Walid Bakry

Bio: Walid Bakry is an academic researcher from University of Sydney. The author has contributed to research in topics: Stock market & Cryptocurrency. The author has an hindex of 4, co-authored 18 publications receiving 47 citations.

The Australian Stock Market’s Reaction to the First Wave of the COVID-19 Pandemic and Black Summer Bushfires: A Sectoral Analysis

Abstract: In this study, we investigated the impact of the first wave of the COVID-19 pandemic on various sectors of the Australian stock market. Market capitalization and equally weighted indices were formed for eleven Australian sectors to examine the influence of the pandemic on them. First, we examined the financial contagion between the Chinese stock market and Australian sector indices through the dynamic conditional correlation fractionally integrated generalized autoregressive conditional heteroskedasticity (DCC-FIGARCH) model. We found high time-varying correlations between the Chinese stock market and most of the Australian sector indices, with the financial, health care, information technology, and utility sectors displaying a decrease in co-movements during the pandemic. The Modified Iterative Cumulative Sum of Squares (MICSS) analysis results indicated the presence of structural breaks in the volatilities of most of the sector indices around the end of February 2020, but consumer staples, industry, information technology and real estate indices did not display any break. Markov regime-switching regression analysis depicted that the pandemic has mainly affected three sectors: consumer staples, industry, and real estate. When we considered the firm size, we found that smaller companies in the energy sector exhibited gradual deterioration, whereas small firms in the consumer staples sector experienced the largest positive impact from the pandemic.

Oil price volatility and firm profitability: an empirical analysis of Shariah-compliant and non-Shariah-compliant firms

Abstract: PurposeThis study examines the impact of oil price volatility on firm profitability. As Shariah-compliant firms operate under restrictions, the study also explores whether oil price volatility affects Shariah-compliant firms differently from their non-Shariah-compliant counterparts.Design/methodology/approachThe study sample includes all non-financial firms listed on Gulf Cooperation Council stock exchanges from 2005 to 2019. In evaluating the oil price volatility–profitability relationship, static (panel fixed effects) and dynamic (system generalised method of moments) models were used.FindingsOil price volatility significantly depresses firm profitability. In addition, Shariah-compliant firms are more significantly affected by oil price volatility than their non-Shariah-compliant peers. The results suggest that high oil price volatility exposes Shariah-compliant firms to higher bankruptcy risk than non-Shariah-compliant firms and that positive and negative oil price shocks have asymmetric effects on firm performance.Research limitations/implicationsThe findings of the paper call for more economic diversification by supporting non-oil sectors in the region and raise the need for more development of Islam-compliant products that compete with traditional instruments to help Shariah-compliant firms cope with uncertainty. Moreover, managers need to prepare quick alert and response procedures to reduce the negative impacts of oil price volatility on profitability.Originality/valueTo the best of the authors’ knowledge, this study is the first to explore the relationship between oil price volatility and profitability of non-financial firms. Further, the study extends prior Islamic corporate finance literature by enhancing the understanding of how Islamic corporate decisions affect firm performance during instability.

Bitcoin and Portfolio Diversification: A Portfolio Optimization Approach

Abstract: The fundamental objective of portfolio diversification is to construct a portfolio of uncorrelated or mildly correlated assets so as to maximize the risk-adjusted returns on a portfolio. Portfolio Optimization is one of the techniques used by investment professionals to explore the potential of different assets in maximizing the risk-adjusted returns of the portfolio by adjusting the weight of each asset using simulations or constrained scenarios. A significant amount of research has already been conducted in the area of portfolio diversification that helps investors in devising their investment strategies and policies. Cryptocurrencies in general, and Bitcoin in particular, have aroused significant interest among investment professionals, policymakers, and regulators alike. Although much research has primarily focused on the legal and technological aspects of Bitcoin, the examination of other financial, diversification, hedge, and safe-haven aspects of Bitcoin has not progressed as far. This study explores the potential of Bitcoin as an alternative asset, and its potential in portfolio diversification, by using the portfolio optimization approach. The study employs the portfolio optimization approach under multiple constraining scenarios to evaluate the effectiveness of Bitcoin in portfolio diversification. A Monte-Carlo Simulation approach is then employed to evaluate the outcomes under each scenario by randomizing the outcomes of portfolio optimization. This study suggests that Bitcoin, due to its exotic nature, unwavering appeal, and unknown set of drivers, could at best act as a diversifier rather than a hedge or a safe-haven.

Forecasting, Structural Time Series Models and the Kalman Filter

Abstract: In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Modelling Financial Time Series

Abstract: Features of Financial Returns Modelling Price Volatility Forecasting Standard Deviations The Accuracy of Autocorrelation Estimates Testing the Random Walk Hypothesis Forecasting Trends in Prices Evidence Against the Efficiency of Futures Markets Valuing Options Appendix: A Computer Program for Modelling Financial Time Series.

Exploiting Cross Section Variation for Unit Root Inference in Dynamic Data (Now published in Economics Letters 44 (1), 1994, pp.9-19.)

Abstract: This paper considers unit root regressions in data having simultaneously extensive cross-section and time-series variation. The standard least-squares estimators in such data structures turn out to have an asymptotic distribution that is neither Op(T-1) Dickey-Fuller, nor Op(N-?) normal and asymptotically unbiased. Instead, the estimator turns out to be consistent and asymptotically normal, but has a non-vanishing bias in its asymptotic distribution.