Markov Switching Model

The stock exchange is considered to be an important establishment to finance long term projects, on one hand, and to collect savings and finance of private section. The stock exchange can be a safe and secure place to invest surplus funds to purchase corporate stocks. As recession and prosperity in this market can have a great role in stockholders` decision-making, it becomes vital to predict these cycles. In this paper, using model MSMH(4)AR(2), we extract the financial cycles of the market. Then, using the ant colony algorithm, we determine the most significant predictors and predict the market financial cycles using neural networks. The results show that the PNN model performs better in predicting the future market with respect to the criteria of mean squared error, the root mean squared error, the model accuracy and kappa coefficient.

In this paper, we empirically investigate the relationship between oil price changes and output in a group of oil exporting countries. The dynamics of business cycles in Libya, Saudi Arabia, Nigeria, Kuwait, Venezuela and Qatar are modeled by alternative regime switching models. We show that the extension of uni-variate Markov Switching model in order to include oil revenue improves dating business cycles in these economies. For all countries, the optimal specification suggested by the data is to consider three cycles or regimes, namely, high growth, mild growth, and recession. These three regimes can be associated to high positive oil shock, mild positive oil shock and negative oil price shock. An interesting finding of the paper is that there is a variety of relationships between oil price shocks and business cycles. Thus, in order to see the effects of an oil price shock one should take into consideration the economic regime when the oil price shock hits the economy. Therefore, it is not possible to talk about a general relationship between oil price shocks and macroeconomic variables for all the main oil exporting countries. JEL Classification: E31, E32, E52, Q41

Value at Risk model based on a switching regime approach was used in this study to optimize portfolios consisting of industry index (petroleum products, investment, chemical products, and metal products). For this purpose, the VaR of returns on index should first be extracted through parametric models of the (GARCH) family in each of the above industries by using regime transitions. After the risk of return on index is obtained for each industry, the optimal portfolio is created in the next step based on VaR minimization, and the optimal value of each industry is determined in the portfolio. According to the results, (MRS-FIEGARCH) model had no superiority in VaR estimation over the other parametric models of the GARCH family. In fact (MS-EGARCH-t) was introduced as the optimal model. Among the designated industries, returns on indices followed regime transitions only in chemical products and investment by showing asymmetric reactions to external shocks. Moreover, the optimal weights were on the rise in the industries where VaR decreased over time, whereas the optimal weight of the portfolio decreased in the industries where VaR increased over time. The higher share of an optimal portfolio belonged to the industries where stock returns had lower rates of VaR. The risk-return-ratio was employed to show that the optimal portfolio with a risk rate was measured by considering the switching regime was superior over the optimal portfolio with a risk rate extracted without considering the switching effects. To create an optimal portfolio, it is then recommended to make investments in the industries characterized by higher stability in prices and lower fluctuations in stock returns in the long run. This approach can be employed to obtain the best results from optimal portfolio preparation in the worst-case scenario of the market fluctuations.

The purpose of this study is to investigate the impact of exchange rate misalignment on inflation persistence. For this purpose, Vector Auto Regression method and Markov Switching model is used for quarterly data during 1989:4 -2014:3. The results show that, the impact of liquidity growth and exchange rate misalignment on inflation persistence is positive. On the other hand, GDP growth has a negative effect on inflation persistence. By Markov Switching model the nonlinear relationship between variables was investigated; based on Markov Switching model, quarterly inflationary environment (inflation regime) were extracted for economy of Iran, and the results show three different regimes for quarterly inflation. Markov model findings are consistent with VAR model findings. Results also show that, the impact of exchange rate misalignment on stable inflation regime is positive and the impact of exchange rate misalignment on unstable inflation regime is negative. JEL Classifications: F31, E3, D31, C22, H5

The main objective of this study was to investigate the effect of monetary policy on changes in the price of financial assets (including foreign exchange, gold and stocks) in Iranian economy. In this regard, this paper answers whether monetary policy could lead to regime changes in asset markets. To answer this question, monthly data during the years 1995 to 2017 and a combination of Markov Switching and Probit methods were used. First, using Markov Switching method, each market was divided into two high-volatility and low-volatility regimes with different average returns, and then, by a Probit model, the effect of monetary policy on the probability of markets being exposed to these regimes was studied. The results of this study show that in all three markets, the Markov Switching model offers better fit than the linear model, which indicates the occurrence of regime changes in the markets. The results of the Probit model show that monetary policy in all three markets is effective on their regime changes, and an expansionary monetary policy will strengthen the position of all three markets in the high-volatility regime with a positive average return. Also, inflation is also one of the factors affecting regime changes in all three markets. The market situation in the past period as well as the situation of other markets are among the factors that lead to regime changes in asset markets. The sanctions imposed on Iran's economy in the currency and gold markets are among the factors that have strengthened the likelihood of changing the regime of these two markets to a volatile environment.