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In this paper we take up Bayesian inference for the consumption capital asset pricing model. The model has several econometric complications. First, it implies exact relationships between asset returns and the endowment growth rate that will be rejected by all possible realizations. Second, it was thought before that it is not possible to express asset returns in closed form. We show that Labadie's (1989) solution procedure can be applied to obtain asset returns in closed form and, therefore, it is possible to give an econometric interpretation in terms of traditional measurement error models. We apply the Bayesian inference procedures to the Mehra and Prescott (1985) dataset, we provide posterior distributions of structural parameters and posterior predictive asset return distributions, and we use these distributions to assess the existence of asset returns puzzles. The approach developed here, can be used in sampling theory and Bayesian frameworks alike. In fact, in a sampling-theory context, maximum likelihood can be used in a straightforward manner.

Purpose – The purpose of this chapter is to estimate non-Gaussian distributions by means of Johnson distributions. An empirical illustration on hedge fund returns is detailed.

Methodology/approach – To fit non-Gaussian distributions, the chapter introduces the family of Johnson distributions and its general extensions. We use both parametric and non-parametric approaches. In a first step, we analyze the serial correlation of our sample of hedge fund returns and unsmooth the series to correct the correlations. Then, we estimate the distribution by the standard Johnson system of laws. Finally, we search for a more general distribution of Johnson type, using a non-parametric approach.

Findings – We use data from the indexes Credit Suisse/Tremont Hedge Fund (CSFB/Tremont) provided by Credit Suisse. For the parametric approach, we find that the SU Johnson distribution is the most appropriate, except for the Managed Futures. For the non-parametric approach, we determine the best polynomial approximation of the function characterizing the transformation from the initial Gaussian law to the generalized Johnson distribution.

Originality/value of chapter – These findings are novel since we use an extension of the Johnson distributions to better fit non-Gaussian distributions, in particular in the case of hedge fund returns. We illustrate the power of this methodology that can be further developed in the multidimensional case.

I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.

The paper uses 101 years of Chilean and international financial assets returns to investigate mean-variance optimal portfolio allocations. The key conclusion is that the share of international unhedged investments is substantial even in minimum risk portfolios (20%), unless the period 1980–2002 is assumed to be drawn from a different distribution and previous history is disregarded. In addition to that, the paper finds that mean-variance optimal investors would have generated substantial demand for an asset replicating the return profile of an efficient pay-as-you-go pension scheme. Labour income and departures from log-normality of returns might, however, affect the latter conclusion.

To investigate the evolutionary process of the retail logistics network formation, and to propose a relationship framework between the logistics asset buyer (the retailer) and the logistics asset supplier (the third‐party logistics firm).

The evolutionary process is based on the way the asset specificity element of transaction costs theory can be perceived by the logistics asset buyer and the logistics asset supplier. The asset specificity element is linked to both network and buyer‐supplier relationship theories with the aim of conceptualising a buyer‐supplier relationship framework. Secondary data for the UK food retail chain are also employed.

A new relationship framework is developed based on the buyers’‐suppliers’ perceptions in relation to logistics asset specificity, and the conditions required for the formation of the retail logistics network are illustrated. If transaction costs are perceived as high by both the buyer and the supplier of a logistics asset, the retailer will engage into a fourth‐party logistics network formation where the use of information technology systems is of critical importance. At this stage, these systems will become the primary co‐ordination device for the reduction and absorption of complexity in the retail chain.

The paper offers a unique buyer‐supplier partnership framework by proposing that the formation of a fourth‐party logistics network will decrease the complexity of modern retail logistics operations. The paper will assist retail managers responsible for the development of logistics strategies and will be beneficial to researchers examining logistics and supply chain management operations.

In this paper we consider the effects of certain capital market imperfections on portfolio choice problems. We show that as a result of these imperfections, the distribution functions of rates of return may depend on portfolio allocation, thus leading to non‐convexities and consequently to patterns of specialisation rather than diversification.

The purpose of this paper is to establish a methodological framework to address key issues in electricity distribution network development. The paper defines subtasks in the strategy process and presents key elements in the strategy work and long‐term network planning. The results are illustrated by a case network.

The paper describes the methodology for cost and reliability analyses in the strategy work. The focus is on techno‐economic feasibility of certain network development technologies in the network strategy and the surveys are linked to economic regulation, specifically to reliability of supply and allowed return. The study addresses the stages of strategic decision making and compilation of investment strategies.

The strategic planning concept and methods are applicable in practice; the results have proven valuable in the long‐term business development and in discussions with the company owners. Outage costs are an essential element in the economic regulation of the business, reliability being a key driver in network planning.

There is no universal solution to strategic decision making, but each development task is highly case specific. This is due to diverging operating environments and targets set by the company owners; these issues strongly influence the strategy process.

The work illustrates strategic planning in an actual distribution company and shows how the methodology can be applied to the strategic network development. Nevertheless, the results cannot be generalised as such, but each network has to be considered individually.

The proposed concept can be applied to the long‐term development of distribution networks. The results are internationally applicable, yet diverging regulatory models call for specific methodology in each country.

Over the past four decades, real values of farm real estate and the share of assets on farmers’ balance sheets attributed to farm real estate have increased. The purpose of this paper is to examine the factors that explain the concentration of the US agricultural balance sheet around a particular asset, farm real estate, and the extent to which the degree of asset concentration varies across United States Department of Agriculture production regions.

State-level data from 48 states and entropy-based inequality measures are used to examine changes in asset distributions (real estate vs non-real estate assets) both within and between regions over time.

The agricultural balance sheet is found to concentrate into real estate in the USA over the period 1960-2003 with the rate of concentration varying across production regions. In some regions, the concentration is mainly due to changes in real estate prices, while in other regions concentration is also driven by changes in real estate holdings or changes in total factor productivity.

This study formally estimates the degree to which the concentration of balance sheet items can be explained by the observed changes in farm real estate prices relative to observed changes in agricultural factor productivity or changes in farm real estate holdings. The computed regional differences in asset concentration and its main drivers have implications for changes in equity and solvency positions of farmers as well as agricultural lenders’ risk exposure.

Application of financial risk models in the emerging markets poses special challenges. A fundamental challenge is to accurately model the return distributions which are particularly fat tailed and skewed. Value-at-Risk (VaR) measures based on the Extreme Value Theory (EVT) have been suggested, but typically data histories are limited, making it hard to test and apply EVT. The chapter addresses issues in (i) modeling the VaR measure in the presence of structural breaks in an economy, (ii) the choice of stable innovation distribution with volatility clustering effects, (iii) modeling the tails of the empirical distribution, and (iv) fixing the cut-off point for isolating extreme observations. Pakistan offers an instructive case since its equity market exhibits high volatility and incidence of extreme returns. The recent Global Financial Crisis has been another source of extreme returns. The confluence of the two sources of volatility provides us with a rich data set to test the VaR/EVT model rigorously and examine practical challenges in its application in an emerging market.

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.

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.

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.

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.

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.

The novelty of this work lies with the data used and the detailed analysis and for the post-GFC sample.