Risk management is a core activity by financial institutions. There are different types of financial risks, e.g. market, credit, operational, model, liquidity, business, etc. Managing these risks to minimize potential losses is essential to ensure viability and good reputations for financial institutions. Therefore, it is necessary to have an accurate model and a proper measurement that describes the risk. / In this thesis, we model the risks with proper measurement, like Value-at-risk (VaR) and Conditional Value-at-Risk (CVaR). The dependence between risks is described by the so-called copula, which can connect marginal distributions with joint distribution. Among many popular copulas, we find a proper copula to describe the correlations between risks and between financial data. Portfolio optimization problems with VaR and CVaR as risk measurement are solved and numerical results indicate that the model can describe the real world risk very well. In addition, we propose another method, called Independent Component Analysis. By linear transformation, we obtain models for independent components with the same optimal solution. The time of solving the new models is highly reduced with the same accuracy.
| Identifer | oai:union.ndltd.org:CHENGCHI/U0003463849 |
| Creators | Yang, Song. |
| Publisher | North Carolina State University. |
| Source Sets | National Chengchi University Libraries |
| Detected Language | English |
| Type | text |
| Rights | Copyright © nccu library on behalf of the copyright holders |
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