Journal of Economic Modeling Research

  In this study we compare a set of Markov Regime-Switching GARCH models in terms of their ability to forecast the Tehran stock market volatility at different time intervals. SW-GARCH models have been used to avoid the excessive persistence that usually found in GARCH models. In SW-GARCH models all parameters are allowed to switch between a low or high volatility regimes. Both Gaussian and fat-tailed conditional distributions are assumed for the residuals, and the degrees of freedom can also be state-dependent to capture possible time-varying kurtosis. Using stationary bootstrap and re-sampling, the forecasting performances of the competing models are evaluated by statistical loss functions. The empirical analysis demonstrates that SW-GARCH models outperform all standard GARCH models in forecasting volatility. Also, the SW-GARCH model with the t distribution for errors has the best performance in fitting a model and estimation.

As one of the important energy forms, natural gas consumption has an upward trend in recent years. Therefore management and planning for provision of it requires prediction of the future consumption. But many of prediction procedures are inherently stochastic therefore it is important to have better knowledge about the robustness of prediction procedures. This paper compares robustness of two prediction procedures Artificial Neural Networks as a nonlinear and ARIMA as a linear model. using resampling method to predict the monthly consumption of natural gas in the household sector. Data spans from 2001-4 to 2012-3, to train the networks, we used genetic algorithms and Particle Swarming Optimization then results were compared using 10-fold method. According to the results, the particle swarm optimization (PSO) outperforms the genetic algorithm. Then we used data from 2001-4 to 2010-3, with resampling by 2000 to predict the  natural gas consumption for the 2001 -4 to 2012-3 and to form critical values. Results show that prediction by a mixed method using ANN and PSO is more robust than ARIMA method.

Monetary policy is one of the most important macroeconomic policies which could be used for achieving economic targets such as reducing the output gap and reducing the inflation's deviation from it's target level.  These policies can be implemented through the control of volume of money or the rate of interest. Based on economic theories, the Central Bank should conduct monetary policies within a rule-based framework. In periods of positive or negative output gap or when inflation's deviation from it's target level is positive or negative, different monetary policies should be adopted. Assessment of Central Bank monetary policy's conformity to rules and the consistency of these policies with economic theories like Taylor's theory, is of vital importance. In order to evaluate the consistency of central bank monetary policies with economic theories, this study investigates the monetary strategies of Central Bank regarding the inflation's deviation and output gap during the period 1974-2013. It applies the Bootstrap method for this purpose. The result shows that Central Bank does not counteract the output gap during the periods of recession and boom and it's reactions to the inflation's deviation is in the reverse direction.