Search results

The main thrust of the present study is to look into the trading patterns of behavior and investment performance exhibited by individual and institutional investor categories in the Qatar Exchange (QE). The paper aims to discuss these issues.

The present study uses daily aggregated investment flows made separately by each investor group, as well as daily closing price observations of the QE stock composite index. The trading patterns of investor categories are examined by estimating a bivariate vector autoregressive process of order p, VAR (p). To determine whether each category performs well or poorly over the entire sample period, each investor category's cumulative returns are estimated and analyzed.

The empirical results reveal that institutional investors pursue positive feedback trading strategies, whereas individual investors tend to be negative feedback traders. Both investor categories appear to be engaged in herding behavior. Additionally, institutional investors perform well over almost the entire sample period. In contrast, individual investors' negative market timing ability dominates their overall poor performance.

The investment performance gap found between institutional investors and individual investors in the Qatari capital market may reflect a large information asymmetry in favour of the former category. Indeed, the poor performance of individual investors implies that their trading activities are generally driven by factors and considerations that are irrelevant to fundamentals. Moreover, their irrational trading decisions may play some role in the formation of asset price bubbles.

The present study makes the first attempt to provide empirical evidence on the investment patterns and performance of individual and institutional investors trading on the Qatari capital market.

The paper seeks to examine cycles and common cycles in the real estate markets of the UK, Japan, Singapore, Hong Kong and Malaysia using a combination of time domain and frequency domain methods.

The paper identifies the patterns of cyclical movement (if any) in the five public real estate markets, and searches for common cycle characteristics and patterns in international real estate markets. In addition to the time domain analyses, these empirical investigations are further empowered by a frequency domain method that includes spectral and co‐spectral analyses.

International real estate markets are characterized by cyclical behavior that exhibits phenomenal fluctuations. The markets are also pro‐cyclical; they do tend to move together. Furthermore, some differences in the patterns of the common cycles and their lead‐lag linkages are evident.

International investors would probably benefit from diversifying real estate stocks across the UK and Asian real estate markets, especially in the short and medium terms. However, the long‐term cyclical patterns across the national real estate stock markets are not sharply different, indicating that smaller diversification benefits are to be expected in the long term.

Common cycle analysis advances investors' understanding of the long‐term relationship and medium‐ and short‐term linkages across international real estate markets, thereby allowing investors and portfolio managers an opportunity to discern any contrasting cyclical patterns at all frequencies so as to assist in their portfolio decisions.

We develop a framework for measuring and monitoring business cycles in real time. Following a long tradition in macroeconometrics, inference is based on a variety of indicators of economic activity, treated as imperfect measures of an underlying index of business cycle conditions. We extend existing approaches by permitting for heterogenous lead–lag patterns of the various indicators along the business cycles. The framework is well suited for high-frequency monitoring of current economic conditions in real time – nowcasting – since inference can be conducted in the presence of mixed frequency data and irregular patterns of data availability. Our assessment of the underlying index of business cycle conditions is accurate and more timely than popular alternatives, including the Chicago Fed National Activity Index (CFNAI). A formal real-time forecasting evaluation shows that the framework produces well-calibrated probability nowcasts that resemble the consensus assessment of the Survey of Professional Forecasters.

The purpose of this paper is to investigate the volatility spectral of five major public real estate markets, namely, the USA, the UK, Japan (JP), Hong Kong (HK), and Singapore (SG), during the pre- and post-global financial crisis (GFC) periods.

First, univariate spectral analysis is concerned with discovering price cycles for the respective real estate markets. Second, bivariate cross-spectral analysis seeks to uncover whether any two real estate price series share common cycles with regard to their relative magnitudes and lead-lag patterns of the cyclical variations. Finally, to test the contagion effects, the authors estimate the exact percentage change in co-spectral density (cyclical covariance) due to high frequencies (short run) after the GFC.

