VOLATILITY CHANGES THROUGH MARKOV SWITCHING ARCH MODEL FOR VALUE-AT-RISK PREDICTION
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...
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| المؤلف الرئيسي: | |
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| التنسيق: | Theses |
| اللغة: | Indonesia |
| الموضوعات: | |
| الوصول للمادة أونلاين: | https://digilib.itb.ac.id/gdl/view/33940 |
| الوسوم: |
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| المؤسسة: | Institut Teknologi Bandung |
| اللغة: | Indonesia |
| الملخص: | 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. |
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