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1	Online Monitoring Systems of Market Reaction to Realized Return Volatility Liu, Chi-chin 23 July 2008 (has links) Volatility is an important measure of stock market performance. Competing securities market makers keep abreast of the pace of volatility change by adjusting the bid-ask spreads and bid/ask quotes properly and efficiently. For intradaily high frequency transaction data, the observed volatility of stock returns can be decomposed into the sum of the two components - the realized volatility and the volatility due to microstructure noise. The quote adjustments of the market makers comprise part of the microstructure noise. In this study, we define the ratio of the realized integrated volatility to the observed squared returns as the proportion of realized integrated volatility (PIV). Time series models with generalized error distributed innovations are fitted to the PIV data based on 70-minute returns of NYSE tick-to-tick transaction data. Both retrospective and dynamic online control charts of the PIV data are established based on the fitted time series models. The McNemar test supports that the dynamic online control charts have the same power of detecting out of control events as the retrospective control charts. The Wilcoxon signedrank test is adopted to test the differences between the changes of the market maker volatility and the realized volatility for in-control and out-of-control periods, respectively. The results reveals that the points above the upper control limit are related to the situation when the market makers can not keep up with the realized integrated volatility, whereas the points below the lower control limit indicate excessive reaction of the the market makers. generalized error distribution integrated volatility retrospective control chart high frequency transaction data market maker
2	Heavy-tail statistical monitoring charts of the active managers' performance Chen, Chun-Cheng 03 August 2006 (has links) Many performance measurement algorithms can only evaluate measure active managers' performance after a period of operating time. However, most investors are interested in monitoring the active managers' performances at any time, especially, when the performance is going down. So that the investors can adjust the targets and contents of their portfolios to reduce their risks. Yashchin,Thomas and David (1997) proposed to use a statistical quality control (SQC) procedure to monitor active managers' performances. In particular, they established the IR (Information Ratio) control charts under normality assumption to monitor the dynamic performances of active managers. However, the distributions of IR statistic usually possess fat tail property. Since the underlying distribution of IR is an important hypothesis in building up the control chart, we consider the heavy tail distributions, such as mixture normal and generalized error distribution to fit the IR data. Based on the fitted distribution, the IR control charts are rebuilt. By simulations and empirical studies, the remedial control charts are found to detect the shifts of active managers' performances more sensitively. Generalized error distribution Information Ratio Mixture Normal distribution CUSUM control chart fat tail performance measurement Acceptance-Rejection sampling method
3	GARCH models applied on Swedish Stock Exchange Indices Blad, Wiktor, Nedic, Vilim January 2019 (has links) In the financial industry, it has been increasingly popular to measure risk. One of the most common quantitative measures for assessing risk is Value-at-Risk (VaR). VaR helps to measure extreme risks that an investor is exposed to. In addition to acquiring information of the expected loss, VaR was introduced in the regulatory frameworks of Basel I and II as a standardized measure of market risk. Due to necessity of measuring VaR accurately, this thesis aims to be a contribution to the research field of applying GARCH-models to financial time series in order to forecast the conditional variance and find accurate VaR-estimations. The findings in this thesis is that GARCH-models which incorporate the asymmetric effect of positive and negative returns perform better than a standard GARCH. Further on, leptokurtic distributions have been found to outperform normal distribution. In addition to various models and distributions, various rolling windows have been used to examine how the forecasts differ given window lengths. Value-at-Risk GARCH GJR-GARCH EGARCH student´s t distribution generalized error distribution Kupiec´s test Chrisoffersen´s test forecast Economics and Business Ekonomi och näringsliv
4	Corrected LM goodness-of-fit tests with applicaton to stock returns Percy, Edward Richard, Jr. 05 January 2006 (has links) No description available. Goodness-of-Fit Tests Hypothesis Testing Computational Techniques Model Evaluation and Testing Density Estimation Symmetric Stable Distribution Generalized Student-t Distribution Generalized Error Distribution Mixture of Gaussian Distributions
5	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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