ARCH MODEL: VALUE-AT-RISK AND COPULA-BASED PREDICTION - Nur'aini, Risti
Modelling a risk to a stochastic model is commonly used in risk management. One of stochastic models which can capture heteroscedacticity property is Autoregressive Conditional Heteroscedastic (ARCH) model. In this thesis, some alternative representation of ARCH model are constructed. These new representation giving us a new perspective about ARCH model. Value-at-Risk is a risk measure that still can be developed. This thesis will study some alternative calculation of VaR, using correlation measure between two random variables. As a innovation, we will try to calculating VaR using Copula. Copula is a distribution function model for two or more random variables. There is many kind of Copula model. Then, Akaike Information Criterion will be used to selecting the best Copula model to data. Alternative calculation of VaR then applied to two stock return. As the result, correlation-based VaR giving a more effective and accurate VaR prediction.