Multivariate Moments Expansion Density: Application of the Dynamic Equicorrelation Model

In this study, we propose a new semi-nonparametric (SNP) density model for describing the density of portfolio returns. This distribution, which we refer to as the multivariate moments expansion (MME), admits any non-Gaussian (multivariate) distribution as its basis because it is specified directly in terms of the basis density’s moments. To obtain the expansion of the Gaussian density, the MME is a reformulation of the multivariate Gram-Charlier (MGC), but the MME is much simpler and tractable than the MGC when positive transformations are used to produce well-defined densities. As an empirical application, we extend the dynamic conditional equicorrelation (DECO) model to an SNP framework using the MME. The resulting model is parameterized in a feasible manner to admit two-stage consistent estimation and it represents the DECO as well as the salient non-Gaussian features of portfolio return distributions. The in- and out-of-sample performance of a MME-DECO model of a portfolio of 10 assets demonstrate that it can be a useful tool for risk management purposes.

Should cash transfer be conditional? Condition, preventive care, and health outcomes

We study a Conditional Cash Transfer program in which the cash transfers to the mother only depends on the fulfillment of the national preventive visit schedule by her children born before she registered in the program. We estimate that preventive visits of children born after the mother registered in the program are 50% lower because they are excluded from the conditionality requirement. Using the same variation, we also show that attendance to preventive care improves children's health.

Higher-order moments in the theory of diversifying and portfolio composition

This paper examines the role of higher-order moments in portfolio choice within an expected-utility framework. We consider two-, three-, four- and five-parameter density functions for portfolio returns and derive exact conditions under which investors would all be optimally plungers rather than diversifiers. Through comparative statics we show the importance of higher-order risk preference properties, such as riskiness, prudence and temperance, in determining plunging behaviour. Empirical estimates for the S&P500 provide evidence for the optimality of diversification.

Exchange-Trade Funds Evaluation using Performance Measures Distribution

In this study we propose the use of the performance measure distribution rather than its punctual value to rank hedge funds. Generalized Sharpe Ratio and other similar measures that take into account the higher-order moments of portfolio return distributions are commonly used to evaluate hedge funds performance. The literature in this field has reported non-significant difference in ranking between performance measures that take, and those that do not take, into account higher moments of distribution. Our approach provides a much more powerful manner to differentiate between hedge funds performance. We use a non-semiparametric density based on Gram-Charlier expansions to forecast the conditional distribution of hedge fund returns and its corresponding performance measure distribution. Through a forecasting exercise we show the advantages of our technique in relation to using the more traditional punctual performance measures.

Multivariate Appoximations to Portfolio Return Distribution

This article proposes a three-step procedure to estimate portfolio return distributions under the multivariate Gram-Charlier (MGC) distribution. The method combines quasi maximum likelihood (QML) estimation for conditional means and variances and the method of moments (MM) estimation for the rest of the density parameters, including the correlation coefficients. The procedure involves consistent estimates even under density misspecification and solves the so-called ‘curse of dimensionality’ of multivariate modelling. Furthermore, the use of a MGC distribution represents a flexible and general approximation to the true distribution of portfolio returns and accounts for all its empirical regularities. An application of such procedure is performed for a portfolio composed of three European indices as an illustration. The MM estimation of the MGC (MGC-MM) is compared with the traditional maximum likelihood of both the MGC and multivariate Student’s t (benchmark) densities. A simulation on Value-at-Risk (VaR) performance for an equally weighted portfolio at 1% and 5% confidence indicates that the MGC-MM method provides reasonable approximations to the true empirical VaR. Therefore, the procedure seems to be a useful tool for risk managers and practitioners.

Moments of Collusion? Close readings of affective, hidden moments within feminist research

This paper addresses affective ‘moments of collusion’ present in feminist research relationships, and contextualises these seemingly personal encounters within a wider systematic framework of the early career researcher and the increasingly neoliberal climate of academia. Focusing on the temporal transition from doctoral research to postdoctorate research positions immediately post-PhD, this paper questions the concept of collusion within (immersive) fieldwork, and examines the delicate and complex question of who is colluding with whom, and for what purpose at different times within the early career academic journey. Specifically, this paper focuses on how the increasing pressures of the neoliberal university play out on our emotions and bodies during fieldwork, an area which still requires attention within the growing critiques of the affects of neo-liberalism in Higher Education. Using personal case studies as springboard for a far wider and important discussion, this paper situates such methodological dilemmas within a broader temporal framework of the increasingly precarious nature of early career academics, where ‘moments of collusion’ may be the only way to keep your head above water.