The optimal trade-off between interest rate risk and annual return of bond ladders

Bond laddering is a popular fixed-income investment strategy. The main purpose of this paper is to develop a methodology for determining private investors’ most interest rate risk (IRR)-return-efficient investment horizon for bond ladders (BLs), which are virtually free of credit risk. Two IRR measures of a continuously rolling and homogenous BL (CRHBL) are analytically derived under the assumption that interest rates are martingales. The first measure is the modified duration, which assumes a flat term structure of interest rates. However, this assumption is not fully supported by the empirical data and, thus, an additional IRR measure is proposed. Under each of these two measures, the ratios between the annual return in excess of the demand deposit rate and IRR of CRHBLs with different investment horizons are calculated. As expected, CRHBLs with rather low IRR are most risk-return-efficient. The results for the theoretical CRHBLs also apply to “real-world” discrete BLs. Thus, the proposed methodology can help private investors construct IRR-return-efficient discrete BLs.     

CORPORATE SOCIAL RESPONSIBILITY: A MICROECONOMIC REVIEW OF THE LITERATURE

This paper provides a review of the theoretical and empirical literature on Corporate Social Responsibility (CSR). Depending on whether an individual can be considered as selfish, or whether she has also social preferences, motives for CSR, can be explained in different ways. Furthermore, we explicitly highlight the influence of different Corporate Governance System on CSR as well as the relationship between firms' size and social commitment. Because it is difficult to distinguish between different theories in empirical studies, we argue that an experimental approach might be suitable to test theories of CSR.     

Concept‐Based Bayesian Model Averaging and Growth Empirics

In specifying a regression equation, we need to specify which regressors to include, but also how these regressors are measured. This gives rise to two levels of uncertainty: concepts (level 1) and measurements within each concept (level 2). In this paper we propose a hierarchical weighted least squares (HWALS) method to address these uncertainties. We examine the effects of different growth determinants taking explicit account of the measurement problem in the growth regressions. We find that estimates produced by HWALS provide intuitive and robust explanations. We also consider approximation techniques which are useful when the number of variables is large or when computing time is limited.     

Corporate Responsibility and the Collegial Field

This article is based on 21 interviews of informants actively engaged with corporate responsibility in Sweden. The article introduces a new concept—the “collegial field”—which is helpful in understanding the course of events. With systems that are more open to other organizations, horizontal groupings with common interests become more influential. Collegial fields can also be relevant for understanding other activities in organizations than corporate responsibility.     

Prospect Theory in the Heterogeneous Agent Model

Using the Heterogeneous Agent Model framework, we incorporate an extension based on Prospect Theory into a popular agent-based asset pricing model. This extension covers the phenomenon of loss aversion manifested in risk aversion and asymmetric treatment of gains and losses. Using Monte Carlo methods, we investigate behavior and statistical properties of the extended model and assess how our extension is manifested in different strategies. We show that, on the one hand, the Prospect Theory extension keeps the essential underlying mechanics of the model intact, but on the other hand it considerably changes the model dynamics. Stability of the model is increased and fundamentalists may be able to survive in the market more easily. When only the fundamentalists are loss-averse, other strategies profit more.

     

Disentangling types of liquidity and testing limits-to-arbitrage theories in the CDS–bond basis

We disentangle asset-specific, market, and funding liquidity in the CDS–bond basis outside and during the 2007–9 global financial crisis. Our findings stress the importance of separating different types of liquidity, since all three measures have independently negative impacts on the basis. Funding liquidity emerges as the economically most important liquidity metric. While asset-specific liquidity is cross-correlated in both the cash and derivative markets, funding and market liquidity only matter for the cash market. We exploit the decomposition of the basis to test predictions of limits-to-arbitrage theories. We find strong evidence in favor of margin-based asset pricing and flight-to-quality effects.     

Small Area Estimation for Disease Prevalence Mapping

Small area estimation (SAE) entails estimating characteristics of interest for domains, often geographical areas, in which there may be few or no samples available. SAE has a long history and a wide variety of methods have been suggested, from a bewildering range of philosophical standpoints. We describe design-based and model-based approaches and models that are specified at the area level and at the unit level, focusing on health applications and fully Bayesian spatial models. The use of auxiliary information is a key ingredient for successful inference when response data are sparse, and we discuss a number of approaches that allow the inclusion of covariate data. SAE for HIV prevalence, using data collected from a Demographic Health Survey in Malawi in 2015–2016, is used to illustrate a number of techniques. The potential use of SAE techniques for outcomes related to coronavirus disease 2019 is discussed.