Do Islamic indices provide diversification to bitcoin? A time-varying copulas and value at risk application

This is an accepted manuscript of an article published by Elsevier in Pacific-Basin Finance Journal on 08/04/2020, available online: https://doi.org/10.1016/j.pacfin.2020.101326 The accepted version of the publication may differ from the final published version.© 2020 The emergence of new asset classes offers avenues to international investment community however understanding relationship between any two assets in a single portfolio is important. We investigate the risk dependence between daily Bitcoin and major Islamic equity markets spanning over from July 2010 to March 2018. We start by examining long memory properties of Bitcoin and sampled Islamic indices and report significant results. The residuals from fractionally integrated models are then used in bivariate time invariant and time varying copulas to investigate dependence structure. Among all Islamic indices, DJIUK, DJIJP and DJICA exhibit time varying dependence with Bitcoin. In addition, we apply VaR, CoVaR and ΔCoVaR as risk measure to examine spillover between Bitcoin and Islamic equity markets. VaR of Bitcoin exceeds from VaR of Islamic indices and CoVaR of both Islamic and Bitcoin exceeds their respective VaR, suggesting presence of risk spillover between each other. Our results also report asymmetry between downside and upside ΔCoVaR suggesting implications for investors with different risk preferences. Finally, the diversification benefits indicate that Islamic equity market serves as an effective hedge in a portfolio along with Bitcoin.Accepted versio

Acceleration of the EM algorithm via extrapolation methods: Review, comparison and new methods

EM-type algorithms are popular tools for modal estimation and the most widely used parameter estimation procedures in statistical modeling. However, they are often criticized for their slow convergence. Despite the appearance of numerous acceleration techniques along the last decades, their use has been limited because they are either difficult to implement or not general. In the present paper, a new generation of fast, general and simple maximum likelihood estimation (MLE) algorithms is presented. In these cyclic iterative algorithms, extrapolation techniques are integrated with the iterations in gradient-based MLE algorithms, with the objective of accelerating the convergence of the base iterations. Some new complementary strategies like cycling, squaring and alternating are added to that processes. The presented schemes generally exhibit either fast-linear or superlinear convergence. Numerical illustrations allow us to compare a selection of its variants and generally confirm that this category is extremely simple as well as fast.

Internal friction study of the influence of humidity on set plaster

International audienc

Analysis of crack propagation behavior in alumina with different grain sizes under static and cyclic fatigue

International audienceno abstrac

Thermal-Electrical-Mechanical Simulation of High Pressure Spark Plasma Sintering (HP-SPS) Process

In the present work, we investigate the stresses distribution using a high pressure die during SPS (Spark Plasma Sintering) experiments. In this context, we used a finite element modeling (FEM) in the case of the sintering of an alumina sample, chosen as an electrically insulator ceramic material. A thermal sintering cycle is imposed using a control pyrometer of temperature at the SiC inner die surface

Multiscaled causality of infections on viral testing volumes: The case of COVID‐19 in Tunisia

International audienceObjectives: Coronavirus disease (COVID-19) is one of the most detrimental pandemics that affected the humanity throughout the ages. The irregular historical progression of the virus over the first year of the pandemic was accompanied with far-reaching health and social damages. To prepare logistically against this worsening disaster, many public authorities around the world had set up screening and forecasting studies. This article aims to analyse the time-frequency co-evolution of the number of confirmed cases (NCC) in Tunisia and the related number of performed polymerase chain reaction (PCR) tests over the COVID-19 first year. Accurately predicting such a relationship allows Tunisian authorities to set up an effective health prevention plan.Study design: In order to keep pace with the speed of evolution of the virus, we used uninterrupted daily time series from the Tunisian Ministry of Public Health (TMPH) recorded over the COVID-19 first year. The objective is to: (1) analyse the time-frequency progress of the NCC in relationship with the number of PCR tests, (2) identify a multi-scale two-factor stochastic model in order to develop a robust bivariate forecasting technique.Methods: We assume a bivariate stochastic process which is projected onto a set of wavelet sub-spaces to investigate the scale-by-scale co-evolvement the NCC/PCR over the COVID-19 first year. A wavelet-based multiresolutional causality test is then performed.Results: The main results recommend the rejection of the null hypothesis of no instantaneous causality in both directions, while the statistics of the Granger test suggest failing to reject the null hypothesis of non-causality. However, by proceeding scale-by-scale, the Granger causality is proven significant in both directions over varying frequency bands.Conclusions: It is important to include the NCC and PCR variables in any time series model intended to predict one of these variables. Such a bivariate and multi-scale model is supposed to better predict the needs of the public health sector in screening tests. On this basis, testing campaigns with multiple periodicities can be planned by the Tunisian authorities

Fracture behavior of Ce‐TZP/alumina/aluminate composites with different amounts of transformation toughening. Influence of the testing methods

International audienceno abstrac