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Risk management plays an important role in controlling loss of stock returns. We deals in this thesis with Value-at-Risk (VaR) as a tool to calculate the loss or risk. VaR is defined as a maximum loss of a portfolio at a given level of confidence. In fact, VaR is a function of volatility . Predicted...
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id-itb.:151762017-09-27T11:43:10Z#TITLE_ALTERNATIVE# ALFA MARESHA P. (NIM : 10107028); Pembimbing Tugas Akhir : Khreshna I.A. Syuhada, M.Sc. PhD, BIRGITTA Indonesia Final Project INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/15176 Risk management plays an important role in controlling loss of stock returns. We deals in this thesis with Value-at-Risk (VaR) as a tool to calculate the loss or risk. VaR is defined as a maximum loss of a portfolio at a given level of confidence. In fact, VaR is a function of volatility . Predicted VaR is more appropriate if calculated under normal situation. We do, however, investigate VaR during 2008 global market crisis. To do so, we calculate and compare VaR for three periods of time: pre-crisis, during crisis, and whole period. Volatility model we have used is Autoregressive- <br /> <br /> <br /> Autoregressive Conditional Heteroscedastic or AR-ARCH model of orde one. Its parameters are estimated by using Maximum Likelihood approach. Numerical anlaysis of NYSE and IHSG data show that the largest predicted VaR appears during pre-crisis in comparison to other two periods. text |
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Risk management plays an important role in controlling loss of stock returns. We deals in this thesis with Value-at-Risk (VaR) as a tool to calculate the loss or risk. VaR is defined as a maximum loss of a portfolio at a given level of confidence. In fact, VaR is a function of volatility . Predicted VaR is more appropriate if calculated under normal situation. We do, however, investigate VaR during 2008 global market crisis. To do so, we calculate and compare VaR for three periods of time: pre-crisis, during crisis, and whole period. Volatility model we have used is Autoregressive- <br />
<br />
<br />
Autoregressive Conditional Heteroscedastic or AR-ARCH model of orde one. Its parameters are estimated by using Maximum Likelihood approach. Numerical anlaysis of NYSE and IHSG data show that the largest predicted VaR appears during pre-crisis in comparison to other two periods. |
| format |
Final Project |
| author |
ALFA MARESHA P. (NIM : 10107028); Pembimbing Tugas Akhir : Khreshna I.A. Syuhada, M.Sc. PhD, BIRGITTA |
| spellingShingle |
ALFA MARESHA P. (NIM : 10107028); Pembimbing Tugas Akhir : Khreshna I.A. Syuhada, M.Sc. PhD, BIRGITTA #TITLE_ALTERNATIVE# |
| author_facet |
ALFA MARESHA P. (NIM : 10107028); Pembimbing Tugas Akhir : Khreshna I.A. Syuhada, M.Sc. PhD, BIRGITTA |
| author_sort |
ALFA MARESHA P. (NIM : 10107028); Pembimbing Tugas Akhir : Khreshna I.A. Syuhada, M.Sc. PhD, BIRGITTA |
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https://digilib.itb.ac.id/gdl/view/15176 |
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