THE PREDICTION OF BROWNIAN MOTION RISK MEASUREMENT WITH GARCH(1,1) MODEL
Risk is frequentlyinterpreted as the possibility of a loss. Oftentimes, loss occurs not only due to one risk but also due to several risks which is referred to as aggregate risk. In this thesis, aggregate risk is modeled by a Brownian motion.The aggregate risk of Brownian motion at time t is the...
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id-itb.:425292019-09-20T11:00:50ZTHE PREDICTION OF BROWNIAN MOTION RISK MEASUREMENT WITH GARCH(1,1) MODEL Mahrani, Dwi Indonesia Theses aggregate risk, Brownian motion, geometry Brownian Motion, Asset’s returns, GARCH(1,1), Value-at-Risk. INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/42529 Risk is frequentlyinterpreted as the possibility of a loss. Oftentimes, loss occurs not only due to one risk but also due to several risks which is referred to as aggregate risk. In this thesis, aggregate risk is modeled by a Brownian motion.The aggregate risk of Brownian motion at time t is the sumof asset’s returns until t-th time. Financial asset prices data generally has high volatility so that the the asset’s returns at time t are modeled by volatility model, that is generalized Autoregressive Conditional Heteroskedasticity (GARCH). The amount of risk can be determined by the prediction of risk measure. The risk measure used is Value-at-Risk (VaR). After the prediction of risk measure is done, the VaR accuracy test is done next using the coverage probability. The prediction with VaR is considered accurate if the proportion of VaR occurrence is close to the given level of confidence, that is alpha. text |
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Risk is frequentlyinterpreted as the possibility of a loss. Oftentimes, loss occurs
not only due to one risk but also due to several risks which is referred to as
aggregate risk. In this thesis, aggregate risk is modeled by a Brownian motion.The
aggregate risk of Brownian motion at time t is the sumof asset’s returns until t-th
time. Financial asset prices data generally has high volatility so that the the asset’s
returns at time t are modeled by volatility model, that is generalized
Autoregressive Conditional Heteroskedasticity (GARCH). The amount of risk can
be determined by the prediction of risk measure. The risk measure used is
Value-at-Risk (VaR). After the prediction of risk measure is done, the VaR
accuracy test is done next using the coverage probability. The prediction with VaR
is considered accurate if the proportion of VaR occurrence is close to the given
level of confidence, that is alpha. |
| format |
Theses |
| author |
Mahrani, Dwi |
| spellingShingle |
Mahrani, Dwi THE PREDICTION OF BROWNIAN MOTION RISK MEASUREMENT WITH GARCH(1,1) MODEL |
| author_facet |
Mahrani, Dwi |
| author_sort |
Mahrani, Dwi |
| title |
THE PREDICTION OF BROWNIAN MOTION RISK MEASUREMENT WITH GARCH(1,1) MODEL |
| title_short |
THE PREDICTION OF BROWNIAN MOTION RISK MEASUREMENT WITH GARCH(1,1) MODEL |
| title_full |
THE PREDICTION OF BROWNIAN MOTION RISK MEASUREMENT WITH GARCH(1,1) MODEL |
| title_fullStr |
THE PREDICTION OF BROWNIAN MOTION RISK MEASUREMENT WITH GARCH(1,1) MODEL |
| title_full_unstemmed |
THE PREDICTION OF BROWNIAN MOTION RISK MEASUREMENT WITH GARCH(1,1) MODEL |
| title_sort |
prediction of brownian motion risk measurement with garch(1,1) model |
| url |
https://digilib.itb.ac.id/gdl/view/42529 |
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1821998631672086528 |