Inflation-inflation uncertainty nexus in Iran——Using TARCH model

This paper examines the relationship between inflation and inflation uncertainty in Iran economy using monthly data of Iran over the period 1990-2009. TARCH model is used to peruse the stochastic variation and asymmetries in the economic instruments. The result indicates that there is a positive relationship between inflation and inflation uncertainty. Also, the authors investigate from the Granger causality test that inflation is Granger causality of inflation uncertainty.     

The causality direction in consumption–income process and sensitivity to lag structures

Using the statistical procedures developed by Granger (1969) and Sims (1972), the causality direction in the consumption–income process is examined in this study. The results provide some justification in concluding that the causality direction is from income to consumption. It can also be seen that the results of causality tests are very sensitive to the lag lengths employed. The statistical procedure, which is based on Grangers's contribution, appears to be more consistent than Sims's procedure.     

A dual measure of economies of scope

A derivative-based measure of economies of scope is obtained by exploiting the duality between the shadow cost function and the input distance function. This is a useful measure when the econometric estimation of a cost function is not viable.     

Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms

Security indices are the main tools for evaluation of the status of financial markets. Moreover, a main part of the economy of any country is constituted of investment in stock markets. Therefore, investors could maximize the return of investment if it becomes possible to predict the future trend of stock market with appropriate methods. The nonlinearity and nonstationarity of financial series make their prediction complicated. This study seeks to evaluate the prediction power of machine‐learning models in a stock market. The data used in this study include the daily close price data of iShares MSCI United Kingdom exchange‐traded fund from January 2015 to June 2018. The prediction process is done through four models of machine‐learning algorithms. The results indicate that the deep learning method is better in prediction than the other methods, and the support vector regression method is in the next rank with respect to neural network and random forest methods with less error.     

The behavior of MENA oil and non-oil producing countries in international portfolio optimization

It is well documented in developed economies that portfolio investment across national borders brings benefits of increasing returns and/or reducing risk. Dividing MENA stock markets into two main groups (oil producing and non-oil producing countries), this study examines the potential role of each group in providing diversification benefits for international investors. In addition, the behavior of the long and the short-run Efficient Frontiers (EFs) constructed by each of the sub-groups and the combined MENA markets is explored. Multi-objective international portfolio models are proposed under Mean-Variance and Mean-Lower Partial Moment frameworks, and the Multiple Fitness Function Genetic Algorithm (MFFGA) is used to find the EFs of optimal portfolios. The findings indicate that the stock markets of oil producing countries can be considered as a potential avenue for international portfolio diversification for investors not only from the same countries but also from the other MENA markets. It was also found that international portfolios constructed from the combination of MENA equity markets are more stable compared to the portfolios of sub-group markets. Further, the findings indicate that the behavior of short-term EFs in the MENA region cannot be predicted by the behavior of long-term EFs.     

Estimation and testing of stochastic frontier models using variational Bayes

We show how a wide range of stochastic frontier models can be estimated relatively easily using variational Bayes. We derive approximate posterior distributions and point estimates for parameters and inefficiency effects for (a) time invariant models with several alternative inefficiency distributions, (b) models with time varying effects, (c) models incorporating environmental effects, and (d) models with more flexible forms for the regression function and error terms. Despite the abundance of stochastic frontier models, there have been few attempts to test the various models against each other, probably due to the difficulty of performing such tests. One advantage of the variational Bayes approximation is that it facilitates the computation of marginal likelihoods that can be used to compare models. We apply this idea to test stochastic frontier models with different inefficiency distributions. Estimation and testing is illustrated using three examples.