The Generalized Conditional Autoregressive Wishart Model for Multivariate Realized Volatility

It is well known that in finance variances and covariances of asset returns move together over time. Recently, much interest has been aroused by an approach involving the use of the realized covariance (RCOV) matrix constructed from high-frequency returns as the ex-post realization of the covariance matrix of low-frequency returns. For the analysis of dynamics of RCOV matrices, we propose the generalized conditional autoregressive Wishart (GCAW) model. Both the noncentrality matrix and scale matrix of the Wishart distribution are driven by the lagged values of RCOV matrices, and represent two different sources of dynamics, respectively. The GCAW is a generalization of the existing models, and accounts for symmetry and positive definiteness of RCOV matrices without imposing any parametric restriction. Some important properties such as conditional moments, unconditional moments, and stationarity are discussed. Empirical examples including sequences of daily RCOV matrices from the New York Stock Exchange illustrate that our model outperforms the existing models in terms of model fitting and forecasting.     

Layered adaptive importance sampling

Monte Carlo methods represent the de facto standard for approximating complicated integrals involving multidimensional target distributions. In order to generate random realizations from the target distribution, Monte Carlo techniques use simpler proposal probability densities to draw candidate samples. The performance of any such method is strictly related to the specification of the proposal distribution, such that unfortunate choices easily wreak havoc on the resulting estimators. In this work, we introduce a layered (i.e., hierarchical) procedure to generate samples employed within a Monte Carlo scheme. This approach ensures that an appropriate equivalent proposal density is always obtained automatically (thus eliminating the risk of a catastrophic performance), although at the expense of a moderate increase in the complexity. Furthermore, we provide a general unified importance sampling (IS) framework, where multiple proposal densities are employed and several IS schemes are introduced by applying the so-called deterministic mixture approach. Finally, given these schemes, we also propose a novel class of adaptive importance samplers using a population of proposals, where the adaptation is driven by independent parallel or interacting Markov chain Monte Carlo (MCMC) chains. The resulting algorithms efficiently combine the benefits of both IS and MCMC methods.     

Varying kernel marginal density estimator for a positive time series

This paper analyses the large sample behaviour of a varying kernel density estimator of the marginal density of a non-negative stationary and ergodic time series that is also strongly mixing. In particular we obtain an approximation for bias, mean square error and establish asymptotic normality of this density estimator. We also derive an almost sure uniform consistency rate over bounded intervals of this estimator. A finite sample simulation shows some superiority of the proposed density estimator over the one based on a symmetric kernel.     

Can Quasi‐market and Multi‐level Governance Co‐exist? Insights from the Case of Lombardy's Employment Services System

This article explores the functioning of Lombardy's networked employment services system, inspired by quasi‐market and horizontal subsidiarity principles, and specifically addresses a gap in the quasi‐market literature, where little attention is devoted to the role played by institutions at lower levels of government. A qualitative study of the Lombardy system, with a focus on the municipality of Milan, is relied upon in order to explore the extent to which the principles of quasi‐market and multi‐level governance pursued by the regional government are allowed to co‐exist in practice. Here, sub‐regional levels of government are directly involved in services provision, but enjoy a more privileged condition relative to the private providers, thereby jeopardizing the implementation of an effective quasi‐market. The article contributes to existing theories by suggesting that horizontal subsidiarity and marketization cannot neglect multi‐level governance in those sectors where public bodies at various levels of government are directly involved in implementation.     

A Multi-Dimensional Conceptual Framework for Trauma-Informed Practice in Addictions Programming

Previous research has conceptualized trauma-informed practice in relation to five key values: safety, trust, choice, collaboration, and empowerment. This research identifies key organizational, programmatic, and interpersonal characteristics in community-based residential addictions treatment programming that exemplify each of these principles. Utilizing qualitative research methods, involving open-ended, one to one interviews with clients in residential substance misuse treatment (n = 41), respondents identified the importance of experiencing “safety” in relation to physical safety, confidentiality, reassurance, rule enforcement, and peer relationships. “Trust” was manifested in sharing, staff availability, nonjudgmental interactions, positive relationship dynamics, and caring. “Choice” was articulated in relation to individual needs, participation, opportunities, and focus of efforts. “Collaboration” was characterized in relation to opportunities for feedback, planning, goal setting, specificity, and support. Finally, “empowerment” was characterized by comfort in sharing, trigger management, trauma awareness, and understanding. The findings provide a conceptual framework for a trauma-informed social services organizational practice environment. Findings can inform adaptations to social service delivery processes and programs to become aligned with the values of trauma-informed practice. Future research can build on this framework by testing the study findings with quantitative methods along with replicating current methods in other social service delivery sectors.     

Constructing prevention programmes with a Māori health service provider view

Mainstream approaches to chronic condition management and prevention inadequately address the needs of Māori, the Indigenous people of New Zealand. Māori health service providers (MHSPs) are uniquely placed to address the critical gap in the prevention of chronic conditions. In this paper, we report qualitative research findings investigating how prevention was being modelled, practiced and measured in selected MHSP settings. Results indicate barriers to achieving wellbeing through health service delivery. The dominant individualistic, medical conditions-focused discourse, along with responding to acute need, is a driver of service delivery norms.There are examples of shifts in organisational structure and delivery configurations that demonstrate that these norms are being challenged and reframed, in some form, by MHSPs. Consolidation of these approaches requires significant work and increased resources as well as a broader systems-level response that prioritises prevention.