and oil exporting countries that depend heavily on oil production and exports.

This paper intuitively applies the generalized autoregressive conditional heteroskedasticity (GARCH)-in-mean vector autoregression (VAR) model using weekly data over the period January 2001-February 2014.

The findings indicate that oil uncertainty matters in the determination of real stock returns. There is a negative and significant relationship between oil price uncertainty and real stock returns in all countries in the sample. The influence of oil price risk is more serious in those economies that depend heavily on oil revenues to grow.

The findings have important implications. For instance, managers should be aware of the linkages between oil price uncertainty and equity returns when they use oil to hedge and diversify equities, particularly in economies where oil is important for economic growth. The policymakers in oil importing countries should encourage companies to improve efficiency in the usage of energy and to resort to alternative sources to avoid fluctuations in earnings and equity prices. In the countries that heavily depend on oil efforts should focus on diversifying the domestic economy away from oil to protect against oil price fluctuations.

To the best of our knowledge, this is the first attempt to study the influence of oil price uncertainty in the MENA region. The sample contains both oil importing and oil exporting countries that depend heavily on oil production and exports. The empirical findings of the paper have valuable policy implications for investors, market participants and policymakers.

Are share markets too volatile? While it is difficult to ignore share market volatility it is important to determine whether volatility is excessive. This paper replicates the Shiller (1981) test as well as applying standard time series analysis to annual Australian stock market data for the period 1883 to 1999. While Shiller’s test suggests the possibility of excess volatility, time series analysis identifies a long‐run relationship between share market value and dividends, consistent with the share market reverting to its fundamental discounted cash flow value over time.

Using a data set comprising 16 European office markets provided by the DTZ Research Institute from Q1 2003 to Q4 2013, the purpose of this paper is to measure the strength of the unconditional transmission of volatility in the returns to direct property between 16 European office markets with the objective of determining the degree of unconditional spillover between markets.

To examine volatility spillovers across the 16 office markets, the authors adopted the generalized VAR methodology, variance decomposition and the generalized spillover index of Diebold and Yilmaz (2012) by measuring cross-office market volatility transmission in asset pricing through estimates of several “volatility spillover indices.”

Volatility spillovers are important and time-varying across the leading office markets, with cross-market volatility interaction being bi-directional and of relative endogenous nature for many markets. The London office market is the “volatility leader” and has exerted significant net volatility influence on the other markets. Additionally, the volatility spillovers between business cycle fluctuations and asset market cycle volatilities are linked across some European economies.

Evidence of co-integration among the domestic volatility spillover cycles implies the presence of unobserved common shocks and might not be good news for international investors who pursue diversification strategies in European office real estate markets.

No previous study has addressed formally the measurement and assessment of the nature and intensity of volatility spillovers across direct office markets on such a broad range of European office markets. The relevance of the topic has been even increasing over the previous years as more and more investors seek for flexibility and participation in the investment process and asset management.

This paper seeks to reconsider the Euler equation of the Consumption Capital Asset Pricing Model (CCAPM), to derive a regression‐based model to test it, and to present evidence that the model is consistent with reasonable values for the coefficient of relative risk aversion (CRRA). This runs contrary to the findings of the literature on the equity premium puzzle, but is in agreement with the literature that estimates the CRRA for the purpose of computing the social discount rate, and is in line with the research on labor supply. Tests based on General Method of Moments (GMM) for the same sample produce results that are extremely disparate and unstable. The paper aims to check and find support for the robustness of the regression‐based tests. Habit formation models are also to be evaluated with regression‐based and GMM tests. However, the validity of the regression‐based models depends critically on their functional forms.

The paper presents empirical evidence that the conventional use of GMM fails because of four pathological features of GMM that are referred to under the general caption of “weak identification”. In addition to GMM, the paper employs linear regression analysis to test the CCAPM, and it is found that the regression residuals follow well‐behaved distributional properties, making valid all statistical inferences, while GMM estimates are highly unstable.

Four unexpected findings are reported. The first is that the regression‐based models are consistent with reasonable values for the CRRA, i.e. estimates that are below 4. The second is that the regression‐based tests are robust, while the GMM‐based tests are not. The third is that regression‐based tests with habit formation depend crucially on the specification of the model. The fourth is that there is evidence that market stock returns are sensitive to both consumption and dividends. The author calls the latter “extra sensitivity of market stock returns”, and it is described as a new puzzle.

The regression‐based models of the CCAPM Euler equation are novel. The comparison between GMM and regression‐based models for the same sample is original. The regression‐based models with habit formation are new. The equity premium puzzle disappears because the estimates of the CRRA are reasonable. But another puzzle is documented, which is the “extra sensitivity of market stock returns” to consumption and dividends together.

The purpose of this paper is to examine the macroeconomic determinants of stock market development in South Africa during the period 1975–2015. Specifically, it examines the impact of banking sector development, economic growth, inflation rate, real interest rate and trade openness on the development of the South African stock market.

The author employs autoregressive distributed lag bounds testing procedure that allows the author to empirically investigate both the short- and long-run relationships between the stock market development and its determinants in the context of South Africa. In addition, the author also conducts a sensitivity analysis by accounting for the presence of structural breaks in the underlying series to check for the robustness of the estimation.

This paper confirms the findings by other studies that banking sector development and economic growth promote stock market development, while inflation rate and real interest rate inhibit stock market development. In addition, this paper finds an interesting result in the fact that trade openness has a negative impact on stock market development, which is different from the findings of many other studies.

Currently, while the theoretical and empirical literature presents diverse views on the relationship between each determinant and stock market development, no study has focussed on the South African stock market. Given the significant role that the South African stock market plays in Africa as measured by its market capitalisation and market capitalisation ratio, there is a need for a better understanding of the macroeconomic factors influencing its development.

The purpose of this paper is to provide an estimate of the demand and supply in the housing market in Colombia in a period of high real estate valuation (2005-2016). On the demand side, it evaluates the impact of new housing prices, unemployment, stock market returns, real wages in the retail sector, remittances and mortgage rates. On the supply side, it estimates the influence of the price of new housing, construction costs, time deposit (TD) and mortgage rates. Real estate valuation was analyzed considering foreigners migration and land prices evolution.

Ordinary least squares (OLS) was used to estimate housing area with the semilog regression model and also to construct price models. OLS was also used in price models. Since quantities depend on prices and vice versa, a two-stage least squares (2SLS) was implemented.

Rising prices in new homes have an “elastic” effect on both demand and even higher effect on supply. Likewise, the real wage index for the retail sector has an elastic effect. On the other hand, the response to interest rates is negative, but statistically significant only on the supply side. Furthermore, the inflow of remittances is “inelastic” and statistically insignificant.

Housing can sometimes be a Giffen good, this result challenges the traditional neoclassical model, but it can be explained by investment reasons and “bubble” behavior in the housing market. One last influence is the difference between “temporary” and “permanent” migrations. The latter has a statistically significant and perfectly inelastic effect on the price of new homes.