asymmetric ACD model" of Bauwens and Giot (2003) which captures information related to the evolution of prices as well as the quote-durations.

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Using high frequency financial data and associated risk decomposition and quantile regression techniques we characterise some stylised facts and relationship(s) between standard betas, diffusion betas and jump betas of individual stocks and portfolios in Japanese market. We then investigate whether the beta in the conventional CAPM is the weighted average of the jump beta and diffusion beta in the jump-diffusion model and how these different betas behave across different banks. Our empirical findings indicate that jump betas are cross-sectionally more dispersed than diffusion and standard betas. We find that the relationship(s) between the three betas are non-linear. We also find that standard betas are influenced more by diffusion betas than the jump betas, although the actual magnitude of the weights differ significantly across the quantile. This relationship holds for both individual stocks and portfolios. Empirical studies have shown that betas vary systematically across large and small firm equities. For large equity portfolios, the jump beta-diffusion beta ratios are lower that the jump betadiffusion beta ratios of the small equity portfolios. Empirically, we further find that the standard CAPM beta is composed of two-components, i.e. it is the weighted average of the diffusion component and the jump component.

Purpose This paper aims to contribute to knowledge by investigating the return behaviour of seven global real estate investment trusts (REITs) with respect to the appropriate distributional fit that captures tail and shape characteristics. The study adds to the knowledge of distributional properties of seven global REITs by using the generalised lambda distribution (GLD), which captures fairly well the higher moments of the returns. Design/methodology/approach This is an empirical study with GLD through three rival methods of fitting tail and shape properties of seven REIT return data from January 2008 to November 2017. A post-Global Financial Crisis (GFC) (from July 2009) period fits from the same methods are juxtaposed for comparison. Findings The maximum likelihood estimates outperform the methods of moment matching and quantile matching in terms of goodness-of-fit in line with extant literature; for the post-GFC period as against the full-sample period. All three methods fit better in full-sample period than post-GFC period for all seven countries for the Region 4 support dynamics. Further, USA and Singapore possess the strongest and stronger infinite supports for both time regimes. Research limitations/implications The REITs markets, however, developed, are of wide varied sizes. This makes comparison less than ideal. This is mitigated by a univariate analysis rather than multivariate one. Practical implications This paper is a reminder of the inadequacy of the normal distribution, as well as the mean, variance, skewness and kurtosis measures, in describing distributions of asset returns. Investors and policymakers may look at the location and scale of GLD for decision-making about REITs. Originality/value The novelty of this work lies with the data used and the detailed analysis and for the post-GFC sample.