The authors find that whilst none of the public real estate markets examined are spared from the crisis, the three Asian markets were less severely affected by the GFC and were accompanied by a reversal in volatility increase three years post-global financial crisis. Additionally, the public real estate markets studied have become more cyclically linked in recent years. This is particularly true at longer frequencies. Finally, these increased cyclical co-movements measure the outcomes of contagion and indicate fairly strong contagious effects between the public real estate markets examined due to the crisis.

The implication of this research is that benefits to investors from international real estate diversification may not be as great during the present time compared to previous periods because national public real estate markets have become more correlated. Nevertheless, the findings do not imply the complete absence of diversification benefits. This is because although cyclical correlations increase in the short run, many of the correlation values are still between low and moderate range, indicating that some diversification benefits may still be realized.

Given the significant market share and the highest levels of securitization in Asia-Pacific markets including JP, HK/China, and SG, this cyclical research including major public real estate markets has practical implications for ongoing international real estate investment strategies, particularly for the USA/UK and Asian portfolio managers.

This paper contributes to the limited research on the cyclical return and co-movement dynamics among major public real estate markets during financial/economic crisis in international finance. Moreover, the frequency-domain analysis conducted in this paper adds to better understanding regarding the impact of GFC on the cyclical return volatility and co-movement dynamics of major developed public real estate markets in international investing.

This study documents that high book‐to‐market (value) and low book‐to‐market (glamour) stock prices react asymmetrically to both common and firm‐specific information. Specifically, we find that value stock prices exhibit a considerably slow adjustment to both common and firm‐specific information relative to glamour stocks. The results show that this pattern of diferential price adjustment between value and glamour stocks is mainly driven by the high arbitrage risk borne by value stocks. The evidence is consistent with the arbitrage risk hypothesis, predicting that idiosyncratic risk, a major impediment to arbitrage activity, amplifies the informational loss of value stocks as a result of arbitrageurs’ (informed investors) reduced participation in value stocks because of their inability to fully hedge idiosyncratic risk.

This paper aims to accommodate the influence of both economic policy uncertainty and geopolitical risks in the relationship between oil price and exchange-rate returns in the Brazil, Russia, India, China and South Africa (BRICS) countries through an interaction term (EPGR).

The authors use continuous wavelet transform (CWT), wavelet coherence (WC) and partial wavelet coherence (PWC). First, the authors apply the CWT to examine the evolution of oil prices, EPGR and exchange rate returns. Second, the authors use WC to investigate the relationship between oil price and exchange rate returns (excluding EPGR). Third, the authors use PWC to account for EPGR’s impact on the oil exchange rate returns dynamics and explore partial correlations in the oil and exchange rate returns dynamics.

The empirical results generally show that EPGR is a key driver in the oil and exchange rate returns nexus.

The relevance of EPGR in influencing exchange rate volatility is confirmed by the findings. As a result, it is critical for government officials and foreign exchange investors to use EPGR as a leading indicator when establishing foreign exchange trading strategies and economic forecasts.

This study is the first, to the best of the authors’ knowledge, to apply a wavelet-based technique to account for EPGR in the relationship between oil and exchange rate returns in the BRICS countries.

The main objective of this study is to obtain new empirical evidence on non-synchronous trading effects through modelling the predictability of market indices.

The authors test for lead-lag effects between the Indian Nifty and Nifty Junior indices using Pesaran–Timmermann tests and Granger-Causality. Then, a simple test on overnight returns is proposed to infer whether the observed predictability is mainly attributable to non-synchronous trading or some form of inefficiency.

The evidence suggests that non-synchronous trading is a better explanation for the observed predictability in the Indian Stock Market.

The indication that non-synchronous trading effects become more pronounced in high-frequency data suggests that prior studies using daily data may underestimate the impacts of non-synchronicity.

