Monetary economy
Soheil Roudari; Masoud Homayounifar; Mostafa Salimifar
Abstract
In this research, the role of nominal exchange rate volatility and business cycles on the banking nonperforming loans was investigated by using Markov-Switching model during 2005-2018 using seasonal data. Business cycles were extracted from GDP by using the Hodrick Prescott filter. Also, the wavelet ...
Read More
In this research, the role of nominal exchange rate volatility and business cycles on the banking nonperforming loans was investigated by using Markov-Switching model during 2005-2018 using seasonal data. Business cycles were extracted from GDP by using the Hodrick Prescott filter. Also, the wavelet transform model was used to extract nominal exchange rate fluctuations. The results showed that the exchange rate volatility varies in different periods of time and in longer period of time, the foreign exchange rate volatility has a greater negative and significant effect on nonperforming loans of banking network. It shows a dependence of government on banking network. Also, the impact of business cycles depends on the nonperforming loans regime. The sustainability of low regime is bigger than high regime. The results also show that the impact of value added of different sectors of economy varies in different regimes of nonperforming loans. These results indicate that banking system should take into account the value added of different sectors of economy and nonperforming loans regimes which could decrease nonperforming loans.
Abbas Kalantari; Navid Khalil Paktinat
Volume 19, Issue 58 , April 2014, , Pages 183-206
Abstract
In this paper, the effect of trade volume on TEPIX index is investigated based on bull and bear cycles of Tehran stock Exchange (TSE) using nonlinear Markov-Switching model. In this regards, monthly data of TEPIX and trade volume of TSE is used for the period of the first month of 1381 to the ninth month ...
Read More
In this paper, the effect of trade volume on TEPIX index is investigated based on bull and bear cycles of Tehran stock Exchange (TSE) using nonlinear Markov-Switching model. In this regards, monthly data of TEPIX and trade volume of TSE is used for the period of the first month of 1381 to the ninth month of 1391. The results show that, there is significant nonlinear relationship between trade volume and TEPIX index. Trade volume has positive and significant impacts on TEPIX in both bull and bear regimes but these effects is higher in bull regime. Based on the results, it is expected that increase in trade volume leads to growth in TEPIX index in both bull and bear regimes. However, comparing results with historical evidences shows that Markov-switching model properly fits the bull and bear cycles of TSE. On the other hand, the results of Robustness tests emphasis adequacy and good performance of Markov-Switching model. Based on MAE criterion, the Markov-Switching model has more accurate performance for in-sample fitting and out-of-sample forecasting than ARIMA and VAR models.
