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1	Examining GARCH forecasts for Value-at-Risk predictions Lindholm, Dennis, Östblom, Adam January 2014 (has links) In this thesis we use the GARCH(1,1) and GJR-GARCH(1,1) models to estimate the conditional variance for five equities from the OMX Nasdaq Stockholm (OMXS) stock exchange. We predict 95% and 99% Value-at-Risk (VaR) using one-day ahead forecasts, under three different error distribution assumptions, the Normal, Student’s t and the General Error Distribution. A 500 observations rolling forecast-window is used on the dataset of daily returns from 2007 to 2014. The empirical size VaR is evaluated using the Kupiec’s test of unconditional coverage and Christoffersen’s test of independence in order to provide the most statistically fit model. The results are ultimately filtered to correspond with the Basel (II) Accord Penalty Zones to present the preferred models. The study finds that the GARCH(1,1) is the preferred model when predicting the 99% VaR under varying distribution assumptions. Value-at-Risk Unconditional coverage Christoffersen’s GJR-GARCH
2	Value at Risk: GARCH vs. modely stochastické volatility: empirická studie / Value at Risk: GARCH vs. Stochastic Volatility Models: Empirical Study Tesárová, Viktória January 2012 (has links) The thesis compares GARCH volatility models and Stochastic Volatility (SV) models with Student's t distributed errors and its empirical forecasting per- formance of Value at Risk on five stock price indices: S&P, NASDAQ Com- posite, CAC, DAX and FTSE. It introduces in details the problem of SV models Maximum Likelihood examinations and suggests the newly devel- oped approach of Efficient Importance Sampling (EIS). EIS is a procedure that provides an accurate Monte Carlo evaluation of likelihood function which depends upon high-dimensional numerical integrals. Comparison analysis is divided into in-sample and out-of-sample forecast- ing performance and evaluated using standard statistical probability back- testig methods as conditional and unconditional coverage. Based on empirical analysis thesis shows that SV models can perform at least as good as GARCH models if not superior in forecasting volatility and parametric VaR. 1
3	市場風險值管理之應用分析以某金融控股公司為例 / The analysis of Market Risk VaR management ：the case of financial holding company 周士偉, Chou, Jacky Unknown Date (has links) 2008年次貸風暴橫掃全球金融市場，Basel II制度歷經多年的實施，卻無法有效防阻金融風暴的發生。觀察2008已採用內部模型法之主要國際金融機構之年報，亦發現採用蒙地卡羅模擬法之代表銀行『德意志銀行』於該年度竟發生了35次穿透，市場風險管理到底出了什麼問題？這是被極度關心的現象，產官學界也對此現象提出了許多議題。2012年的現在，次貸的風暴尚未遠去，新的歐債危機也正在蔓延，若金融風暴再次來臨，市場風險管理是否能克服次貸風暴後所凸顯的缺失，市場風險管理的價值除被動管理外，是否還可以進階到主動預警，以作為經營決策的重要參考資訊？這些都是國內金融機構需積極面對的急迫的市場風險管理議題。個案金控的市場風險管理機制致力於解決次貸以來所凸顯的市場風險管理議題、提升市場風險衡量的精準度、擴大市場風險管理之應用範圍，並將市場風險管理的價值由被動管理角色進階到主動預警角色，以期作為經營決策的重要參考。經過多年的淬煉，其發展理念與經驗應具相當參考價值，故本論文以個案金融控股公司(以下簡稱個案金控)之實務經驗進行個案研究，除分析個案金控市場風險管理機制的基礎架構外，也將研究重心放在個案金控如何在此基礎架構下，開發多種進階市場風險量化管理功能。本論文除研究個案金控如何完善市場風險值量化機制外，也對各量化功能的實施結果進行分析，以期研究成果可更客觀的作為其他金融控股公司未來發展進階市場風險衡量機制之參考。市場風險風險值經濟價值信託基金操作風險風險因子一般風險值壓力風險值條件風險值增額風險值邊際風險值變異數-共變異數法歷史模擬法蒙地卡羅模擬法隱含波動度厚尾順景氣循環標準法內部模型法市場流動性風險外生性市場流動性風險內生性市場流動性風險流動性調整後風險值壓力測試回溯測試 Market Risk Value at Risk Economic Value Active Management VaR Tracking Error Risk Factor Common VaR Stressed VaR Conditional VaR Incremental VaR Marginal VaR Variance-Covariance Approach Historical Simulation Approach Monte Carlo Simulation Approach Fat Tail Time-Varying Volatility pro-cyclical Standardized Approach Internal Models Approach Implied Volatility Generalized Error Distribution Bernoulli trial test Likelihood ratio test Unconditional Coverage Test Conditional Coverage Test Backward Looking Model Forward Looking Model Forecasting VaR Pearson Correlation Cholesky decomposition Kendall Correlation Copula method Future Volatility Disconnection Market Liquidity Risk Exogenous Market Liquidity Risk Endogenous Market Liquidity Risk Liquidity-adjusted VaR Stress Test Backtesting Net Interest Income Re-pricing Model Maturity Model Duration Model

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