Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 142 for “"Value-at-Risk"”.
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Industry value at risk in Australia
Value at Risk (VaR) models have gained increasing momentum in recent years. Market VaR is an important issue for banks since its adoption as a primary risk metric in the Basel Accords and the requirement that it is calculated on a daily basis. Credit risk modelling has become increasingly important …
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Value at risk and the distortion operator
VaR is a popular measure for benchmarking market risk based on price or return fluctuations of instruments among institutions. Calculation of VaR depends very much on the model explaining the price changes and volatility of the underlying assets. However, theoretical models can be very unrealistic …
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Application of GARCH Type Models in Forecasting Value at Risk
Dynamic risk management requires the risk measures to adapt to information at different times, such that this dynamic framework takes into account the time consistency of risk measures interrelated at different times. The value-at-risk (VaR) is one of the most well-known downside risk measures due …
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Informativeness of Value-at-risk Disclosure in the Banking Industry
Following the Basel Committee’s advocacy of value-at-risk (VaR) disclosure in external reports of financial institutions, the U.S. Securities and Exchange Commission issued Financial Reporting Release No. 48 to permit VaR disclosure as one of the most important disclosure approaches for market-risk …
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Variance reduction techniques for estimating quantiles and value-at-risk
… contexts. In finance, quantiles are called values-at-risk (VARs), and they are widely used in the financial industry to measure portfolio risk. When the cumulative distribution function is unknown, the quantile can not be computed exactly and must be estimated. In addition to computing a …
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Refining Value-at-Risk estimates: An Extreme Value Theory Approach
This thesis proposes new approaches to Value-at-Risk estimation using (1) Multivariate GARCH Dynamic Conditional Correlation volatility model with skewed Student’s-t distributions, (2) Bayesian GARCH model with Student’s-t distribution, and (3) Bayesian Markov-Switching GJR-GARCH model with skewed …
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Estimation of value-at-risk and expected shortfall using copulas
Includes bibliographical references (leaves 76-77).
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Volatility Forecasting and Value-at-Risk: An Application to Cattle Feeding
… error criteria, the overall conclusion of the volatility forecasting exercise mirrors that found in the literature: performance of any volatility forecast is both data and horizon specific. However, composite techniques, especially simple composites that combine both conditional time series and …
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Estimating value at risk and expected shortfall: a kalman filter approach
Calculating Value-at-Risk (VaR) to estimate the maximum loss a portfolio may incur at a given confidence level and over a specified time has undergone several adaptations, iterations, and additions since its inception in 1994. In 2013, the Basel Committee on Banking Supervision (BCBS) replaced VaR …
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Quantifying Model Risk in Option Pricing and Value-at-Risk Models
… use models in order to price, hedge and measure risk. These models are reliant on assumptions and are prone to ”model risk”. Increased innovation in complex financial products has lead to increased risk exposure and has spurred research into understanding model risk and its underlying factors. …
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Evaluating value at risk models: an application to the Johannesburg Stock Exchange
The management of market risk is an essential determinant of the stability of a financial institution, and by extension, of the overall financial system. There are various variables which impact on the accuracy of a market risk management system. For various reasons which are discussed in this …
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Application of extreme value theory to the calculation of value-at-risk
… applicability of published EVT-based VaR calculation methods to the South African market. Two methods were tested on a hypothetical portolio of South African stocks, using the standard backtesting technique.
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An Application of Generative Adversarial Networks to One-Dimensional Value-at-Risk
A generative adversarial network (GAN) is an implicit generative model made up of two neural networks. This minor dissertation applies GANs to recover target statistical distributions. GANs have a distinctive training architecture designed to create examples that reproduce target data samples. …
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Analytical Estimation of Value at Risk Under Thick Tails and Fast Volatility Updating
Despite its recent advent, value at risk (VaR) became the most widely used technique for measuring future expected risk for both financial and non-financial institutions. VaR, the measure of the worst expected loss over a given horizon at a given confidence level, depends crucially on the …
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Portfolio risk analysis : conditional estimates of value-at-risk and international volatility spillovers
… concerned with the establishment of more accurate and easily implemented methods of modelling portfolio Value-at-Risk (VaR) . We establish this by taking the view that unconditional volatility estimates are inappropriate in VaR analysis. To provide the motivation and the justification for …
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Acceleration of Monte Carlo Value at Risk Estimation Using Graphics Processing Unit (GPU)
"Value at Risk (VaR) is one of the most popular tools used to estimate the exposure to market risks, and it measures the worst expected loss at a given confidence level. Monte Carlo simulation is one of the best methods to calculate VaR and it is widely used in financial industry. Unfortunately, it …
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Application of Supply Chain Risk Management through visualization and value-at-risk quantification
Supply Chain Risk Management ("SCRM") is often discussed in business and academia but is still underdeveloped as a practical tool. Many studies have examined the effects of supply chain disruptions, and many studies have also produced tools for mitigating risk. However, there is still a need for an …
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