Pricing innovations in consumption growth: a re-evaluation of the recursive utility model

We re-evaluate the cross-sectional asset pricing implications of the recursive utility function of Epstein and Zin, 1989 and Epstein and Zin, 1991, using innovations in future consumption growth in our tests. Our empirical specification helps explain the size, value and momentum effects. Specifically, we find that (і) the beta associated with news about consumption growth has a systematic pattern: beta decreases along the size dimension and increases along the book-to-market and momentum dimensions, (іі) innovation in consumption growth is significantly priced in asset returns using both the Fama and MacBeth (1973) and the stochastic discount factor approaches, and (ііі) the model performs better than both the CAPM and Fama–French model.

Synthetic portfolio for event studies: Estimating the effects of volatility call auctions

We propose a method denoted as synthetic portfolio for event studies in market microstructure that is particularly interesting to use with high frequency data and thinly traded markets. The method is based on Synthetic Control Method and provides a robust data driven method to build a counterfactual for evaluating the effects of the volatility call auctions. We find that SMC could be used if the loss function is defined as the difference between the returns of the asset and the returns of a synthetic portfolio. We apply SCM to test the performance of the volatility call auction as a circuit breaker in the context of an event study. We find that for Colombian Stock Market securities, the asynchronicity of intraday data reduces the analysis to a selected group of stocks, however it is possible to build a tracking portfolio. The realized volatility increases after the auction, indicating that the mechanism is not enhancing the price discovery process.

A robust optimization approach for index tracking problem

Index tracking is an investment approach where the primary objective is to keep portfolio return as close as possible to a target index without purchasing all index components. The main purpose is to minimize the tracking error between the returns of the selected portfolio and a benchmark. In this paper, quadratic as well as linear models are presented for minimizing the tracking error. The uncertainty is considered in the input data using a tractable robust framework that controls the level of conservatism while maintaining linearity. The linearity of the proposed robust optimization models allows a simple implementation of an ordinary optimization software package to find the optimal robust solution. The proposed model of this paper employs Morgan Stanley Capital International Index as the target index and the results are reported for six national indices including Japan, the USA, the UK, Germany, Switzerland and France. The performance of the proposed models is evaluated using several financial criteria e.g. information ratio, market ratio, Sharpe ratio and Treynor ratio. The preliminary results demonstrate that the proposed model lowers the amount of tracking error while raising values of portfolio performance measures.

Index Tracking Using Data-Mining Techniques and Mixed-Binary Linear Programming

Index tracking has become one of the most common strategies in asset management. The index-tracking problem consists of constructing a portfolio that replicates the future performance of an index by including only a subset of the index constituents in the portfolio. Finding the most representative subset is challenging when the number of stocks in the index is large. We introduce a new three-stage approach that at first identifies promising subsets by employing data-mining techniques, then determines the stock weights in the subsets using mixed-binary linear programming, and finally evaluates the subsets based on cross validation. The best subset is returned as the tracking portfolio. Our approach outperforms state-of-the-art methods in terms of out-of-sample performance and running times.

The role of the non-government organisation (NGO) in practicing editorial photography in a globalised media environment

The news media industry has changed dramatically in the last 10 to 20 years into a global business with ever increasing attention being devoted to entertainment and celebrity. There is also a growing reliance on images produced by citizens (citizen photojournalism) by media outlets and publishers. It is widely acknowledged this has shrunk publication opportunities for professional photographers undertaking editorial projects. As a result, photographers are increasingly relying on non-government organisations (NGOs) to gain access to photographing issues and events in developing countries and to expand their economic and portfolio opportunities. This increase of photographers working for and alongside NGOs has given rise to a new genre of editorial photography I call NGO Reportage. By way of a case study, an exploration of this new genre reveals important issues for photographers working alongside NGO’s and examines the constructed narratives of images contained within these emerging practices.