The originality of the paper rests on various important contributions: overnight returns is looked at to infer whether predictability is more attributable to non-synchronous trading or to some form of inefficiency; the impacts of non-synchronicity are investigated in terms of lead-lag effects rather than serial correlation; and high-frequency data is used which gauges the impacts of non-synchronicity during less active parts of the trading day.

There exists a voluminous literature which examines house price diffusions across space and quality tiers. Numerous observers have pointed out that housing price dynamics can also be analysed in terms of house price diffusions across different housing sub‐markets (e.g. landed vs non‐landed housing). However, there has been no research to document these cyclical interactions. The purpose of this paper is to remedy this research gap by offering evidence of cyclical dynamics between different housing sub‐markets for the case of Malaysia.

The dynamics between house prices of non‐landed housing (condominium) and landed housing (terrace, semi‐detached and detached housing sub‐markets) are analysed. Band‐pass filters are first used to extract the cyclical components from the four house price series. Next, interactions between house price cycles are documented using cross‐correlation analysis, Granger‐causality tests and impulse response functions. Finally, the out‐of‐sample forecasting performance of the respective house price series is examined.

The author finds that condominium price cycles are exogenous in that they lead the price cycles in other housing sub‐markets by one‐ to two‐quarters. Second, condominium price cycles drive price cycles in the terrace and detached sub‐markets. Third, cycles in condominium prices are able to predict house price cycles in other sub‐markets with a high degree of precision.

This is the first known study that establishes stylised facts of lead‐lag relationships between house price cycles in different housing sub‐markets. The findings have several practical implications for housing investors who intend to optimise investment decisions on housing purchases.

Various empirical studies have demonstrated that house prices in different geographic regions have a tendency to co‐move. But these studies have focused on developed economies in the west. The purpose of this paper is to test this hypothesis in the case of three major urban areas in the rapidly developing economy of Malaysia, namely Klang Valley, Penang and Johor.

Using Pesaran et al.'s bounds testing approach to cointegration and Granger non‐causality analysis, the short‐run and long‐run dynamics of regional house prices are analysed.

First, house prices in all three regions appear to be cointegrated. Second, there is evidence of short‐run bi‐directional causality between house prices in all regions. Third, long‐run house price movements in Johor are not Granger‐caused by house prices in Klang Valley and Penang. This observation could be rationalised in the light of the argument that Johor house values may be more closely aligned with activities in the Singaporean economy, given the region's geographic proximity to Singapore.

The findings have several practical implications for housing investors who intend to optimise investment decisions on housing purchases. The understanding of the nature of regional house dynamics could also enrich the government's knowledge of how the local housing markets work and enable the design and implementation of relevant housing policies.

This is the first known study that establishes stylised facts of lead‐lag relationships for regional house prices, while also providing short‐run and long‐run estimates of regional house price interactions for Malaysia.

This paper aims to analyze systemic risk in and the effect of capital regulation on the European insurance sector. In particular, the evolution of an exposure measure (SRISK) and a contribution measure (Delta CoVaR) are analyzed from 1985 to 2016.

With the help of multivariate regressions, the main drivers of systemic risk are identified.

The paper finds an increasing degree of interconnectedness between banks and insurance that correlates with systemic risk exposure. Interconnectedness peaks during periods of crisis but has a long-term influence also during normal times. Moreover, the paper finds that the insurance sector was greatly affected by spillovers from the process of capital regulation in banking. While European insurance companies initially at the start of the Basel process of capital regulation were well capitalized according to the SRISK measure, they started to become capital deficient after the implementation of the model-based approach in banking with increasing speed thereafter.

These findings are highly relevant for the ongoing global process of capital regulation in the insurance sector and potential reforms of Solvency II. Systemic risk is a leading threat to the stability of the global financial system and keeping it under control is a main challenge for policymakers and supervisors.

This paper provides novel tools for supervisors to monitor risk exposures in the insurance sector while taking into account systemic feedback from the financial system and the banking sector in particular. These tools also allow an evidence-based policy evaluation of regulatory measures such as Solvency II.