Spelling suggestions: "subject:"risk management -- atatistical methods"" "subject:"risk management -- astatistical methods""
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Estimation of value at risk using parametric regression techniques.January 2003 (has links)
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
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A robust non-time series approach for valuation of weather derivativesand related productsFriedlander, Michael Arthur. January 2011 (has links)
published_or_final_version / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy
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On a subjective modelling of VaR: fa Bayesianapproach蕭偉成, Siu, Wai-shing. January 2001 (has links)
published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy
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Modelling market risk with SAS Risk Dimensions : a step by step implementationDu Toit, Carl 03 1900 (has links)
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.
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Statistical analysis of value-at-risk (VaR).January 2008 (has links)
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
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