markov-switching model

providing a model for better identifying the pattern of exchange rate effects on the capital market and forecast the next one and two years of the Iranian capital market is The purpose of this study. In this regard, using 4 specification criteria (Maximum likelihood, Akaik, Schwartz and Hannan Quinn) we compare the fitness of two-regime Markov switching Model(2R-MSM) with three-regime Markov switching model(3R-MSM) relating to exchange rate fluctuations and the relationship between exchange rate fluctuations and capital market. Afterward we forecast the regime for relationship of exchange rate and capital market. results show in recognizing the pattern of exchange rate effects on the capital market, 3R-MSM outperforms 2R-MSM. According to the research findings, if the foreign exchange market fluctuates severely, an increase in the exchange rate will certainly increase capital market return, but when foreign exchange market is almost stable, the effect of this variable on the capital market can be positive or negative. According to the estimated probabilities, in the coming years we are expected to see a positive effect of the exchange rate on the capital market in a low volatility environment.

Because of the sanctions imposed on Iran and the reduction of foreign exchange earnings, the capital market has undergone significant changes. This study tries to provide the transition mechanism of foreign exchange crisis on the stock market and compare with other emerging countries. In order to obtain the results, using the monthly data of the period of 2009:01–2020:03 and using the nonlinear Markov switching approach. For this purpose, among the various modes of Markov switching model, MSIAH (2) –VAR (2) has been selected. The empirical findings of the study show that only in a regime with high fluctuations, the increase in the exchange rate has increased the stock index while the stock index has no impact on the exchange rate. In addition, the results indicate that the sustainability of the stock index in the regime with the low fluctuations (first regime) was more than that of the regime with the high fluctuations (second regime).

Identifying the pattern of exchange rate fluctuations makes it easier to predict currency crises and can help prevent or reduce their harmful consequences. The present study seeks to provide a model to better identify the pattern of exchange rate fluctuations and also predict the likelihood of a currency crisis in Iran in the future. The degree of government intervention in the foreign exchange market affects how this variable fluctuates; therefore, exchange rate fluctuations in Iran (managed floating exchange rate system) can’t be similar to exchange rate fluctuations in developed countries (floating exchange rate system). In developed countries, the Markov switching 2-regime model is mainly used to explain exchange rate fluctuations and predict currency crises, and this is in line with their currency system. Despite the large differences in the exchange rate fluctuations of Iran and developed countries, domestic researchers have also used Markov's 2-regime model to identify the pattern of Rial changes. using 3 criteria: maximum likelihood, Akaik, and Hanan Quinn, we show that for detecting the pattern of rial changes, the Markov switching 3-regime model is better than Markov switching 2-regime model. The results also show that 2-regime model fits better with euro and pound fluctuations. Forecasts show that the probability of Iran’s presence in the Currency Crisis Regime in the next one or two years is decreasing and the probability of Relative Stable Regime is increasing. Therefore, we predict that in the coming years, Iran will enter a new period of Relative Stable Regime. The present study has been conducted in the period of 1387 to 1400.

The present study tries to investigate the impact of the exchange rate fluctuations on the car stock index in Tehran Stock Exchange using the monthly data of the period of 1387:10-1398:12 and using the nonlinear Markov switching approach. For this purpose, among the various modes of Markov switching model, MSIAH(3) -VAR(2) has been selected. The empirical findings of the study show that only in a regime with high fluctuations, the exchange rate is the causal relationship of the car stock index and the increase in the exchange rate has increased the car stock index while the car stock index has no impact on the exchange rate. In addition, the results indicate that the sustainability of the car stock index in the regime with the very low fluctuations (first regime) was more than that of the regime with the low fluctuations (second regime) and that of the regime with the high fluctuations (third regime).

In the research, Explain the role of financial and economic variables on stock returns With Markov-switching Model.  Based on information collected from the two codal.ir and TSE.ir websites, availability and momentum. contrarian strategies have been dealt with in the market. From among 318 companies from those listed in Tehran Stock Exchange for the period of 2011-2017 and based on restrictions, 108 companies have been selected as research samples.Then, using various statistical models, three statistical models of autoregressive time series (with no help from auxiliary variable), linear regression, and Markov-switching have been applied. Using the model’s goodness of fit criterion, these three models have been compared and the best one has been selected. Based on selected model, stock trading strategy for the next 12 months has been predicted. To collect data and perform statistical analyses, Excel spread sheet, as well as SPSS and R software packages have been used.According to the findings, from among minor variables (base volume, institutional investment, and free float) and major variables (currency and inflation rates), only three variables from minor types (base volume, institutional investment, and free float) have been effective on stock trading strategy; and, these variables can be used as auxiliary variables to predict return on stock and to specify stock trading strategy in future, as a result.

The main objective of this paper is to identify the factors affecting the business cycle in Iran through analyzing financial market indicators including, money supply, loans and deposits of banks (from the money market), and the stock price index (from the capital market).

First, using Hodrick Prescott filter, we extract the business cycles. Then, Markov Switching model estimates the optimal interrupt, and then revealed facts regarding the business cycle, including the momentum indicators, their relative variability, and their stability throughout the cycles between the variables of the financial market are compared. We used the Johansson coincidence test to recognize co integration. Finally, the model estimation is performed using Vector Error Correction Model (VECM).

