scholarly journals Selection Criteria in Regime Switching Conditional Volatility Models

Econometrics ◽  
2015 ◽  
Vol 3 (2) ◽  
pp. 289-316 ◽  
Author(s):  
Thomas Chuffart
Author(s):  
Serge Darolles ◽  
Gaëlle Le Fol ◽  
Christian Francq ◽  
Jean-Michel Zakoïan

2019 ◽  
Vol 16 (2) ◽  
pp. 98-103
Author(s):  
Aisyah Zahrotul Hidayah ◽  
Sugiyanto Sugiyanto ◽  
Isnandar Slamet

The banking crisis reflects the liquidity crisis and bankruptcy of banks in the financial system. The financial crisis that occurred in mid-1997 resulted in a financial crisis that had a severe impact on the Indonesian economy. This made it aware of the importance of building a financial crisis early detection system to prepare for a crisis. The crisis occurs due to several macroeconomic indicators undergoing structural changes (regimes) and contain very high fluctuations. Combined volatility models and Markov regime switching are very suitable for explaining crises. The M2/international reserves indicator from 1990 to 2018 was used to build a crisis model. The results showed that the Markov regime switching autoregressive conditional heteroscedasticity model MRS-ARCH(2,1) could explain the crisis that occurred in mid-1997. Based on this model, in the future the crisis might occur if the M2/international reserves indicator decreased minimum of 13%


2019 ◽  
Vol 9 (1) ◽  
pp. 69-79 ◽  
Author(s):  
Carl Hope Korkpoe ◽  
Nathaniel Howard

We adopt a granular approach to estimating the risk of equity returns in sub-Saharan African frontier equity markets under the assumption that, returns are influenced by developments in the underlying economy. Four countries were studied – Botswana, Ghana, Kenya and Nigeria. We found heterogeneity in the evolution of volatility across these markets and also that two-regime switching volatility models describe better the heteroscedastic returns generating processes in these markets using the deviance information criteria. We backtest the results to assess whether the models are a good fit for the data. We concluded that, the selected models are the most suitable for predicting the volatility of future returns in the markets studied. 


Author(s):  
Yun Bao ◽  
Carl Chiarella ◽  
Boda Kang

This chapter proposes an auxiliary particle filter algorithm for inference in regime switching stochastic volatility models in which the regime state is governed by a first-order Markov chain. It proposes an ongoing updated Dirichlet distribution to estimate the transition probabilities of the Markov chain in the auxiliary particle filter. A simulation-based algorithm is presented for the method that demonstrates the ability to estimate a class of models in which the probability that the system state transits from one regime to a different regime is relatively high. The methodology is implemented in order to analyze a real-time series, namely, the foreign exchange rate between the Australian dollar and the South Korean won.


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