VOLATILITY CHANGES THROUGH MARKOV SWITCHING ARCH MODEL FOR VALUE-AT-RISK PREDICTION - Nugroho, Setyo
Markov Swithcing Autoregressive Conditional Heteroscedastic (MSARCH) model provides a description of return uctuation for low and high volatilities. Return behavior with volatility changes is interesting topic, in particular for Value-at-Risk (VaR) prediction. In this thesis, we employ a Markov chain to compute the transition probability of volatility changes. Then, a volatility model MSARCH of order (p,1) is used to predict risk measure. Simulation results show that MSARCH(1,1) dominates in calculating VaR prediction.