Combination of volatility and Markov-switching models for financial crisis in Indonesia based on real exchange rate indicators
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disasters and ARCH model does not take into account the structural changes that occur in such volatility.
Hamilton 1989 introduced Markov switching models as an alternative modelling of time series data that undergo structural changes. Hamilton combined Markov switching and
autoregressive models resulting in a Markov switching autoregressive models SWAR. Hamilton Susmel 1994 introduced a model that combined ARCH models and
Markov_Switching models then called Markov Switching ARCH SWARCH. SWARCH model can explain changes in the structure and illustrates the volatility. Some researchers have
applied SWARCH model to detect crisis that occur in a country. Among these researchers were Chen Lin 2000 which apply SWARCH models to identify the stock market volatility
in Taiwan. Then Chang et al. 2010 also apply SWARCH models to identify the volatility of the stock market and the exchange rate in Korea as well as the financial crisis global.
This research will be carried out modeling of the financial crisis in Indonesia is based on indicators of the real exchange rate using combined of volatility and Markov switching
models. The real exchange rate data that indicated heteroscedasticity and undergo structural changes can be modeled by SWARCH model of two and three states.
2. Materials and methods
This research uses monthly real exchange rate data of the January 1990 to December 2014 periods. Data are obtained from the International Financial Statistics IFS.
In conditions of crisis or impending crisis turmoil, the financial data unlucky in particular real exchange rate will experience high fluctuation and structural changes. When
this happens, the combined of volatility and structural changes models are suitable for use. If the real exchange rate does not have heteroscedasticity, then SWAR model is used.
However, when real exchange rate has heteroscedasticity, SWARCH models is more suitable for use. High order on the SWARCH models can lead to biased interpretations on the model,
so SW-GARCH model can be used to overcome this problem 1996. Some lack of proper economic policy or the contagion from abroad will have an impact on the real exchange rate,
this information is often referred to as a bad-new. While the precise economic policy or foreign trust towards Indonesia will give a good impact on the value of the real exchange
rate, information like this is often referred to as a good-new. The existence of bad-new and good-new that does not contribute to balanced then it is used SW- EGARCH models 2007.
Crisis situation can be seen from the inferred probabilities generated by the models: SWARCH, SW-GARCH and EGARCH. According to Hamilton 1989, inferred probabilities
written as
x
j
Væ = 2|T W 1 − x
j
Væ = 1|T W
for two states and
x
j
Væ 3|T W 1 − x
j
Væ = 1|T W x
j
Væ 2|T W
for three states. In the two states conditions, the period of data showing the probabilities inferred value
of more than 0.5 can be said to be in volatile conditions or can indicate the occurrence of a crisis Hamilton, 1989. While on a three states condition, the period of data showing the
probabilities inferred value between 0.4 to 0.6 is assumed to be in a state of moderate volatility, and less than 0.4 is assumed to be at the low volatility conditions and more than
0.6 in a state of high volatility Hermosillo Hesse, 2009.
Sugiyanto, E. Zukhronah
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3. Results and discussion