The evaluated indices, regarding various facts about business cycles, show that money supply and loans are the two variables that can result in the mentioned cycles. The Johansen test and the Wald test respectively confirm the relation between the variables in both the long-term and short-term. Meanwhile, the variance analysis table shows that money supply and loans, each respectively cause, 13% and 9% of business cycles' fluctuations.

The results show that the stock market does not affect business cycles and its fluctuations. Meanwhile, the estimated error correction term in the model for the money market and business cycle variables is -0.55. This shows that every year 55% of imbalances present in the aforementioned relations are corrected in the following year. Hence, the equilibrium quickly moves towards a long-term equilibrium.

One of the factors that investors consider in their decisions is the return on equity. Achieving this return is possible in a situation where economic stability exists. One of the aspects of economic stability is the stability of economic policies that plays an important role in the economy of the country. So, if there is uncertainty about economic policies, this uncertainty will unconscious of the economic activists towards future economic developments, and the subsequent owners of the capital will be able to make decisions for the future, including capital and the money market and the capital market will be in trouble. Considering the importance of the issue in this paper, using the Markov Switching Model and applying annual data, we investigate the effect of economic policy uncertainty on the return on Iranian stock market during the period of 1981-2016. In this research, the variables of economic growth rate, inflation rate, unemployment rate, interest rate and liquidity growth rate are used as independent variables. In order to measure economic policy uncertainty, exchange rate fluctuations and government budget deficits are also used. The findings of the paper show that dynamic communication of uncertainty in economic policies and stock market returns is always negative, as the increase in uncertainty in economic policies significantly reduces capital market returns. Also, the relation between stock market returns and the uncertainty of nonlinear economic policies and the uncertainty about the return on capital during a period of high-fluctuation diet is stronger and more stable.

The global financial crisis has affected the advanced and developed economies. Iran's economy has been affected, like many other developing countries. In this paper, the effects of financial stress index on economic growth using linear and nonlinear models (Markov Switching) has been investigated. In this regard, after the construction of the index, at the beginning defined a production function and the impact of financial stress on economic growth in the rest of the production function variables measured in Linear method. According to the results of financial stress index in the model of the long - term and short – term negative and significant effect on economic growth. The coefficient of this index is 0.02%, which means that with an increase of 1% in the amount of this indicator, the economic growth per capita decreases by 0.20%.So financial stress an obstacle to economic growth and the variables government spending, taxes, and the rate of exchange, which as indications of how to manage the government policies in fiscal, monetary and exchange Optimally utilized. Finally, to test the hypothesis on the nonlinear method of switching Markov model has been used.The results show that, when financial stress is increasing, the effect of uncertainty on financial stress on economic growth is negative

Operational risk is not general definition and it has a unique definition for each company, which depends on the industry and the market of the company. The purpose of this research is to evaluate the effect of stochastic fluctuations on operational risk hedging European options on the S & P500 index. So this research will compare the operational risk level in the Markov Switching and Black Scholes models using the Var. The implicit volatility values for the three confidence levels of 90, 95 and 99% for different values of call options K, different maturity T, and different interest rate r were calculated for both models using S & P500 index call options information. The results of this study showed that due to higher gamma and random variation of operational risk, the coverage of transaction options in the Markov Switching Model compared to the Black Scholes Standard model was higher by using the criterion of value-at-risk op Var's coverage is at 90, 95, and 99 levels. The results show that OP VaR is inversely proportional to  ,    It also reduced doubling of the OP Variant's maturity over the agreed price. Finally, the results show that the interest rate has an asymmetric effect on OP VaR. As is clear from the figure, for K <S, the OP VaR has been lowered by rising interest rates, and for K> S interest rates have reduced OP VaR .

Nowadays, the pharmaceutical industry in terms of its relationship with public health and also the special economic value is considered one of the most important industries in the capital market. Therefore, improvement of the health sector has always been the main objective of the authorities in various countries. Hence, this paper investigates the impact of exchange rate on the pharmaceutical industry stock return in Tehran Stock Exchange by using monthly data from 2005:4 to 2016:2 and applying Markov switching non-linear approach. For this purpose, among the various modes of Markov switching model, MSIH (3) -AR (2) has been selected. The results show that in an optimal model consists of three regimes, and the exchange rate has different impact on the return of the pharmaceutical industry in different regimes. As a result; coefficients exchange rate in the first regime has negative effect, but in regimes 2 and 3 positive effect on the pharmaceutical industry stock return in the Tehran Stock Exchange. Also, the inflation rate of the health sector in the regimes 1 and 2 have a positive effect, but in the regime 3 has a negative effect on the pharmaceutical industry stock return in Tehran Stock Exchange. In addition, the results show that the sustainability of the pharmaceutical industry return in the regime with the high returns (first regime) was more than that of the regime with the little low return (second regime) and that of the regime with the high low returns (third regime). But the volatility of the pharmaceutical industry return in the third regime was more than that of the first and second regimes.

This paper examines regime shifts in TEDPIX return and volatility and the effects of positive and negative crude oil shocks and gold price fluctuations on stock market shifts behavior using Markov switching EGARCH model with Student’s t-distribution. We detect two episodes of series behavior، one relative to low mean/high variance regime namely bear state and the other to high mean/low variance regime namely bull state so that the expected duration of bull state is more than twice as long as the other one. The results show that the external variables involving positive and negative crude oil shocks and gold price fluctuations significantly impact neither on the equity return nor on transition probability between regimes but they have significant effect on the stock market volatility.