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Showing 3 results for Exchange Rate
Hamidreza Mostafaei, Maryam Safaei,
Volume 3, Issue 2 (3-2010)
Abstract
In 2002 the enforcement on policy unification of exchange rate caused dramatic decrease in the nominal price of Iran's Rial against U.S.dollar per on unit.For this reason due to the existence of unexpected and large change we cannot use the linear time series models for surveying the fluctuations of the rate of Iran's Rial change against U.S. dollar per on unit. In this paper we compare Self-Exciting threshold autoregressive and Markov switching autoregressive model. then it will be show that only the Markov switching autoregressive model being able to show the behaviors of Iran's exchange rate.
Maryam Safaei,
Volume 5, Issue 1 (9-2011)
Abstract
This paper offers a method of the estimation of the transition probability for the behaviors of financial time series by Markov Switching Autoregressive model. Using this model, the behaviors of fluctuations of exchange rate form two regimes low and high changes rate are considered. Results of prediction show that the persistence probability of regimes will be decreased. Thus, the probability of transition to other regime will be increased if process were in a specific regime.
Gholam Ali Parham, Parisa Masjedi,
Volume 7, Issue 2 (3-2014)
Abstract
One of the issues in reviewing the performance of a financial market is existence of long-term memory. Since for a financial time series, we may find this feature in the volatility. So reviewing in volatility has been considered by many economists. A common method for identification and modeling of long-term memory in the volatility is to use FIGARCH models. In this paper, we identify and model long-term memory in the data exchange rates volatility (EUR/IRR). According to the statistical properties of skewness, heavy tail and excess kurtosis of data, assuming normal residuals being rejected and therefore cannot identify model by using common methods. The data structure looks NIG distribution is a good choice for the distribution of residuals. Hence with this assumption, we again identify model. The results show a good selection for data is FIGARCH-NIG model.
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