Islamic project financing implementation for sustainable infrastructure development in Indonesia

Islamic financing in Indonesia infrastructure projects development has not been optimally implemented. Therefore this paper serves as a catalyst to explore alternative financial scheme such as Islamic financing for infrastructure development. The purpose of this paper is to explore the enablers and barriers in implementing Islamic project financing for public infrastructure development. The findings are then culminated into enablers and barriers in the implementation of Islamic project financing. The two main enablers are the readily availability of huge fund that can be used to support infrastructure projects; and the acceptability of the concept of shariah-compliant financing. On the other hand, the barriers include: high cost of funding; lack of financial institution capability; lack of government policy and regulation; insufficient government support and commitment; conflict between infrastructure and Islamic finance business practices; profit oriented mindset; lack of understanding of Islamic project financing knowledge in infrastructure; and insufficient project preparation.

Characterising an ECG signal using statistical modelling : a feasibility study

For clinical use, in electrocardiogram (ECG) signal analysis it is important to detect not only the centre of the P wave, the QRS complex and the T wave, but also the time intervals, such as the ST segment. Much research focused entirely on qrs complex detection, via methods such as wavelet transforms, spline fitting and neural networks. However, drawbacks include the false classification of a severe noise spike as a QRS complex, possibly requiring manual editing, or the omission of information contained in other regions of the ECG signal. While some attempts were made to develop algorithms to detect additional signal characteristics, such as P and T waves, the reported success rates are subject to change from person-to-person and beat-to-beat. To address this variability we propose the use of Markov-chain Monte Carlo statistical modelling to extract the key features of an ECG signal and we report on a feasibility study to investigate the utility of the approach. The modelling approach is examined with reference to a realistic computer generated ECG signal, where details such as wave morphology and noise levels are variable.

Modeling conditional correlations of asset returns : a smooth transition approach

In this paper we propose a new multivariate GARCH model with time-varying conditional correlation structure. The time-varying conditional correlations change smoothly between two extreme states of constant correlations according to a predetermined or exogenous transition variable. An LM–test is derived to test the constancy of correlations and LM- and Wald tests to test the hypothesis of partially constant correlations. Analytical expressions for the test statistics and the required derivatives are provided to make computations feasible. An empirical example based on daily return series of five frequently traded stocks in the S&P 500 stock index completes the paper.