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1	On the Autoregressive Conditional Heteroskedasticity Models Stenberg, Erik January 2016 (has links) No description available. ARCH GARCH Volatility clustering fat tail forecasting intra-daily returns
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	限制下方風險的資產配置 / Controlling Downside Risk in Asset Allocation 簡佳至, Chien, Chia-Chih Unknown Date (has links) 由於許多資產報酬率的分配呈現厚尾的現象，因此，本文探討將最低報酬要求限制條件加入傳統的平均數╱變異數模型中，考慮在分配已知的情形下，假設資產報酬率的分配為t分配及常態分配，來求取最適的資產配置；在分配未知的情形下，利用古典Bootstrap法、移動區塊Bootstrap法及定態Bootstrap法的抽樣方法來模擬資產報酬率的分配形式，並利用模擬的資產報酬率分配求出最適的資產配置。同時，本文亦探討資產配置在風險管理上的運用，當分配已知時，若對分配參數的估計正確，則使用的最低要求報酬率就是此資產配置的涉險值，反之，若對參數的估計錯誤時，會對資產配置產生很大的影響及風險管理上的不正確；當分配未知時，利用模擬方法來產生分配，則使用的最低要求報酬率可看成是此資產配置的涉險值。實證部分選取資料分成本國及全球，研究發現對於何種分配或模擬方法的資產配置績效最好？沒有一定的結論。其原因是各種分配或模擬方法皆必須視資料的性質而定，因此，本論文的貢獻僅在建議使用厚尾分配及利用模擬方法，來符合資產報酬率呈現厚尾的現象，並利用此分配，以期在考慮最低報酬要求限制條件下的資產配置更為精確。 / The distributions of many asset returns tend to be fat-tail. This paper attempts to add the shortfall constraint in Mean-Variance Analysis. When the distribution is known, we find the optimal asset allocation under student-t distribution and normal distribution. On the other hand, we use Classical Bootstrap, Moving Block Bootstrap, and Stationary Bootstrap to stimulate the distribution of asset return, and to obtain the optimal asset allocation. We also examine the risk management of asset allocation. When we use the correct estimators of parameters under the known distribution, the threshold in shortfall constraint is the value-at-risk in asset allocation. Otherwise, if using the wrong estimators, we get the incorrect asset allocation and the improper risk management. When the distribution is unknown, using simulation to generate the distribution, the value-at-risk is the threshold. The empirical study is conducted in two parts, domestic and global asset allocation. The results cannot point out which distributions and simulations are suitable. They depend on the data’s property. The contribution of this paper is to introduce some methods to fit the fat-tail behavior of asset return in asset allocation. 資產配置厚尾 Bootstrap法下方風險涉險值最低報酬要求限制 Asset Allocation Fat-tail Bootstrap Downside Risk Value at Risk Shortfall Constraint
4	利用混合模型估計風險值的探討阮建豐 Unknown Date (has links) 風險值大多是在假設資產報酬為常態分配下計算而得的，但是這個假設與實際的資產報酬分配不一致，因為很多研究者都發現實際的資產報酬分配都有厚尾的現象，也就是極端事件的發生機率遠比常態假設要來的高，因此利用常態假設來計算風險值對於真實損失的衡量不是很恰當。針對這個問題，本論文以歷史模擬法、變異數-共變異數法、混合常態模型來模擬報酬率的分配，並依給定的信賴水準估算出風險值，其中混合常態模型的參數是利用準貝式最大概似估計法及EM演算法來估計；然後利用三種風險值的評量方法：回溯測試、前向測試與二項檢定，來評判三種估算風險值方法的優劣。經由實證結果發現： 1.報酬率分配在左尾臨界機率1％有較明顯厚尾的現象。 2.利用混合常態分配來模擬報酬率分配會比另外兩種方法更能準確的捕捉到左尾臨界機率1％的厚尾。 3.混合常態模型的峰態係數值接近於真實報酬率分配的峰態係數值，因此我們可以確認混合常態模型可以捕捉高峰的現象。關鍵字：風險值、厚尾、歷史模擬法、變異數-共變異教法、混合常態模型、準貝式最大概似估計法、EM演算法、回溯測試、前向測試、高峰 / Initially, Value at Risk (VaR) is calculated by assuming that the underline asset return is normal distribution, but this assumption sometimes does not consist with the actual distribution of asset return. Many researchers have found that the actual distribution of the underline asset return have Fat-Tail, extreme value events, character. So under normal distribution assumption, the VaR value is improper compared with the actual losses. The paper discuss three methods. Historical Simulated method - Variance-Covariance method and Mixture Normal .simulating those asset, return and VaR by given proper confidence level. About the Mixture Normal Distribution, we use both EM algorithm and Quasi-Bayesian MLE calculating its parameters. Finally, we use tree VaR testing methods, Back test、Forward tes and Binomial test -----comparing its VaR loss probability We find the following results: 1.Under 1% left-tail critical probability, asset return distribution has significant Fat-tail character. 2.Using Mixture Normal distribution we can catch more Fat-tail character precisely than the other two methods. 3.The kurtosis of Mixture Normal is close to the actual kurtosis, this means that the Mixture Normal distribution can catch the Leptokurtosis phenomenon. Key words: Value at Risk、VaR、Fat tail、Historical simulation method、 Variance-Covariance method、Mixture Normal distribution、Quasi-Bayesian MLE、EM algorithm、Back test、 Forward test、 Leptokurtosis 風險值厚尾歷史模擬法變異數-共變異數法混合常態模型準貝式最大概似估計法 EM演算法回溯測試前向測試高峰 Value at risk (VaR) Fat tail Historical simulation method Variance-covariance method Mixture normal distribution Quasi-bayesian MLE EM algoritm Back test Forward test Leptokurtosis
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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