The “management‐per‐result” approach to international development project design

Results‐based management (RBM) has proved to be a valuable tool for international development project management; however, there are some inconsistencies that limit the use of RBM at the design phase to manage for results. This article presents a “management‐per‐result” approach to reinforcing the project design function of RBM and illustrates its application to a real‐life project. Shying away from a technocratic approach, it emphasizes a “quick‐and‐dirty” approach and proposes an updated version of the logical framework to include success criteria and factors and very rough estimates for both project costs and benefits for targeted project results for different types of projects (infrastructure development, “process” type of project, and so forth).     

Dynamic principal component CAW models for high-dimensional realized covariance matrices

We propose a new dynamic principal component CAW model (DPC-CAW) for time-series of high-dimensional realized covariance matrices of asset returns (up to 100 assets). The model performs a spectral decomposition of the scale matrix of a central Wishart distribution and assumes independent dynamics for the principal components' variances and the eigenvector processes. A three-step estimation procedure makes the model applicable to high-dimensional covariance matrices. We analyze the finite sample properties of the estimation approach and provide an empirical application to realized covariance matrices for 100 assets. The DPC-CAW model has particularly good forecasting properties and outperforms its competitors for realized covariance matrices.     

Correlation of Labor and Civil Contracts Related to the Performance of Work: Preventing the Substitution of Concepts

Employee Responsibilities and Rights Journal - Legal relations arising from a civil law contract for the performance of work or the provision of services are not identical to labor relations, and...     

Using information quality for volatility model combinations

This paper proposes updated methodology for volatility model combinations which account for the informational content of innovations. An adaptive measure of information quality serves for the selection of model weights in order to improve daily volatility forecasts. The information quality proxy is related to the size of unexpected shocks in the volatility process. Our approach is illustrated in an empirical study with German stock market data.     

Multivariate Shrinkage for Optimal Portfolio Weights

Abstract

This paper proposes a multivariate shrinkage estimator for the optimal portfolio weights. The estimated classical Markowitz weights are shrunk to the deterministic target portfolio weights. Assuming log asset returns to be i.i.d. Gaussian, explicit solutions are derived for the optimal shrinkage factors. The properties of the estimated shrinkage weights are investigated both analytically and using Monte Carlo simulations. The empirical study compares the competing portfolio selection approaches. Both simulation and empirical studies show that the proposed shrinkage estimator is robust and provides significant gains to the investor compared to benchmark procedures.     

A latent dynamic factor approach to forecasting multivariate stock market volatility

This paper proposes a latent dynamic factor model for high-dimensional realized covariance matrices of stock returns. The approach is based on the matrix logarithm and combines common latent factors driven by HAR processes and idiosyncratic autoregressive dynamics. The model accounts for positive definiteness of covariance matrices without imposing parametric restrictions. Simulated Bayesian parameter estimates are obtained using basic Markov chain Monte Carlo methods. An empirical application to 5-dimensional and 30-dimensional realized covariance matrices shows remarkably good forecasting results, in-sample and out-of-sample.     

The conditional autoregressive Wishart model for multivariate stock market volatility

We propose a Conditional Autoregressive Wishart (CAW) model for the analysis of realized covariance matrices of asset returns. Our model assumes an autoregressive moving average structure for the scale matrix of the Wishart distribution. It accounts for positive definiteness of covariance matrices without imposing parametric restrictions, and can be estimated by Maximum Likelihood. We also propose extensions of the CAW model obtained by including a Mixed Data Sampling (MIDAS) component and Heterogeneous Autoregressive (HAR) dynamics for long-run fluctuations. The CAW models are applied to realized variances and covariances for five New York Stock Exchange stocks.