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The purpose of this chapter is to analyze the possible portfolio diversification opportunities between Asian Islamic market and other regions’ Islamic markets; namely USA, Europe, and BRIC. This study makes the initial attempt to fill in the gaps of previous studies by focusing on the proxies of global Islamic markets to identify the correlations among those selected markets by employing the recent econometric methodologies such as multivariate generalized autoregressive conditional heteroscedastic–dynamic conditional correlations (MGARCH–DCC), maximum overlap discrete wavelet transform (MODWT), and the continuous wavelet transform (CWT). By utilizing the MGARCH-DCC, this chapter tries to identify the strength of the time-varying correlation among the markets. However, to see the time-scale-dependent nature of these mentioned correlations, the authors utilized CWT. For robustness, the authors have applied MODWT methodology as well. The findings tend to indicate that the Asian investors have better portfolio diversification opportunities with the US markets, followed by the European markets. BRIC markets do not offer any portfolio diversification benefits, which may be explained partly by the fact that the Asian markets cover partially the same countries of BRIC markets, namely India and China. Considering the time horizon dimension, the results narrow down the portfolio diversification opportunities only to the short-term investment horizons. The very short-run investors (up to eight days only) can benefit through portfolio diversification, especially in the US and European markets. The above-mentioned results have policy implications for the Asian Islamic investors (e.g., Portfolio Management and Strategic Investment Management).

In this chapter we perform a Monte Carlo simulation study of the errors-in-variables model examined in Ramsey, Gallegati, Gallegati, and Semmler (2010) by using a wavelet multiresolution approximation approach. Differently from previous studies applying wavelets to errors-in-variables problem, we use a sequence of multiresolution approximations of the variable measured with error ranging from finer to coarser scales. Our results indicate that multiscale approximations to the variable observed with error based on the coarser scales provide an unbiased asymptotically efficient estimator that also possess good finite sample properties.

While it has been claimed in many empirical studies that the political futures market can forecast better than the polls, it is unclear upon which our forecast should be based. Standard practice seems to suggest the use of the closing price of the market, as a reflection of the continuous process of information revealing and aggregation, but we are unsure that this practice applies to thin markets. In this chapter, we propose a number of reconstructions of the price series and use the closing price based on these reconstructed series as the forecast. We then test these ideas by comparing their forecasting performance with the closing price of the original series. It is found that forecasting accuracy can be gained if we use the closing price based on the smoothing series rather than the original series. However, there is no clear advantage by either using more sophisticated smoothing techniques, such as wavelets, or using external information, such as trading volume and duration time. The results show that the median, the simplest smoothing technique, performs rather well when compared with all complications.

Apart from the well-known, high persistence of daily financial volatility data, there is also a short correlation structure that reverts to the mean in less than a month. We find this short correlation time scale in six different daily financial time series and use it to improve the short-term forecasts from generalized auto-regressive conditional heteroskedasticity (GARCH) models. We study different generalizations of GARCH that allow for several time scales. On our holding sample, none of the considered models can fully exploit the information contained in the short scale. Wavelet analysis shows a correlation between fluctuations on long and on short scales. Models accounting for this correlation as well as long-memory models for absolute returns appear to be promising.

The malfunction variables of power stations are related to the areas of weather, physical structure, control and load behaviour. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behaviour into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms are used to perform the Short-term estimation. The environment, the operation and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a data set. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, for any future power grid, there is a testbed ready to estimate the future failures.

The establishment of a currency union is a topical issue in the West African Monetary Zone (WAMZ). The subject of currency union formation needs to be reassessed in light of the recent efforts towards the economic integration of west African countries. This study employs the Markov Switching Model (MSM) to determine whether a currency union in WAMZ is feasible. The study analyzes the regime switching behavior in WAMZ countries’ foreign exchange markets before and after the formation of the union. The contribution of this study is two-fold. First, the study accounts for the success or otherwise of the latest efforts to integrate the fiscal and monetary strategies in the zone. Secondly, the study contributes to the literature on the currency union literature in WAMZ by using Markov Switching Model (MSM) to generate novel results. The results of the study revealed that prior to the WAMZ formation, the real exchange rates of member states were more divergent. In contrast, a growing but marginal, convergence was observed after the formation of the zone amongst four (Nigeria, Sierra Leone, Gambia, and Liberia) of the six countries. The authors conclude that while WAMZ is on course for establishing a currency union, their monetary authorities must work together, particularly with Ghana and Liberia, to synchronize their policy efforts, and policy makers must implement policies to strengthen harmonious trade interactions.