Chan Wing-Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 43-45). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Estimation of Volatility --- p.5 / Chapter 2.1 --- A revisit to the RiskMetrics --- p.6 / Chapter 2.2 --- Predicting Multiple-period of Volatilities --- p.7 / Chapter 2.3 --- Performance Measures --- p.11 / Chapter 2.4 --- Nonparametric Estimation of Quantiles --- p.13 / Chapter 3 --- Univariate Prediction --- p.15 / Chapter 3.1 --- Piecewise Constant Technique --- p.16 / Chapter 3.2 --- Piecewise Linear Technique --- p.22 / Chapter 4 --- Bivariate Prediction --- p.27 / Chapter 4.1 --- Model Selection --- p.28 / Chapter 4.2 --- Piecewise Linear with Discontinuity --- p.29 / Chapter 4.3 --- Piecewise Linear Technique --- p.35 / Chapter 5 --- Conclusions --- p.41 / Bibliography --- p.43

published_or_final_version / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy

published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy

Thesis (MComm (Statistics and Actuarial Science))--University of Stellenbosch, 2005. / Financial institutions invest in financial securities like equities, options and government bonds. Two measures, namely return and risk, are associated with each investment position. Return is a measure of the profit or loss of the investment, whilst risk is defined as the uncertainty about return. A financial institution that holds a portfolio of securities is exposed to different types of risk. The most well-known types are market, credit, liquidity, operational and legal risk. An institution has the need to quantify for each type of risk, the extent of its exposure. Currently, standard risk measures that aim to quantify risk only exist for market and credit risk. Extensive calculations are usually required to obtain values for risk measures. The investments positions that form the portfolio, as well as the market information that are used in the risk measure calculations, change during each trading day. Hence, the financial institution needs a business tool that has the ability to calculate various standard risk measures for dynamic market and position data at the end of each trading day. SAS Risk Dimensions is a software package that provides a solution to the calculation problem. A risk management system is created with this package and is used to calculate all the relevant risk measures on a daily basis. The purpose of this document is to explain and illustrate all the steps that should be followed to create a suitable risk management system with SAS Risk Dimensions.

Sit, Tony. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 49-51). / Abstracts in English and Chinese. / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Background --- p.4 / Chapter 2.1 --- Approaches to Risk Measurement --- p.4 / Chapter 2.2 --- Is VaR a “Good´ح Risk Measure? --- p.9 / Chapter 2.3 --- "Efficient Capital Market, Random Walk and Unit Root" --- p.11 / Chapter 3 --- Historical VaR and Limitations --- p.17 / Chapter 3.1 --- Regression Analysis --- p.18 / Chapter 3.2 --- A Possible Artifact --- p.19 / Chapter 4 --- "Parametric VaR with GARCH(1,1)" --- p.27 / Chapter 4.1 --- "GARCH(1,1), a Conditional Heteroscedastic Model" --- p.27 / Chapter 4.2 --- RiskMctrics VaR --- p.29 / Chapter 5 --- VaR with Regression Quantiles --- p.34 / Chapter 5.1 --- Quantilc Regression --- p.35 / Chapter 5.1.1 --- "Quantiles, Ranks and Optimisation" --- p.35 / Chapter 5.2 --- CAViaR --- p.39 / Chapter 5.2.1 --- The model --- p.39 / Chapter 5.2.2 --- Empirical Studies --- p.42 / Chapter 6 --- Conclusion and Future Research --- p.46 / Bibliography --- p.48