The role of Non-Government Organisations (NGOS) in practising editorial photography in a globalised media environment

The news media industry has changed dramatically into a global business with ever-increasing attention being devoted to entertainment and celebrity across the last 10–20 years. There has also been a growing reliance on images produced by citizens (citizen photojournalism), by media outlets and publishers. It is widely acknowledged that in tandem these changes have shrunk publication opportunities for professional photographers undertaking editorial projects. As a result, photographers are increasingly relying on non-government organisations (NGOs) to gain access to photographing issues and events in developing countries and to expand their economic and portfolio opportunities. This increase in photographers working for and alongside NGOs has given rise to a new genre of editorial photography which I call NGO Reportage. By way of a case study, an exploration of this new genre reveals important issues for photographers working with NGOs and examines the constructed narratives of images contained within these emerging practices.

The financial performance of socially responsible investments: insights from the intertemporal CAPM

© 2015 Springer Science+Business Media Dordrecht This study formulates a two-factor empirical model under the intertemporal CAPM framework to evaluate the cross-sectional implications of socially responsible investments in the US equity market. Our results show that socially responsible investments have no asset pricing impact on the US market. We argue that this ‘no financial impact’ finding indicates that investors will not be disadvantaged financially by investing in socially responsible funds or corporations.

Measuring globalisation: An evolutionary economic approach to tracking the evolution of international trade

Operationalising and measuring the concept of globalisation is important, as the extent to which the international economy is integrated has a direct impact on industrial dynamics, national trade policies and firm strategies. Using complex systems network analysis with longitudinal trade data from 1938 to 2003, this paper presents a new way to measure globalisation. It demonstrates that some important aspects of the international trade network have been remarkably stable over this period. However, several network measures have changed substantially over the same time frame. Taken together, these analyses provide a novel measure of globalisation.

Channel tracking algorithms for highly efficient wireless broadband communications in rural areas

High-speed broadband internet access is widely recognised as a catalyst to social and economic development, having a significant impact on global economy. Rural Australia’s inherent dispersed population over a large geographical area make the delivery of efficient, well-maintained and cost-effective internet a challenging task. The novel and highly-efficient Multi-User-Single-Antenna for MIMO (MUSA-MIMO) broadband wireless communication technology can effectively be used to deliver wireless broadband access to rural areas. This research aims to develop for the first time, an efficient and accurate algorithm for the tracking and prediction of Channel State Information (CSI) at the transmitter, by characterising time variation effects of the wireless communication channel on the performance of a highly-efficient MUSA-MIMO technology particularly suited for rural communities, improving their quality of life and economic prosperity.

Evaluating multivariate volatility forecasts : how effective are statistical and economic loss functions?

Multivariate volatility forecasts are an important input in many financial applications, in particular portfolio optimisation problems. Given the number of models available and the range of loss functions to discriminate between them, it is obvious that selecting the optimal forecasting model is challenging. The aim of this thesis is to thoroughly investigate how effective many commonly used statistical (MSE and QLIKE) and economic (portfolio variance and portfolio utility) loss functions are at discriminating between competing multivariate volatility forecasts. An analytical investigation of the loss functions is performed to determine whether they identify the correct forecast as the best forecast. This is followed by an extensive simulation study examines the ability of the loss functions to consistently rank forecasts, and their statistical power within tests of predictive ability. For the tests of predictive ability, the model confidence set (MCS) approach of Hansen, Lunde and Nason (2003, 2011) is employed. As well, an empirical study investigates whether simulation findings hold in a realistic setting. In light of these earlier studies, a major empirical study seeks to identify the set of superior multivariate volatility forecasting models from 43 models that use either daily squared returns or realised volatility to generate forecasts. This study also assesses how the choice of volatility proxy affects the ability of the statistical loss functions to discriminate between forecasts. Analysis of the loss functions shows that QLIKE, MSE and portfolio variance can discriminate between multivariate volatility forecasts, while portfolio utility cannot. An examination of the effective loss functions shows that they all can identify the correct forecast at a point in time, however, their ability to discriminate between competing forecasts does vary. That is, QLIKE is identified as the most effective loss function, followed by portfolio variance which is then followed by MSE. The major empirical analysis reports that the optimal set of multivariate volatility forecasting models includes forecasts generated from daily squared returns and realised volatility. Furthermore, it finds that the volatility proxy affects the statistical loss functions’ ability to discriminate between forecasts in tests of predictive ability. These findings deepen our understanding of how to choose between competing multivariate volatility forecasts.